WO2023224044A1 - Equipment diagnosis system and equipment diagnosis method - Google Patents

Abstract

This equipment diagnosis system for identifying an abnormality cause candidate on the basis of a state quantity of equipment comprises: an input device for receiving a state quantity; a diagnosis device for identifying, on the basis of the state quantity received by the input device and a prescribed diagnosis rule, an abnormality cause candidate and a physical phenomenon and an engineering factor relating to the abnormality cause candidate; and an output device for outputting the abnormality cause candidate, the physical phenomenon, and the engineering factor, as diagnosis results.

Description

Device diagnosis system and device diagnosis method

The present invention relates to a device diagnosis system and a device diagnosis method.
This application claims priority based on Japanese Patent Application No. 2022-081303 filed in Japan on May 18, 2022, the contents of which are incorporated herein.

Conventional equipment diagnostic systems use past experience and knowledge to detect abnormal symptoms such as alarms determined by logger systems and pre-alarms detected using various methods from one or more measurement data input from sensors. In many cases, causes of abnormalities such as abnormal parts/external influence factors and points to be checked are listed on the screen as diagnostic results according to diagnostic rules established by the system. Diagnostic rules include a method of linking related causes of an abnormality based on the content of abnormal symptoms, and a method of narrowing down to the most likely cause of an abnormality based on measurement data. Patent Documents 1 to 3 listed below disclose a diesel engine support device, a monitoring diagnosis system, and an engine failure diagnosis device as examples of equipment diagnosis systems.

Japanese Patent No. 3696326 Japanese Patent No. 4163995 Japanese Patent Application Publication No. 2004-156516

In the above background technology, the user can check the abnormal parts/external influence factors that may be the cause of the abnormality from the screen, but if the user does not have sufficient knowledge of the equipment to be diagnosed, the background of the diagnosis result Unable to understand the reason for the diagnosis. As a result, it takes time for the user to take appropriate measures when dealing with the diagnosis result of the device diagnosis system, and there is a possibility that the user cannot take prompt action regarding the diagnosis result.

The present invention has been made in view of the above-mentioned circumstances, and provides a device diagnostic system that makes it easy to read the phenomenon that led to the diagnosis result, and allows for quicker response than before. The purpose of this invention is to provide a method for diagnosing devices.

A device diagnostic system according to a first aspect of the present invention is a device diagnostic system that identifies an abnormality cause candidate based on a state amount of a device, and includes an input device that receives the state amount, and the state amount accepted by the input device. and a diagnostic device that identifies the abnormality cause candidate, and physical phenomena and engineering factors related to the abnormality cause candidate, based on the abnormality cause candidate, the physical phenomenon, and the engineering factor based on the abnormality cause candidate and a predetermined diagnosis rule. and an output device that outputs the diagnosis result.

In the device diagnostic system according to a second aspect of the present invention, in the first aspect, when the diagnostic device identifies a plurality of abnormality cause candidates, the diagnostic device generates an evaluation value indicating the possibility of failure based on the state quantity. and the output device adds the evaluation value and the magnitude of the evaluation in the process of calculating the evaluation value to the abnormal symptom in the state quantity, the abnormal cause candidate, the physical phenomenon, and the engineering factor. The diagnostic results obtained are output.

In the device diagnostic system according to a third aspect of the present invention, in the first or second aspect, the input device receives abnormality confirmation information indicating the normal abnormality cause candidate, and the diagnostic device The abnormality cause candidates excluding the normal abnormality cause candidates, the physical phenomenon, and the engineering factors are identified based on the confirmation information, and the output device outputs a diagnosis result excluding the normal abnormality cause candidates. do.

In the device diagnostic system according to a fourth aspect of the present invention, in any one of the first to third aspects, the input device receives abnormality cause identification information indicating the true cause of the abnormality, and the diagnostic device includes: A diagnostic rule is updated based on the abnormality cause identification information.

In a device diagnostic system according to a fifth aspect of the present invention, in any one of the first to fourth aspects, the diagnostic device may detect the abnormality cause candidate, the physical phenomenon, and the engineering diagnosis for a plurality of abnormal symptoms. The cause is identified, and the output device outputs the diagnostic results regarding the plurality of abnormal symptoms.

In the equipment diagnosis system according to the sixth aspect of the present invention, in any one of the first to fifth aspects, the output device outputs the diagnosis result as a Sankey diagram.

A device diagnostic system according to a seventh aspect of the present invention is a device diagnostic system that identifies an abnormality cause candidate based on a state amount of a device, and includes an input device that receives the state amount, and the state amount accepted by the input device. and a diagnostic device that identifies the abnormality cause candidate and a physical phenomenon or engineering factor related to the abnormality cause candidate based on the abnormality cause candidate, the physical phenomenon, or the engineering factor based on the abnormality cause candidate and a predetermined diagnosis rule. and an output device that outputs the diagnosis result.

An apparatus diagnosis method according to an eighth aspect of the present invention is a device diagnosis method for identifying an abnormality cause candidate based on a state quantity of the apparatus, comprising: an acceptance step for accepting the state quantity; and the state quantity accepted in the acceptance process. and a diagnostic step of identifying the abnormality cause candidate, and physical phenomena and engineering factors related to the abnormality cause candidate, based on the abnormality cause candidate, the physical phenomenon, and the engineering factor based on the abnormality cause candidate and a predetermined diagnosis rule. and an output step of outputting the diagnosis result as a diagnosis result.

According to the present invention, it is possible to provide a device diagnosis system and a device diagnosis method that make it easy to read what kind of phenomenon led to the diagnosis result and can take action more quickly than before. It is.

1 is a block diagram showing the overall configuration of a device diagnostic system according to an embodiment of the present invention. FIG. 1 is a block diagram showing the main configuration of a device diagnostic system according to an embodiment of the present invention. 1 is a flowchart showing the overall operation of a device diagnostic system according to an embodiment of the present invention. 1 is a flowchart illustrating operations of main parts of a device diagnostic system according to an embodiment of the present invention. It is a correspondence table showing an example of the relationship between state quantities and abnormal symptoms in one embodiment of the present invention. It is a table showing an example of diagnostic rules in one embodiment of the present invention. FIG. 2 is a first schematic diagram showing the interrelationships among abnormal symptoms, physical phenomena, engineering factors, and abnormal cause candidates in an embodiment of the present invention. It is a schematic diagram which shows the output screen (Sankey diagram) of a diagnostic result in one Embodiment of this invention. 7 is a flowchart showing a process for excluding abnormality cause candidates in an embodiment of the present invention. FIG. 7 is a second schematic diagram showing the correspondence of device abnormalities in an embodiment of the present invention. It is a flowchart which shows update processing of a diagnostic rule in one embodiment of the present invention. FIG. 7 is a third schematic diagram showing the correspondence of device abnormalities in an embodiment of the present invention. FIG. 2 is a schematic diagram showing an output screen (Sankey diagram) of diagnostic results for a plurality of abnormal symptoms in an embodiment of the present invention.

Hereinafter, one embodiment of the present invention will be described with reference to the drawings.
The device diagnosis system according to this embodiment provides device diagnosis services to a plurality of users (clients) on a specific communication network. As shown in FIG. 1A, the device diagnosis system includes a device diagnosis server A, a communication line B, and n pieces of device equipment C1 to Cn (n: a natural number).

The device diagnosis server A and the n devices C1 to Cn are electrically connected via a communication line B as shown. First, the n pieces of equipment C1 to Cn will be explained. Each of the n pieces of equipment C1 to Cn includes equipments 1a, 2a, . . . , na and communication devices 1b, 2b, . . . nb. That is, the first equipment C1 includes a first equipment 1a and a first communication device 1b, the second equipment C2 includes a second equipment 2a and a second communication device 2b, and... , the n-th equipment Cn includes an n-th equipment na and an n-th communication device nb.

The n devices 1a, 2a, ..., na are diagnostic targets in the device diagnostic system. The devices 1a, 2a, . . . , na are devices that operate in each of the devices C1 to Cn, and are equipped with a plurality of sensors and measuring instruments that detect or measure the operating state. The devices 1a, 2a, ..., na are electrically connected to the communication devices 1b, 2b, ..., nb, and transmit the detection results or measurement results of the plurality of sensors and measuring instruments to the devices 1a, 2a, ..., na. It is output as the state quantity J to the communication devices 1b, 2b, . . . , nb.

That is, the first device 1a in the first device facility C1 outputs the detection results and/or measurement results of the plurality of sensors and measuring instruments to the first communication device 1b as the state quantity J of the first device 1a. . The second device 2a in the second device facility C2 outputs the detection results and/or measurement results of the plurality of sensors and measuring instruments to the second communication device 2b as the state quantity J of the second device 2a. Similarly, the n-th device na in the n-th device facility Cn outputs the detection results and/or measurement results of the plurality of sensors and measuring instruments to the n-th communication device nb as the state quantity J of the n-th device na. do. The devices 1a, 2a, . . . , na are, for example, engines operated in each of the devices C1 to Cn.

As shown in FIG. 1A, the n communication devices 1b, 2b, ..., nb are each electrically connected to the communication section a1 of the device diagnosis server A via the communication line B. The communication devices 1b, 2b, ..., nb transmit diagnostic requests regarding the respective devices 1a, 2a, ..., na to the communication section a1 via the communication line B, and also transmit the diagnosis results corresponding to these diagnostic requests to the communication section a1. A client computer that receives data from a client computer. Further, each communication device 1b, 2b, . . . , nb is electrically connected to each device 1a, 2a, .

Here, the diagnosis request sent from each communication device 1b, 2b, ..., nb (client computer) to the device diagnosis server A includes the state quantity J of each device 1a, 2a, ..., na and the device equipment C1 to Cn. Device information that specifies devices 1a, 2a, ..., na is included. In addition to the detection results and/or measurement results of sensors and measuring instruments in each device 1a, 2a, ..., na, the state quantity J is determined by the operator of the devices 1a, 2a, ..., na. Includes abnormal symptoms recognized by na (e.g. strange sounds, strange smells, etc.).

Each communication device 1b, 2b, ..., nb notifies the device diagnosis server A of the operating state of each device 1a, 2a, ..., na at the time of a diagnosis request, thereby informing the device 1a, 2a, ..., na of each device 1a, 2a, ..., na. Diagnosis, that is, whether or not an abnormality has occurred, identification of a candidate cause of the abnormality if an abnormality has occurred, and provision of the reason for the abnormality behind the identification of the candidate cause of the abnormality is requested. Although the details will be described later, the reason for the abnormality is a physical phenomenon and engineering factor related to identifying the candidate cause of the abnormality, and is derived based on the state quantity J of each device 1a, 2a, ..., na. .

The device diagnosis server A performs diagnosis on diagnosis requests sequentially received randomly from the n devices C1 to Cn via the communication line B, and provides the diagnosis results to the devices C1 to Cn as service information. . The device diagnosis server A is a type of computer equipped with a device diagnosis program and a communication function, and as shown in FIG. Equipped with a5.

The communication unit a1 is electrically connected to each equipment C1 to Cn via the communication line B, and mainly receives diagnosis requests and the like and transmits diagnosis results and the like. The communication unit a1 is a functional component that performs information communication with each device C1 to Cn according to a preset communication protocol, and is electrically connected to the calculation unit a2 inside the device diagnosis server A. The communication unit a1 performs information communication with each equipment C1 to Cn under the control of the calculation unit a2.

The calculation unit a2 is electrically connected to the communication unit a1, the storage unit a3, the operation unit a4, and the display unit a5, and is a central functional component in the device diagnosis server A. In response to the diagnosis request input from the communication section a1, the calculation section a2 executes the device diagnosis program stored in the storage section a3, thereby executing the device diagnosis according to the diagnosis request. Further, the calculation unit a2 transmits the diagnosis result corresponding to the diagnosis request to each device C1 to Cn via the communication unit a1.

The storage unit a3 stores in advance a device diagnosis program and also stores in advance device basic information necessary for executing the device diagnosis program. Furthermore, the storage section a3 is electrically connected to the calculation section a2, and temporarily stores the results of calculations performed by the calculation section a2 when executing the device diagnosis program. Reading and writing of information in the storage unit a3 is controlled by the calculation unit a2.

Here, the device diagnosis program is an application program that can be executed by the calculation unit a2. Although the details will be described later, the device diagnosis program uses the state quantity J and basic device information of each device 1a, 2a, ..., na (diagnosis target) included in the diagnosis request. The arithmetic unit a2 is caused to perform a diagnostic process for diagnosing na and a process for outputting the diagnostic results in each device 1a, 2a, . . . , na.

Further, the device basic information is information necessary for the calculation unit a2 to execute the device diagnosis program, and includes, for example, design information of each device 1a, 2a, ..., na (diagnosis target), each device 1a, 2a, ... , na during normal times (normal state quantities), and diagnostic rules preset for each abnormal symptom of each device 1a, 2a, . . . , na. Note that the details of the diagnostic rule will be described later.

The operation unit a4 is electrically connected to the calculation unit a2, and is mainly operated during maintenance of the device diagnosis server A. That is, the operation unit a4 is not directly involved in the normal processing of the device diagnosis server A, that is, the device diagnosis processing based on a diagnosis request. Note that during maintenance of the device diagnosis server A, operation section a4 is operated by the administrator of the device diagnosis server A to input operation information to the calculation section a2, and based on this operation information, information is stored in advance in the storage section a3. The device diagnostic program and basic device information will be updated.

The display section a5 is electrically connected to the calculation section a2, and is mainly operated during maintenance of the device diagnosis server A, like the operation section a4. That is, information necessary for maintenance of the device diagnosis server A is displayed as an image on the display section a5. The administrator of the device diagnosis server A performs appropriate maintenance of the device diagnosis server A by checking the image displayed on the display section a5.

The communication line B is a signal transmission line that conforms to a predetermined communication protocol, and the transmission medium is wired and/or wireless. Furthermore, the communication signals transmitted through the communication line B are electrical signals and/or optical signals. The communication line B is a WAN (Wide Area Network) or a LAN (Local Area Network).

Here, in the device diagnosis system according to the present embodiment, the state quantity of each device 1a, 2a, ..., na is received by the device diagnosis server A from any of the n device facilities C1 to Cn via the communication line B. Identify abnormality cause candidates based on J, identify physical phenomena and engineering factors related to identification of abnormality cause candidates in addition to abnormality cause candidates, and diagnose abnormality cause candidates, physical phenomena, and engineering factors. As a result, it is output to each equipment C1 to Cn via the communication line B. In the device diagnostic system, the device diagnostic server A corresponds to the input device, diagnostic device, and output device of the present invention.

Next, the operation of the device diagnosis system according to this embodiment and the device diagnosis method using the device diagnosis system will be described in detail with reference to FIGS. 2A to 8.

As shown in FIG. 2A, when the device diagnosis server A receives a diagnosis request from any of the n devices C1 to Cn (step S1), the device diagnosis server A performs the diagnosis of the devices 1a, 2a, ..., na based on this diagnosis request. One of the diagnostic processes is executed (step S2), and the result of this diagnostic process (diagnosis result) is outputted (transmitted) to one of the n devices C1 to Cn (step S3).

Here, the diagnosis request includes device information regarding the devices 1a, 2a, ..., na. That is, since the diagnosis request includes the state quantity J regarding the devices 1a, 2a, . Therefore, step S1 corresponds to the receiving step in the device diagnosis method according to the present embodiment.

Further, step S2 is a process of identifying abnormality cause candidates, physical phenomena, and engineering factors based on the state quantity J obtained in step S1 and a diagnosis rule described later, and the device diagnosis method according to the present embodiment. This corresponds to the diagnostic process in Further, step S3 is a process of outputting abnormality cause candidates, physical phenomena, and engineering factors obtained in the diagnosis process to the equipment C1 to Cn as diagnosis results, and is an output step in the equipment diagnosis method according to the present embodiment. corresponds to That is, the series of processes shown in FIG. 2A are basic processes performed by the device diagnosis server A, and are basic steps in the device diagnosis method according to this embodiment.

That is, in the device diagnosis server A, when the communication unit a1 receives a diagnosis request from any of the n communication devices 1b, 2b, . . . , nb, it outputs this diagnosis request to the calculation unit a2. Then, the calculation unit a2 recognizes the diagnosis execution target based on the device information included in the diagnosis request, and starts the diagnosis process (step S2).

For example, when the first communication device 1b in the first equipment C1 transmits a diagnosis request (first diagnosis request) for the first device 1a to be diagnosed to the communication unit a1 of the device diagnosis server A, The communication unit a1 receives the first diagnosis request and outputs it to the calculation unit a2. As a result, the calculation unit a2 starts a diagnostic process (step S2) that targets the first device 1a for diagnosis based on the device information included in the first diagnostic request.

Next, with reference to FIG. 2B, the diagnosis processing (step S2) and output processing (step S3) in the device diagnosis system and device diagnosis method according to the present embodiment will be described in detail. In addition, below, the case where the 1st apparatus 1a is diagnosed is demonstrated, and the case where the 1st apparatus 1a is a diesel engine is demonstrated to make it more concrete.

In the diagnostic process (step S2), the calculation unit a2 first obtains an abnormal symptom item and an abnormality degree X indicating the degree of the abnormality (step S21). That is, the calculation unit a2 acquires the abnormal symptom item regarding the first device 1a based on the state quantity J of the first device 1a received from the first communication device 1b, and also stores the degree of the abnormal symptom in the storage unit a3. The degree of abnormality X is obtained by evaluating using the normal state quantity of the first device 1a stored in advance.

For example, the state quantity J of the diesel engine (first device 1a) includes supply air pressure, supercharger rotation speed, supercharger inlet exhaust gas temperature, cylinder outlet exhaust temperature, cylinder maximum pressure, and color of combustion exhaust gas. These include taste, vibration noise of the cylinder head, mist concentration in the crank chamber, number of times the starting motor is used, engine output, and drop in lubricating oil pressure.

Among these state quantities J, for example, the deviation (deviation amount) of the relationship between the charge air pressure and the turbocharger rotation speed from the normal relationship is calculated based on the combination of the charge air pressure and the supercharger rotation speed. can be calculated. That is, in step S21, the calculation unit a2 calculates the supply pressure and supercharger rotation speed in the state quantity J and the supply pressure and supercharger rotation speed in the normal state quantity of the first equipment 1a included in the basic equipment information. A quantity indicating the degree of abnormality, that is, an abnormality degree X is obtained from the difference between the two, and when the abnormality degree X exceeds a predetermined threshold value, it is determined that it is an abnormal symptom. Furthermore, if the degree of abnormality X is smaller than a predetermined threshold value, it can be determined that no abnormal symptoms are occurring.

Here, the calculation unit a2 obtains the degree of abnormality indicating the degree of the abnormal symptom as "high" or "low" based on the positive/negative, ie, magnitude relationship between the state quantity J and the normal state quantity. For example, if the supply pressure when the supercharger rotational speed in the state quantity J is higher than the supply pressure in the normal state quantity, the calculation unit a2 calculates "supply pressure vs. supercharger rotational speed ( "high)" is acquired as the abnormality degree, and if the supply pressure at a certain state of the turbocharger rotation speed in the state quantity J is smaller than the supply pressure in the normal state quantity, "supply pressure vs. Obtain "machine rotation speed (low)" as the abnormality level.

Note that by using the state quantity J and the normal state quantity of the diesel engine, it is possible to extract abnormal symptoms as shown in FIG. 3 in addition to the relationship between the supply air pressure and the supercharger rotation speed. For example, based on the combination of turbocharger inlet exhaust gas temperature and engine output, "supercharger inlet exhaust gas temperature vs. engine output (high)" and "supercharger inlet exhaust gas temperature vs. engine output (low)" It is possible to obtain the degree of abnormality X and determine the abnormal symptoms.

Also, based on the cylinder outlet exhaust temperature, "Cylinder outlet exhaust temperature positive deviation (large)", "Cylinder outlet exhaust temperature negative deviation (large)", "Average cylinder outlet exhaust temperature (high)", and "Cylinder outlet exhaust It is possible to obtain an abnormality degree X called "temperature average (low)" and determine abnormal symptoms.

Here, "cylinder outlet exhaust gas temperature positive deviation (large)" means that when the exhaust gas temperature at the cylinder exit is higher than the average value, the difference from the average value (positive deviation) is large; ``Outlet exhaust gas temperature negative side deviation (large)'' means that when the exhaust gas temperature at the cylinder outlet is lower than the average value, the difference from the average value (negative side deviation) is large. Further, an abnormality degree X called "maximum cylinder pressure (high)" is obtained based on the maximum cylinder pressure, and an abnormal symptom can be determined.

Furthermore, even when the state quantity J indicates a symptom, usage status, etc., by comparing it with the normal state quantity, the degree of abnormality X can be obtained and abnormal symptoms can be determined. For example, it is possible to determine an abnormality by obtaining an abnormality level X of "exhaust color (blackish)" based on the color of the combustion exhaust gas, and to determine "cylinder head abnormal noise (large)" based on the vibration sound of the cylinder head. )", which is the degree of abnormality X, can be obtained to determine abnormal symptoms.

Furthermore, it is possible to determine abnormal symptoms by obtaining the degree of abnormality X called "mist amount (large)" based on the amount of mist in the crank chamber, and to determine the "number of times the starting motor is used" based on the number of times the starting motor is used. It is possible to determine an abnormal symptom by obtaining an abnormality degree X of "Exceeding". Furthermore, an abnormality level X of "lubricating oil pressure (low)" is obtained from alarm information detected on the equipment side indicating a decrease in lubricating oil pressure, and an abnormal symptom can be determined.

When the calculation unit a2 acquires the abnormal symptom with the degree of abnormality regarding the first device 1a based on the state quantity J and the normal state quantity regarding the first device 1a in this way, the calculation unit a2 stores a diagnostic rule corresponding to the abnormal symptom. It is acquired from section a3 (step S22). This diagnostic rule establishes a relationship between an abnormal symptom and a candidate cause of the abnormality through physical phenomena and engineering factors.

Let's explain the diagnostic rules in more detail. As shown in FIG. 4 as an example, the diagnostic rule includes an abnormal symptom with an abnormal degree, a lower judgment limit set for each abnormal symptom with an abnormal degree, and one or more physical phenomena related to the abnormal symptom. , a weight for abnormal symptoms of a physical phenomenon (weighting coefficient g), one or more engineering factors related to the physical phenomenon, and a degree of relationship of the engineering factor to the physical phenomenon (first ratio k1), It includes an abnormality cause candidate and the degree of relationship of the abnormality cause candidate to the engineering factor (second ratio k2).

In other words, this diagnostic rule uses physical phenomena and engineering factors to describe the process of identifying abnormal cause candidates from abnormal symptoms, and uses physical phenomena to provide specific background information when identifying abnormal cause candidates from abnormal symptoms. Shown as phenomena and engineering factors. Note that FIG. 4 shows, as an example, diagnostic rules regarding abnormal symptoms: "Cylinder outlet exhaust gas temperature negative side deviation (large)" and "Cylinder outlet exhaust gas temperature average (high)."

Looking at "Cylinder outlet exhaust temperature negative side deviation (large)" in Figure 4, the physical phenomena related to this abnormal symptom are "amount of heat input to a specific cylinder (small)", "measurement interference (low temperature)", "cooling (Large)" is set. Furthermore, the weighting coefficients g for abnormal symptoms of such physical phenomena are set to "80", "10", and "5", respectively.

Of the physical phenomena mentioned above, ``insufficient fuel in a specific cylinder'' and ``poor combustion/unburnt'' are set as engineering factors related to ``amount of heat input into a specific cylinder (small)'', and ``measurement interference (low temperature )" is set as an engineering factor related to "Unburnt material accumulation", and "Cylinder water intrusion" is set as an engineering factor related to "Cooling (large)".

Then, the first ratio k1 to "insufficient fuel in a specific cylinder" which is an engineering factor and "amount of heat input to a specific cylinder (small)" which is a physical phenomenon of "poor combustion/unburnt" is "insufficient fuel in a specific cylinder". "55%" is set for "poor combustion/unburned" and "45%" for "poor combustion/unburned". In addition, the first ratio k1 for "measurement interference (low temperature)", which is a physical phenomenon of "unburnt material accumulation" which is an engineering factor, is set to "100%", and the engineering factor "inside cylinder A first ratio k1 for "cooling (large)" which is a physical phenomenon of "water intrusion" is set to "100%".

Then, ``fuel injection pump,'' ``fuel injection valve,'' and ``fuel high pressure pipe'' were set as abnormality cause candidates related to the engineering factor of ``insufficient fuel in a specific cylinder,'' and "Fuel injection pump" and "fuel injection valve" are set as abnormality cause candidates related to the factors. In addition, ``temperature sensor'' has been set as a candidate for an abnormality cause related to the engineering factor of ``unburnt material accumulation,'' and furthermore, ``cylinder head'' has been set as a candidate for an abnormality cause related to an engineering factor of ``water intrusion into the cylinder.'' Set.

Regarding the engineering factor "insufficient fuel in a specific cylinder," the second ratio k2 for the abnormality cause candidate "fuel injection pump" is set to "20%," and the second ratio k2 for the abnormality cause candidate "fuel injection valve" is set to "20%." k2 is set to "60%", and the second ratio k2 regarding the "fuel high pressure pipe" which is a candidate for the abnormality cause is set to "20%". In addition, regarding the engineering factor "poor combustion/uncombustion," the second ratio k2 for the abnormality cause candidate "fuel injection pump" is "30%," and the second ratio k2 for the abnormality cause candidate "fuel injection valve" is ``30%.'' The ratio k2 of each is set to "70%".

In addition, for the engineering factor "unburnt material accumulation", the second ratio k2 for the abnormality cause candidate "temperature sensor" is set to "100%", and for the engineering factor "water intrusion into the cylinder", the second ratio k2 is set to "100%". , the second ratio k2 regarding the abnormality cause candidate "cylinder head" is set to "100%".

On the other hand, when looking at the "average cylinder outlet exhaust temperature (high)", the physical phenomena related to this abnormal symptom are "insufficient amount of air in the cylinder", "air temperature in the cylinder (high)", and "heat input to all cylinders ( Large)" is set. Further, the weighting coefficients g for abnormal symptoms of such physical phenomena are set to "70", "30", and "30", respectively.

The engineering factors related to the physical phenomenon of ``insufficient air amount in the cylinder'' are ``supercharger efficiency (low),'' ``pressure loss in the exhaust path after the turbocharger (large),'' and ``pressure loss at the inlet of the air supply path ( "Intake air cooling capacity (Low)" and "Intake air temperature (High)" are set as engineering factors related to the physical phenomenon "Cylinder air temperature (High)". )' is set, and 'excessive fuel in all cylinders' and 'excessive fuel (burden by abnormal cylinder)' are further set as engineering factors related to the physical phenomenon 'heat input to all cylinders (large)'.

Then, the physical effects of the engineering factors ``supercharger efficiency (low)'', ``pressure drop in the exhaust path after the turbocharger (large)'', ``pressure drop at the inlet of the air supply path (large)'', and ``air supply path leakage'' are considered. The first ratios k1 for the phenomenon "insufficient air amount in the cylinder" are set to "30%", "10%", "30%", and "30%", respectively.

In addition, the first ratio k1 of the engineering factor "supply air cooling capacity (low)" to the physical phenomenon "in-cylinder air temperature (high)" is set to "70%", and the engineering factor The ratio (degree of relationship) of a certain "intake air temperature (high)" to "cylinder air temperature (high)" which is a physical phenomenon is set to "30%". Furthermore, the first ratio k1 to "all cylinder input heat amount (large)" which is a physical phenomenon of "excess fuel in all cylinders" which is an engineering factor is set to "60%", and the engineering factor "fuel excess" is set to "60%". The ratio (degree of relationship) of the physical phenomenon of "excessive heat (load of abnormal cylinder)" to "all cylinders input heat (large)" is set to "40%".

Then, "supercharger turbine side," "supercharger blower side," and "intake filter" are set as abnormality cause candidates related to the engineering factor "supercharger efficiency (low)." ``Exhaust gas system after turbocharger'' is set as a candidate cause of abnormality related to the engineering factor ``pressure drop in exhaust path after turbocharger (large)''. In addition, "air supply valve/valve seat" and "air supply route after supercharger" were set as abnormality cause candidates related to the engineering factor "air supply route inlet pressure drop (large)", and the engineering factor "air supply route inlet pressure drop (large)" "Air supply route after supercharger" is set as an abnormality cause candidate related to "air supply route leakage".

In addition, "A/C, A/C cooling water system" was set as a candidate cause of abnormality related to the engineering factor "supply air cooling capacity (low)", and the engineering factor "intake air temperature (high)" "High engine room temperature" is set as a candidate for the cause of the abnormality related to this. Furthermore, "overload/high Pme" is set as a candidate for abnormality cause related to "excessive fuel in all cylinders" which is an engineering factor corresponding to "heat input to all cylinders (large)" which is a physical phenomenon. "Fuel injection valve," "fuel injection pump," and "fuel high pressure pipe" are set as abnormality cause candidates related to "excess fuel (abnormal cylinder load)."

Furthermore, regarding the engineering factor "supercharger efficiency (low)", the second ratio k2 regarding the abnormality cause candidate "supercharger turbine side" is "40%", and the abnormality cause candidate "supercharger blower side" is 40%. The second ratio k2 regarding the "side" is set to "30%", and the second ratio k2 regarding the "intake filter" which is the abnormality cause candidate is set to "30%".

For the engineering factor "post-supercharger exhaust path pressure drop (large)", the second ratio k2 regarding the abnormality cause candidate "exhaust gas system post-supercharger" is set to "100%". In addition, regarding the engineering factor "air supply route inlet pressure drop (large)", the second ratio k2 related to the abnormality cause candidate "air supply valve/valve seat" is "70%", and the abnormality cause candidate "supercharging The second ratio k2 regarding the "after-flight air supply route" is respectively set to "30%". Further, regarding the engineering factor "air supply route leakage", the second ratio k2 regarding the "post-supercharger air supply route" which is the abnormality cause candidate is set to "100%".

In addition, regarding the engineering factor ``supply air cooling capacity (low)'' which corresponds to the physical phenomenon ``in-cylinder air temperature (high)'', regarding the ``A/C, A/C cooling water system'', which is a candidate cause of the abnormality, The second percentage k2 is set to "100%".

Furthermore, for the engineering factor "intake air temperature (high)", the second ratio k2 regarding the abnormality cause candidate "engine room temperature high" is set to "100%".

Furthermore, regarding the engineering factor "excessive fuel in all cylinders" which corresponds to the physical phenomenon "heat input to all cylinders (large)", the second ratio k2 regarding "overload/high Pme" which is a candidate cause of abnormality is "100 %”. In addition, the second ratio k2 regarding the engineering factor "excess fuel (abnormal cylinder load)" regarding the abnormality cause candidate "fuel injection valve" is "60%", and the abnormality cause candidate "fuel injection pump" The second ratio k2 is set to "20%", and the second ratio k2 regarding the "high pressure fuel pipe" which is a candidate cause of the abnormality is set to "20%".

Upon acquiring such a diagnostic rule from the storage unit a3, the calculation unit a2 determines whether the degree of abnormality of the abnormal symptom is below a preset lower limit for determination (step S23). That is, the calculation unit a2 determines whether or not the degree of abnormality of the abnormal symptom acquired in step S21 is equal to or higher than the lower limit for determination set in the diagnostic rule, and determines whether or not the degree of abnormality of the abnormal symptom is equal to or higher than the lower limit for determination. An individual abnormality cause state index α is calculated (step S24). Note that the calculation unit a2 does not calculate the individual abnormality cause state index α for abnormal symptoms whose degree of abnormality is smaller than the lower limit for determination.

The individual abnormality cause state index α is a value given as a function of the degree of abnormality It is calculated based on the relationship among all abnormal symptoms, physical phenomena, engineering factors, and abnormal cause candidates.
α=(X・g・k1)/100・k2/100 (1)

After calculating the individual abnormality cause state index α for all the abnormal symptoms, physical phenomena, engineering factors, and abnormality cause candidates, the calculation unit a2 calculates the comprehensive abnormality cause state index β (step S25). The comprehensive anomaly cause state index β is the sum of the individual anomaly cause state index α related to each anomaly cause candidate, as shown in equation (2) below, and is the sum of the individual anomaly cause state index α related to each anomaly cause candidate. This is an evaluation value indicating the degree of quality. Note that a specific example of calculating the individual abnormality cause state index α and the comprehensive abnormality cause state index β will be described later in the explanation of FIG.
β=Σ{(X・g・k1)/100・k2/100} (2)

FIG. 5 shows an example of the correlation between abnormal symptoms, physical phenomena, engineering factors, and abnormal cause candidates obtained through the processing of steps S21 to S24 described above. )" is a first schematic diagram showing the mutual relationship regarding " In Figure 5, the numbers shown in square brackets "[]" are numbers that indicate the calculation process of the individual abnormality cause state index α, and the numbers shown in normal parentheses "()" are the weighting coefficient g, and the numbers without parentheses. are the first ratio k1 and the second ratio k2, and the numerical value shown in curly brackets "{}" is the comprehensive abnormality cause state index β.

In the example of FIG. 5, the abnormality degree X of the abnormal symptom "negative side deviation (large) in cylinder outlet exhaust gas temperature" is 7, which is equal to or higher than the lower limit for determination, and therefore is determined to be an abnormal symptom. The weighting coefficient of "specific cylinder input heat amount (small)", which is one of the physical phenomena that has a connection with this, is 80, and the numerical value indicating the calculation process is [560].

The first ratio k1 of "insufficient fuel in a specific cylinder", which is one of the engineering factors related to this, is 55%, and the numerical value indicating the calculation process is [308]. The second ratio k2 of "fuel injection pump", which is one of the causes of abnormality that has a connection with this, is 20%, and the individual abnormality cause state index α is 62.

By similar calculation, the individual abnormality cause state index α in accordance with the connection with "poor combustion/unburnt" is 75, which is 30% of [252]. The comprehensive abnormality cause state index β of the “fuel injection pump” is the sum of these two α, which is {137}. Calculations along other connection relationships shown in FIG. 5 are also similar, and their explanation will be omitted.

FIG. 5 shows the interrelationship between multiple abnormality cause candidates in terms of failure probability using the comprehensive abnormality cause state index β and the magnitude of the evaluation in the calculation process. The calculation unit a2 displays an output screen that displays diagnostic results based on abnormal symptoms, physical phenomena, engineering factors, abnormal cause candidates, comprehensive abnormal cause state index β, and the magnitude of evaluation in the calculation process as shown in FIG. is generated and the output screen is transmitted to the communication unit a1 (step S26).

The output screen (diagnosis result) is, for example, a Sankey diagram as shown in FIG. In FIG. 6, for the abnormal symptom "negative side deviation of cylinder outlet exhaust gas temperature (large)", a plurality of abnormality cause candidates are displayed as thick bands in descending order of failure probability, that is, in descending order of comprehensive abnormality cause state index β. The output screen (diagnosis result) as a Sankey diagram visually indicates the priority to be dealt with among a plurality of abnormality cause candidates estimated as the cause of the abnormal symptom.

The output screen (diagnosis result) is sent to the first equipment C1 that has sent the diagnosis request to the equipment diagnosis server A. In the first equipment C1, the communication device 1b, which is a client computer, receives the output screen (diagnosis result) and displays the output screen (diagnosis result) on the display device.

According to the device diagnosis server A according to the present embodiment, instead of simply presenting an abnormality cause candidate to the first equipment C1, physical phenomena and engineering factors related to the abnormality cause candidate are presented together. Therefore, in the first equipment C1, it is possible to read the phenomenon that led to the diagnosis result, and it is possible to take action more quickly and accurately than in the past.

Furthermore, as shown in FIGS. 5 and 6, multiple abnormality cause candidates can generally be estimated. According to this embodiment, the comprehensive abnormality cause state index β, which is an evaluation value indicating the possibility of failure, is presented for a plurality of abnormality cause candidates. It can be easily grasped, and therefore, even with the comprehensive abnormality cause state index β, the first equipment C1 can take quicker and more accurate measures than before.

In the first equipment C1, for example, the factual relationship of the abnormality is confirmed in order from the abnormality cause candidate with the highest comprehensive abnormality cause state index β (in FIG. 6, the abnormality cause candidate with the highest comprehensive abnormality cause state index β If it is confirmed that the fuel injection valve (fuel injection valve) is normal, it is necessary to understand the cause of the abnormality other than the abnormality cause candidate with the highest comprehensive abnormality cause state index β.

Here, the comprehensive abnormality cause state index β is calculated based on the above-mentioned formulas (1) and (2), that is, it is an estimated value calculated based on engineering rationality. Therefore, at the time when the abnormality cause candidate with the highest comprehensive abnormality cause state index β (estimated value) is confirmed to be normal, this comprehensive abnormality cause state index β and the comprehensive abnormality cause state index β regarding other abnormality cause candidates is the estimate to be corrected.

In the first equipment C1, when the abnormality cause candidate with the highest comprehensive abnormality cause state index β is confirmed to be normal, the first communication device 1b sends the confirmation result, that is, the comprehensive abnormality cause state, to the equipment diagnosis server A. Abnormality confirmation information indicating that the abnormality cause candidate with the highest index β is normal is transmitted. Then, the abnormality confirmation information is received by the communication unit a1 of the device diagnosis server A and output to the calculation unit a2. In addition, to confirm that the abnormality cause candidate with the highest comprehensive abnormality cause state index β is normal, typically, the operator of the first equipment C1 confirms that the abnormality cause candidate is actually normal. This is done by inputting confirmation information into the communication device 1b, but the method is not limited to this method.

Upon acquiring the abnormality confirmation information, the calculation unit a2 executes the process of excluding abnormality cause candidates shown in FIG. 7. The exclusion process is to exclude abnormality cause candidates whose normality has been confirmed as exclusion targets from the abnormality cause candidates, and is a process of updating the comprehensive abnormality cause state index β based on the abnormality confirmation information.

In the abnormality cause candidate exclusion process, the calculation unit a2 corrects the second ratio k2 for abnormality cause candidates whose normality has been confirmed to "0" (step Sa1). For example, as shown in FIG. 8, the second ratio k2 set for "fuel injection valve", which is the abnormality cause candidate with the highest comprehensive abnormality cause state index β in FIG. 5, is all corrected to "0".

That is, the calculation unit a2 corrects the second ratio k2 regarding the abnormality cause candidate "fuel injection valve" from "60%" to "0%" for the engineering factor "specific cylinder fuel shortage", and Regarding the cause "poor combustion/uncombustion", the second ratio k2 regarding the abnormality cause candidate "fuel injection valve" is revised from "70%" to "0%".

Then, the calculation unit a2 determines whether there is an abnormality cause candidate (sibling candidate) that is a sibling of the abnormality cause candidate "fuel injection valve" (step Sa2). The sibling candidate is an abnormality cause candidate other than the exclusion target related to the engineering factor related to the abnormality cause candidate whose second ratio k2 has been corrected to "0%".

For example, in FIG. 5, the engineering factors related to the "fuel injection valve" that is a candidate cause of the abnormality are "insufficient fuel in a specific cylinder" and "poor combustion/unburned." In step Sa1, the engineering factors "insufficient fuel in specific cylinder" and "poor combustion/unburnt" and the second ratio k2 regarding the abnormality cause candidate "fuel injection valve" were all corrected to "0", so the sibling candidate The second ratio k2 regarding "insufficient fuel in a specific cylinder" and "poor combustion/unburnt" is naturally adjusted under the influence of the correction of the second ratio k2 regarding the "fuel injection valve" which is a candidate cause of the abnormality. Should.

If the calculation unit a2 determines in step Sa2 that there is a sibling candidate, it adjusts the second ratio k2 regarding the sibling candidates (step Sa3). The calculation unit a2 calculates the adjustment of the second ratio k2 regarding the sibling candidates based on the following formula (3). Note that in equation (3), k2' is the second ratio after adjustment (second adjustment ratio), ka is the value of the second ratio k2 regarding the excluded target, and kb is the value of the second ratio k2 regarding the sibling candidate. This is the value of the ratio k2 of 2.
k2'=ka・kb/(100-ka)+kb (3)

As shown in FIG. 8, the calculation unit a2 changes the second ratio k2 between the engineering factor "insufficient fuel in specific cylinder" and the abnormality cause candidate "fuel injection pump" in FIG. 5 from "20%" to "50%". %'', and the second ratio k2 regarding the engineering factor ``insufficient fuel in a specific cylinder'' and the abnormality cause candidate ``fuel high pressure pipe'' is adjusted from ``20%'' to ``50%.''

On the other hand, when the calculation unit a2 determines that there is no sibling candidate in step Sa2, it changes the first ratio k1 regarding the abnormality cause candidate (single candidate) for which there is no sibling candidate to "0" (step Sa4). That is, the calculation unit a2 excludes the engineering factor (parent factor) related to the individual candidate from the engineering factor candidates by changing the setting of the first ratio k1 regarding the individual candidate to "0".

For example, in Figure 5, the abnormality cause candidate "cylinder head" is related only to the engineering factor "cooling water intrusion into the cylinder", and "cooling water intrusion into the cylinder" is related only to the abnormality cause candidate "cylinder head". Since they are related, it is a single candidate with no sibling candidates. As shown in FIG. 8, the calculation unit a2 changes the first ratio k1 regarding the abnormality cause candidate "cylinder head" from "100%" to "0%", thereby reducing the engineering factor "cylinder cooling". Exclude "water intrusion" from the candidates.

Then, the calculation unit a2 determines whether there is an engineering factor (sibling factor) that is a sibling to the engineering factor excluded in step Sa4 (step Sa5). Then, when a sibling factor exists, the calculation unit a2 adjusts the first ratio k1 regarding the sibling factor (step Sa6).

The calculation unit a2 calculates the adjustment of the first ratio k1 regarding the sibling factor based on the following equation (4). In equation (4), k1' is the first ratio after adjustment (first adjustment ratio), and kc is the first ratio related to the parent factor (engineering factor) related to the abnormality cause candidate to be excluded. is the value of the ratio k1, and kd is the value of the first ratio k1 regarding the sibling factor.
k1'=kc・kd/(100-kc)+kd (4)

Furthermore, if there is no sibling engineering factor (sibling factor) to the engineering factor (exclusion factor) excluded in step Sa4, the calculation unit a2 calculates the weight of the physical phenomenon (parent phenomenon) that is the parent of the exclusion factor. The coefficient g is set to "0" (step Sa7). For example, in the case of FIG. 5, the weighting coefficient g regarding the physical phenomenon "cooling (large)" is changed from "5" to "0" as shown in FIG.

When the adjustment (change) of the weighting coefficient g, the first ratio k1, and the second ratio k2 based on the abnormality confirmation information is completed in this way, the calculation unit a2 calculates the adjusted weighting coefficient g, the first ratio k1 Then, the comprehensive abnormality cause state index β is recalculated based on the second ratio k2 (step Sa8). That is, as shown in FIG. 8, the comprehensive abnormality cause state index β for the abnormality cause candidate "fuel injection pump" is recalculated from "137" to "406", and the "fuel injection valve" to be excluded (abnormality cause candidate) ” is recalculated from “361” to “0”.

Furthermore, the comprehensive abnormality cause state index β regarding the abnormality cause candidate "fuel high pressure pipe" is recalculated from "62" to "154". Furthermore, the comprehensive abnormality cause state index β regarding the abnormality cause candidate "cylinder head" is recalculated from "35" to "0".

The calculation unit a2 recreates the output screen (Sankey diagram) based on FIG. 8 created in this way, and transmits the output screen (Sankey diagram) to the communication unit a1 (step Sa9). The communication unit a1 transmits the output screen (Sankey diagram) to the first equipment C1 as an update screen for the abnormality confirmation information previously received from the first communication device 1b. That is, the output screen recreated based on the abnormality confirmation information is a diagnosis result in which normal abnormality cause candidates are excluded based on the abnormality confirmation information.

Then, in the first equipment C1, the state of the abnormality cause candidate is confirmed based on the comprehensive abnormality cause state index β of the abnormality cause candidate shown on the update screen. In the first equipment C1, for example, the health of the abnormality cause candidate with the largest comprehensive abnormality cause state index β is confirmed on the update screen. As a result of this confirmation, if the abnormality cause candidate with the largest comprehensive abnormality cause state index β on the update screen is normal, abnormality confirmation information indicating this fact will be sent to the device diagnosis server A again.

Here, the update screen (Sankey diagram), like the first output screen (Sankey diagram), does not simply present the abnormality cause candidates and the comprehensive abnormality cause state index β, but rather displays the physical information related to the abnormality cause candidates. Presented together with phenomena and engineering factors. Therefore, in the first equipment C1, it is possible to read what kind of phenomenon led to the diagnosis result, and it is possible to take action more quickly than before.

Through several communications between the first equipment C1 and the equipment diagnosis server A, the true cause of the abnormality is identified in the first equipment C1. Then, the first communication device 1b in the first equipment C1 transmits information indicating the true cause of the abnormality (abnormality cause identification information) to the equipment diagnosis server A.

In the device diagnosis server A, when the communication unit a1 receives the abnormality cause identification information, the abnormality cause identification information is output to the calculation unit a2. When the abnormality cause specifying information is input from the communication unit a1, the calculation unit a2 executes the diagnostic rule update process shown in FIG. 9. The diagnostic rule update process is a process (correction process) that corrects the weighting coefficient g, the first ratio k1 and/or the second ratio k2 in the diagnostic rule shown in FIG. 4 based on the true cause of the abnormality.

In the diagnostic rule update process, the calculation unit a2 first determines whether a predetermined shortest correction interval T1 or more has elapsed between the current correction process and the previous correction process (step Sb1). If the calculation unit a2 determines that the current correction process has elapsed for the shortest correction interval T1 or more, it then determines whether the total correction amount within the predetermined correction effective period T2 is within the predetermined maximum correction amount H. (Step Sb2).

Then, in step Sb1, if the current correction process has not passed the shortest correction interval T1 or more, and in step Sb2, if the total correction amount within the correction effective period T2 is not within the maximum correction amount H, the calculation unit a2 calculates , the weighting coefficient g, the first ratio k1 and/or the second ratio k2 are not corrected, and the correction process is canceled.

That is, the calculation unit a2 performs the calculation only when the current correction process has elapsed for the shortest correction interval T1 or more in step Sb1, and only when the total correction amount within the correction effective period T2 is within the maximum correction amount H in step Sb2. , the weighting coefficient g, the first ratio k1 and/or the second ratio k2. Note that the shortest correction interval T1, correction effective period T2, and maximum correction amount H are parameters for evaluating whether or not the correction process can be executed.

Subsequently, the calculation unit a2 identifies engineering factors, physical phenomena, and abnormal symptoms related to the true cause of the abnormality based on the current diagnostic rules (step Sb3). For example, as shown in Figure 10, if the true cause of the abnormality is the "fuel injection pump," the engineering factors related to the "fuel injection pump" are "insufficient fuel in a specific cylinder" and "improper combustion/unburned." It is. Further, the physical phenomenon related to these "insufficient fuel in a specific cylinder" and "poor combustion/unburnt" is "amount of heat input to a specific cylinder (small)." Furthermore, the abnormal symptom related to this "specific cylinder input heat amount (small)" is "negative side deviation of cylinder outlet exhaust gas temperature (large)."

After identifying the engineering factors, physical phenomena, and abnormal symptoms related to the true cause of the abnormality in step Sb3, the calculation unit a2 calculates only a preset standard correction amount h for the engineering factors, physical phenomena, and abnormal symptoms. The weighting coefficient g, the first ratio k1 and/or the second ratio k2 are increased.

For example, the second ratio k2 regarding "poor combustion/uncombustion", which is an engineering factor, and "fuel injection pump", which is the true cause of the abnormality, is increased from "30%" to "38.5%". In addition, the first ratio k1 between the engineering factor "poor combustion/unburnt" and the physical phenomenon "heat amount input into a specific cylinder (small)" is increased from "45%" to "48.2%". Furthermore, the weighting coefficient g relating to the physical phenomenon "amount of heat input to a specific cylinder (small)" and the abnormal symptom "negative side deviation of cylinder outlet exhaust gas temperature (large)" is increased from "80" to "85".

Furthermore, the calculation unit a2 decreases the first ratio k1 and/or the second ratio k2 related to the increase in the weighting coefficient g and the first ratio k1 or/and the second ratio k2. In the case of FIG. 10, the first ratio k1 and/or second ratio k2 related to this increase is the second ratio regarding "poor combustion/unburnt" and "fuel injection valve" which is a candidate cause of abnormality. k2, and a first ratio k1 regarding the physical phenomenon "heat amount input to a specific cylinder (small)" and the engineering factor "insufficient fuel in a specific cylinder".

As the second ratio k2 regarding "poor combustion/unburned" and "fuel injection pump" which is the true cause of the abnormality increases, the calculation unit a2 calculates "poor combustion/unburned" and "unburned" which is the candidate cause of the abnormality. The second ratio k2 related to "fuel injection valve" is decreased from "70%" to "61.5%". In addition, as the first ratio k1 between the engineering factor "poor combustion/unburnt" and the physical phenomenon "heat input to a specific cylinder (small)" increases, the engineering factor "insufficient fuel in a specific cylinder" increases. and the physical phenomenon "amount of heat input into a specific cylinder (small)", the first ratio k1 is decreased from "55%" to "51.8%".

When such diagnostic rule update processing is completed, the calculation unit a2 ends the device diagnostic service for the first equipment C1. The calculation unit a2 then enters a standby state until a new diagnosis request is input from the communication unit a1. That is, the device diagnosis server A returns from the standby state to the active state every time a diagnosis request is input, and responds to each device 1a, 2a, . . . in response to a diagnosis request randomly received from n devices C1 to Cn. , na (diagnosis target) is performed.

According to such a diagnostic rule update process, the diagnostic rule is optimally updated based on the true abnormality cause every time the true abnormality cause is identified in the first equipment C1. The ability to estimate abnormality cause candidates gradually improves over time. Therefore, according to the present embodiment, it is possible to improve the equipment diagnostic ability for each equipment C1 to Cn as the operating time passes.

Note that the present invention is not limited to the above-described embodiment, and for example, the following modifications can be considered.
(1) In the above embodiment, the device diagnosis system according to the present invention is configured as the device diagnosis server A, but the present invention is not limited to this. For example, the device diagnosis server A may be a computer that does not have the function of a server, and the communication devices 1b, 2b, . . . , nb may be devices that do not have the function of a client.

(2) The communication devices 1b, 2b, ..., nb in each equipment C1 to Cn are not limited to fixedly installed devices, but may also be portable communication devices such as notebook PCs or tablet terminals with communication functions. good. Such a portable communication device is communicably connected to each device 1a, 2a, .

(3) In the above embodiment, the device basic information is stored in the storage unit a3 of the device diagnosis server A in advance. This is to reduce the communication load between the equipment C1 to Cn and the equipment diagnosis server A. However, if such communication load need not be ignored or taken into consideration, all or part of the device basic information may be transmitted to the device diagnosis server A from the device facilities C1 to Cn.

(4) In the above embodiment, the output screen (diagnosis result) is output as a Sankey diagram to each equipment C1 to Cn, but the present invention is not limited to this. The output screen (diagnosis result) may be displayed in a display format other than the Sankey diagram as long as the relationship between the abnormality cause candidate, physical phenomenon, and engineering factor can be understood.

(5) In the above embodiment, the output screen (diagnosis result) for one abnormal symptom was shown as the Sankey diagram in FIG. 6, but the present invention is not limited to this. For events in which multiple abnormal symptoms are related, such as multiple abnormal symptoms appearing due to one abnormal cause, the comprehensive abnormal cause state index β for each abnormal cause candidate is calculated, and the diagnostic results regarding multiple abnormal symptoms are combined into one Sankey diagram. By outputting as , it is possible to diagnose and present diagnostic results including the relationship between physical phenomena, engineering factors, and causes of abnormality for multiple abnormal symptoms.

For example, if you output a diagnosis result including "maximum cylinder pressure deviation (large) on the negative side" in addition to the abnormal symptom "negative side deviation (large) in cylinder outlet exhaust temperature" of the diagnostic rule shown in Figure 4, the result will be output as a Sankey diagram. The result will be as shown in FIG.

(6) In the above embodiment, a hierarchy is provided for both physical phenomena and engineering factors, and the relationship between the four hierarchies of abnormal symptoms, physical phenomena, engineering factors, and abnormal cause candidates is established. An evaluation value indicating the possibility of failure was calculated. However, depending on the equipment to be diagnosed, it may be difficult to clearly distinguish between physical phenomena and engineering factors, or there may be cases where it is not necessary to distinguish between the two for evaluation.

In such a case, there should be no separate hierarchy for physical phenomena and engineering factors, but a single hierarchy that includes at least one of physical phenomena or engineering factors, and should include abnormal symptoms, physical phenomena or engineering factors, and causes of abnormalities. It is also possible to calculate an evaluation value indicating the possibility of failure based on the connection relationship between the three hierarchies of candidates. In this case, the output device outputs a diagnosis result in which an evaluation value indicating the possibility of failure and information indicating the magnitude of the evaluation in the calculation process are added to the abnormal symptoms, physical phenomena or engineering factors, and abnormal cause candidates. Output.

According to the present invention, it is possible to provide a device diagnosis system and a device diagnosis method that make it easy to read what kind of phenomenon led to the diagnosis result and can take action more quickly than before. It is.

A Device diagnosis server (input device, diagnostic device, output device)
a1 Communication section a2 Arithmetic section a3 Storage section a4 Operation section a5 Display section B Communication line C1 to Cn Equipment and equipment 1a, 2a,..., na Communication device 1b, 2b,..., nb Equipment (diagnosis target)

Claims (8)

1. In a device diagnostic system that identifies abnormality cause candidates based on device state quantities,
   an input device that accepts the state quantity;
   a diagnostic device that identifies the abnormality cause candidate and physical phenomena and engineering factors related to the abnormality cause candidate based on the state quantity accepted by the input device and a predetermined diagnostic rule;
   An equipment diagnosis system comprising: an output device that outputs the abnormality cause candidate, the physical phenomenon, and the engineering factor as a diagnosis result.
2. The diagnostic device calculates an evaluation value indicating a failure possibility regarding the plurality of abnormality cause candidates based on the state quantity,
   The output device outputs the diagnostic result obtained by adding the evaluation value and the magnitude of the evaluation in the process of calculating the evaluation value to the abnormal symptom in the state quantity, the abnormality cause candidate, the physical phenomenon, and the engineering factor. The device diagnostic system according to claim 1, which outputs the following.
3. The input device accepts abnormality confirmation information indicating the normal abnormality cause candidate,
   The diagnostic device identifies the abnormality cause candidates excluding the normal abnormality cause candidates, the physical phenomenon, and the engineering factor based on the abnormality confirmation information,
   The device diagnostic system according to claim 1 or 2, wherein the output device outputs a diagnosis result excluding the normal abnormality cause candidates.
4. The input device accepts abnormality cause identification information indicating the true cause of the abnormality,
   The device diagnostic system according to claim 1 or 2, wherein the diagnostic device updates a diagnostic rule based on the abnormality cause identification information.
5. The diagnostic device identifies the abnormal cause candidate, the physical phenomenon, and the engineering factor for a plurality of abnormal symptoms,
   The device diagnostic system according to claim 1 or 2, wherein the output device outputs the diagnostic results regarding the plurality of abnormal symptoms.
6. The device diagnosis system according to claim 1 or 2, wherein the output device outputs the diagnosis result as a Sankey diagram.
7. In a device diagnostic system that identifies abnormality cause candidates based on device state quantities,
   an input device that accepts the state quantity;
   a diagnostic device that identifies the abnormality cause candidate and a physical phenomenon or engineering factor related to the abnormality cause candidate based on the state quantity accepted by the input device and a predetermined diagnostic rule;
   An equipment diagnosis system comprising: an output device that outputs the abnormality cause candidate, the physical phenomenon, or the engineering factor as a diagnosis result.
8. In a device diagnosis method that identifies candidate causes of an abnormality based on state quantities of the device,
   an acceptance step of accepting the state quantity;
   a diagnosis step of identifying the abnormality cause candidate and physical phenomena and engineering factors related to the abnormality cause candidate based on the state quantity accepted in the acceptance step and a predetermined diagnostic rule;
   An apparatus diagnosis method comprising: an output step of outputting the abnormality cause candidate, the physical phenomenon, and the engineering factor as a diagnosis result.

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