JP2024072925A - Method and device for proposing on-site diagnosis for vehicle - Google Patents

Abstract

To propose an on-site diagnosis to a user when a sign of a vehicle failure is detected, provided that a required time is within a predetermined time, and eliminate the need to take the vehicle to a dealer.SOLUTION: A vehicle-side device detects a sign of a failure from vehicle data (step 101). A server side refers to a database of past failure sign detection results for other vehicles (step 103), and extracts one or more components with a high degree of match in a combination pattern of abnormal signals as candidate faulty components (step 105). After determining whether all of the candidate faulty components are compatible with on-site diagnosis (step 108), a time required for the on-site diagnosis is calculated (step 119), and if the required time is within a predetermined time, the on-site diagnosis is proposed to a user (steps 120 and 109).SELECTED DRAWING: Figure 6

Description

This invention relates to a method and device for proposing to a user an on-site diagnosis to diagnose a vehicle malfunction at the user's home or other location without the need to bring the vehicle to a dealer's repair shop.

If the vehicle is not functioning properly or there are signs of a breakdown, the user (owner of the vehicle) must take it to a dealer, i.e., the vehicle's sales company, to have it diagnosed. The diagnosis by a technician will identify the parts that need to be repaired or replaced. In some cases, it may be possible to replace or repair parts at the time of the diagnosis, such as replacing simple consumable parts, but in most cases, preparations for the repair or replacement must be made, such as ordering the parts identified in the diagnosis and adjusting the schedule, so the vehicle will be brought back to the dealer on a different date from the diagnosis. In other words, the user must visit the dealer twice.

Patent Document 1 discloses a maintenance notification device that compares the usage time and mileage of multiple parts with thresholds and notifies the user of vehicle parts that already require maintenance as well as vehicle parts that are close to due for maintenance, in order to reduce the number of times the user must bring the vehicle to a dealer for maintenance.

JP 2008-308075 A

For the average user, taking a vehicle to a dealer is a hassle in terms of time and effort. Therefore, even if they feel that something is not right with their vehicle, they tend to continue driving it without having it diagnosed at the dealer.

Patent Document 1 is based on the premise that the user will bring the vehicle to a dealer, and does not disclose a form of on-site diagnosis in which a dealer worker visits the user's home or other location to perform a diagnosis.

The vehicle on-site diagnosis proposing method according to the present invention comprises:
Detects signs of failure in vehicle parts,
Based on the symptoms, one or more candidate faulty parts are identified,
Calculate the time required for on-site diagnosis of potential faulty parts,
If the required time is within a predetermined time, an on-site diagnosis is proposed to the user.

The user receives a proposal for an on-site diagnosis, for example, via the vehicle's display or a mobile device owned by the user. If the user agrees to this proposal, a dealer's technician will visit the vehicle's storage location, such as the user's home, to perform a fault diagnosis. The vehicle parts that need to be repaired or replaced are finally determined based on the technician's fault diagnosis.

On-site diagnosis is convenient for users, but if the diagnosis requires a long period of work, it becomes a burden for both the worker who is doing the work on-site and the user who is doing the work in their home garage, etc., and on-site diagnosis is not necessarily efficient.

In this invention, one or more candidate faulty parts are identified, the time required for on-site diagnosis is calculated, and if the required time is within a predetermined time, an on-site diagnosis is proposed. If the required time exceeds the predetermined time, an on-site diagnosis is generally not proposed.

According to this invention, when a sign of a malfunction is detected, the user receives a proposal for an on-site diagnosis, provided that the required time is within a specified time. This allows the user to receive an on-site diagnosis with peace of mind, and saves the effort and time of having to take the vehicle to a dealer for diagnosis.

1 is a configuration explanatory diagram showing a first embodiment of an on-site diagnosis proposal system according to the present invention; FIG. 4 is an explanatory diagram showing data on the results of failure sign detection on the vehicle side in a table format. FIG. 4 is an explanatory diagram of a failure sign detection result database on the on-site diagnosis and determination device side. FIG. 4 is an explanatory diagram of an on-site diagnosis compatible parts information database. FIG. 13 is an explanatory diagram of an on-site diagnosis time required database. 4 is a flowchart showing a process flow according to the first embodiment. FIG. 11 is a diagram illustrating the degree of coincidence of failure symptom information. FIG. 13 is an explanatory diagram showing an example of a list of parts with high matching degrees. FIG. 13 is a configuration explanatory diagram showing a second embodiment of the on-site diagnosis proposal system; 10 is a flowchart showing a process flow according to a second embodiment. FIG. 13 is a configuration explanatory diagram showing a third embodiment of the on-site diagnosis proposal system; 13 is a flowchart showing a process flow according to a third embodiment. FIG. 13 is a configuration explanatory diagram showing a fourth embodiment of the on-site diagnosis proposal system; 13 is a flowchart showing a process flow according to a fourth embodiment. 10 is a flowchart showing a modified example of the process of identifying a candidate faulty part. FIG. 11 is an explanatory diagram of a failure symptom detection result database in step 202. FIG. 11 is an explanatory diagram of a failure symptom detection result history database in step 203. 10 is a flowchart showing another modified example of the process for identifying a candidate faulty part. FIG. 4 is an explanatory diagram of a signal importance database in step 206. FIG. 11 is an explanatory diagram showing the result of the processing in step 207. 10 is a flowchart showing yet another modified example of the process for identifying a candidate failed part. FIG. 4 is an explanatory diagram of a signal feature database in step 208. FIG. 11 is an explanatory diagram showing the result of the processing in step 209.

An embodiment of the present invention will be described in detail below with reference to the drawings. FIG. 1 is a configuration explanatory diagram showing a first embodiment of an on-site diagnosis proposal system according to the present invention. The on-site diagnosis proposal system is composed of a vehicle-side device 001 that is included in each vehicle (e.g., vehicle A) and an on-site diagnosis determination device 002 that is composed of a server or cloud computer installed at a dealer or the like.

The vehicle-side device 001 is a part of a so-called computer system of the vehicle, and includes a diagnostic device 003, a memory 004, a display unit 005, an ECU (Electronic Control Unit) 006, a communication unit 007, and a failure prediction unit 008. The diagnostic device 003 monitors various signals (also called vehicle data) obtained from each part of the vehicle and detects abnormal signals. The failure prediction unit 008 detects that a part is about to fail based on one or more abnormal signals, stores the detected abnormal signals in the memory 004, and transmits the detected abnormal signals to the on-site diagnosis and determination device 002 via the communication unit 007. The vehicle is configured as a so-called connected car, and is constantly connected to, for example, the Internet via the communication unit 007. Using this communication function, various information is transmitted and received between the vehicle-side device 001 and the on-site diagnosis and determination device 002. The display unit 005 is, for example, a display provided in front of the driver's seat, and displays various information.

When the failure prediction unit 008 detects a sign of failure, the failure prediction unit 008 may display the fact that there is a sign of failure on the display unit 005 in addition to transmitting the information to the on-site diagnosis determination device 002. Alternatively, the failure sign may not be displayed alone, but may be displayed together with a proposal for on-site diagnosis, which will be described later.

When the failure prediction unit 008 detects a failure sign, data of the failure sign detection result 050 is generated in the form shown in FIG. 2, and this is transmitted via the communication unit 007. As shown in FIG. 2, this data includes the vehicle identification number, the date and time when the sign was detected, and the content of the sign, i.e., the content of the abnormal signal (in the illustrated example, it includes three abnormal signals a, b, and c). It may also include other additional information.

The on-site diagnosis determination device 002 includes a communication unit 009, a faulty part identification unit 010, a fault sign consistency diagnosis unit 011, a fault sign detection result database 012, an on-site diagnosis feasibility determination unit 013, an on-site diagnosis compatible parts information database 014, an on-site diagnosis proposal unit 015, an on-site diagnosis required time calculation unit 025, and an on-site diagnosis required time database 024. Note that each of these units does not necessarily have to be configured by a single computer, and can be configured by multiple servers or cloud computers.

The communication unit 009 communicates with the vehicle-side communication unit 007 via an appropriate communication network. When a sign of a malfunction is detected on the vehicle side, the communication unit 009 receives the data of the malfunction sign detection result 050 shown in FIG. 2 from the vehicle side, and when a proposal for an on-site diagnosis is finally made as described below, the communication unit 009 transmits information to that effect to the vehicle side.

The failure sign detection result database 012 stores a large amount of diagnostic data that summarizes past signs and diagnostic results of other vehicles (which may include past data of the vehicle itself), such as failure sign detection result data 051 as shown in FIG. 3. As shown in FIG. 3, the data 051 of the failure sign detection result database 012 includes information such as the vehicle identification number, the sign detection content, which indicates what part is abnormal, the date and time when the sign was detected, the content of the abnormal signal, the name of the part that was actually repaired and replaced, and the result after the repair and replacement (i.e., whether the defect was resolved). If the result after the repair and replacement is good, it means that the part that was repaired and replaced based on the sign was appropriate. The data 051 of the failure sign detection result database 012 may include other additional information. For example, information such as the elapsed time (time or mileage) from the time when the sign was detected to the actual repair and replacement, or the elapsed time (time or mileage) until the failure occurred if the failure occurred while the failure was left unattended, may be added.

The failure sign coincidence diagnosis unit 011 determines the degree of coincidence between the contents of the sign detected on the vehicle side and the contents of the sign included in the past data 051 in the failure sign detection result database 012. For example, if the contents of the sign obtained by the failure prediction unit 008 are a combination of abnormal signals "a, b, c", a combination pattern including the combination of abnormal signals "a, b, c" is determined to be a combination pattern with a high degree of coincidence in the past data 051 in the failure sign detection result database 012. In the example of FIG. 3, the combinations of abnormal signals "a, b, c" and "a, b, c, f" are determined to be coincident patterns with a high degree of coincidence. Note that the combination of "a, b, c" has a higher degree of coincidence than the combination of "a, b, c, f". A combination pattern that does not include "a, b, c" (partially missing) or a combination pattern that includes multiple abnormal signals other than "a, b, c" is determined to be a non-matching pattern with a low degree of coincidence. Note that in the present invention, the degree of coincidence required is set to any level.

The faulty part identification unit 010 identifies parts that have a high degree of match with the contents of the signs, based on the degree of match between the contents of the signs detected on the vehicle side by the fault sign consistency diagnosis unit 011 and the contents of the signs in the past data 051. The parts identified here become candidate faulty parts corresponding to the signs. These candidate faulty parts may include multiple parts with a high degree of match. Note that it is desirable to exclude from the candidate faulty parts those parts that have been repaired or replaced based on the signs in the past data 051 but have not produced good results, even if the degree of match with the contents of the signs is high.

The on-site diagnosis availability determination unit 013 refers to the on-site diagnosis compatible parts information database 014 to determine whether on-site diagnosis is possible. As shown in FIG. 4, the on-site diagnosis compatible parts information database 014 stores a large amount of parts information data 052 with information on whether on-site diagnosis is possible for each of all parts (or even a major part) included in the vehicle. For example, on-site diagnosis is not possible for parts that are difficult to diagnose without large-scale equipment or special equipment in a repair shop. The on-site diagnosis availability determination unit 013 refers to the on-site diagnosis compatible parts information database 014 to determine whether on-site diagnosis is possible for each of the defective part candidates identified by the defective part identification unit 010, and determines that on-site diagnosis is possible when on-site diagnosis is possible for all defective part candidates. It is desirable to update the on-site diagnosis compatible parts information database 014 successively when an on-site diagnosis or repair or replacement of a part is actually performed. In particular, for parts with new specifications, it is desirable to update the on-site diagnosis compatible parts information database 014 and the on-site diagnosis required time database 024 (described below) to store new information when an on-site diagnosis or part repair or replacement is actually performed.

As another example, the on-site diagnosis feasibility determination unit 013 may determine that on-site diagnosis is possible even if some of the candidate faulty parts cannot be treated by on-site diagnosis. For example, the probability that each of the multiple candidate faulty parts is a part that needs to be repaired or replaced is calculated, and if the probability that the candidate faulty part can be treated by on-site diagnosis is equal to or greater than a predetermined threshold, on-site diagnosis is possible even if the candidate faulty part cannot be treated by on-site diagnosis. Note that the above probability is calculated, for example, based on the degree of agreement of the contents of the signs described above and the past fault sign detection result data 051.

Alternatively, multiple faulty part candidates may be arranged in order of priority, and if the top-ranked faulty part candidates in a specified range are capable of being diagnosed on-site, then on-site diagnosis may be possible even if lower-ranked faulty part candidates are not capable of being diagnosed on-site. For example, if the top three faulty part candidates arranged in order of priority are capable of being diagnosed on-site, then on-site diagnosis may be possible even if lower-ranked faulty part candidates are not capable of being diagnosed on-site.

When the on-site diagnosis feasibility determination unit 013 determines that an on-site diagnosis is possible, the on-site diagnosis required time calculation unit 025 refers to the on-site diagnosis required time database 024 to calculate the required time for the on-site diagnosis. As shown in FIG. 5, the on-site diagnosis required time database 024 stores a large amount of required time data 057 for all parts included in the vehicle (which may be only parts that can be on-site diagnosed), each of which includes information on the required time for the on-site diagnosis. The on-site diagnosis required time calculation unit 025 calculates the required time for the on-site diagnosis by adding up the required times for multiple parts identified as possible faulty parts.

If the required time is within a predetermined time, the on-site diagnosis suggestion unit 015 proposes an on-site diagnosis to the vehicle user. Specifically, information regarding the proposal for an on-site diagnosis is sent to the vehicle-side device 001 via the communication units 009, 007, and the information is displayed (which may also include audio) on the vehicle's display unit 005 or a mobile terminal (such as a so-called smartphone) held by the user. For example, the user is informed of information such as the presence of signs of a malfunction and the availability of an on-site diagnosis at home, etc.

Figure 6 is a flowchart showing the process flow of the on-site diagnosis proposal system of the first embodiment. Note that the flowchart in Figure 6 shows each step in the order of the overall process flow, without distinguishing between the process performed by the vehicle-side device 001 and the process performed by the on-site diagnosis determination device 002.

In step 101, the diagnosis device 003 and the failure prediction unit 008 of the vehicle-side device 001 predict failure (detection of a failure sign) based on the vehicle data. In step 102, the failure prediction information detected here (failure sign detection result 050) is transmitted from the vehicle-side device 001 to the on-site diagnosis and determination device 002.

In step 103, data 051 stored in the failure sign detection result database 012, that is, information on failure signs of other vehicles, is referenced. In step 104, the degree of agreement between the failure sign detection result 050 sent from the vehicle-side device 001 and the information on failure signs of other vehicles obtained from the failure sign detection result database 012 is diagnosed. As described above, a combination pattern of multiple abnormal signals is used to diagnose the degree of agreement. For example, as shown in FIG. 7, if the contents of the detected sign are a combination of abnormal signals "a, b, c", the combination of abnormal signals "a, b, c" or "a, b, c, f" is a matching pattern with a high degree of agreement, and when the signals are different, such as "g, f" or "s, t", or a combination pattern including multiple other abnormal signals, such as "a, b, c, f, g, h", is a non-matching pattern with a low degree of agreement.

Then, in step 105, the parts with a high degree of match are narrowed down to candidates for the faulty part. There may be multiple candidates here. For example, in the example of Figure 8, two parts, a plug and an injector, are extracted as candidates for the faulty part with a high degree of match for the combination of abnormal signals "a, b, c."

Next, in step 106, the data 052 stored in the on-site diagnosis compatible parts information database 014 is referenced, and in step 107, multiple parts that are candidate faulty parts (parts with a high degree of match in step 105) are compared with the data 052 in the on-site diagnosis compatible parts information database 014. For example, in the data 052 illustrated in FIG. 4, plugs and injectors are parts that are candidate faulty parts that are candidate faulty parts for on-site diagnosis. In step 108, it is determined whether all of the candidate faulty parts with a high degree of match are parts that are candidate faulty parts for on-site diagnosis. If all of the candidate faulty parts are parts that are candidate faulty parts for on-site diagnosis, the process proceeds to step 118. If even one of the candidate faulty parts is not a part that is candidate faulty parts for on-site diagnosis, the process proceeds to step 110, and on-site diagnosis is not proposed.

As described above with regard to the on-site diagnosis feasibility determination unit 013, even if some of the candidate faulty parts are not suitable for on-site diagnosis in step 108, it may be determined that on-site diagnosis is possible, for example, depending on priority, and the process may proceed to step 118.

In step 118, data 057 stored in the on-site diagnosis time database 024 is referenced for the faulty part candidates with a high degree of match, and in step 119, the on-site diagnosis time (total time) for all the faulty part candidates is calculated. For example, the data 057 shown in FIG. 5 indicates that the time required for on-site diagnosis is "15 minutes" for plugs and "30 minutes" per injector. Generally, the labor costs incurred in connection with work are recorded as one unit per unit of time (e.g., 15 minutes), so the data 057 in the on-site diagnosis time database 024 is also set to be a multiple of 15 minutes.

Then, in step 120, it is determined whether the total time required for on-site diagnosis is within a predetermined time for on-site diagnosis. If it is within the predetermined time, the process proceeds to step 109, where an on-site diagnosis is proposed via the vehicle's display unit 005 or a mobile terminal held by the user. When proposing an on-site diagnosis, it is desirable to also present the calculated required time. Although not included in the flowchart, the user who has received the proposal for on-site diagnosis ultimately decides whether or not to request an on-site diagnosis. The predetermined time is set appropriately, for example, to one or two hours.

On the other hand, if the specified time has passed, the process proceeds to step 110 and an on-site diagnosis is not suggested. If an on-site diagnosis is not suggested, a message may be displayed indicating that an on-site diagnosis is not appropriate, or nothing may be displayed.

Next, a second embodiment of the present invention will be described with reference to Figures 9 and 10. The following mainly describes the differences from the first embodiment. The second embodiment differs from the first embodiment in that the user can input the desired time required for an on-site diagnosis, and in that if it is difficult to complete the diagnosis within the user's desired time, options such as additional fees are presented and the user is asked about them.

Figure 9 is a configuration diagram showing a second embodiment of the on-site diagnosis proposal system according to the present invention. The on-site diagnosis proposal system is composed of a vehicle-side device 001 that is installed in each vehicle, an on-site diagnosis determination device 002 that is a server or cloud computer installed in a dealer or the like, and a mobile terminal 040 (e.g., a smartphone, etc.) that is installed in the user's possession.

The vehicle-side device 001 is similar to that of the first embodiment and includes a diagnostic device 003, a memory 004, a display unit 005, an ECU (Electronic Control Unit) 006, a communication unit 007, and a failure prediction unit 008.

The on-site diagnosis determination device 002 is not particularly different from that of the first embodiment, and is configured to include a communication unit 009, a faulty part identification unit 010, a fault sign consistency diagnosis unit 011, a fault sign detection result database 012, an on-site diagnosis feasibility determination unit 013, an on-site diagnosis compatible parts information database 014, an on-site diagnosis proposal unit 015, an on-site diagnosis required time calculation unit 025, and an on-site diagnosis required time database 024. The on-site diagnosis proposal unit 015 determines whether to propose an on-site diagnosis, taking into account the user's desired required time.

The mobile terminal 040 includes a display/input unit 041 that serves as an interface with the user, and a communication unit 042 that can exchange information with the server, i.e., the communication unit 009 of the on-site diagnosis determination device 002, via the Internet or an appropriate communication network. Information sent from the on-site diagnosis determination device 002, including a proposal for on-site diagnosis, is displayed on the display/input unit 041 of the mobile terminal 040, which is a smartphone or the like owned by the user. Meanwhile, the user can input the user's desired time required for the on-site diagnosis via the display/input unit 041, and if the on-site diagnosis can be completed within that time, the on-site diagnosis is proposed to the user from the on-site diagnosis determination device 002 via the display/input unit 041.

Figure 10 is a flowchart showing the process flow of the on-site diagnosis proposal system of the second embodiment. The basic process flow is the same as in the first embodiment, and in step 101, the diagnosis device 003 and failure prediction unit 008 of the vehicle-side device 001 predict failure (detection of signs) based on vehicle data. The failure prediction information detected here (failure sign detection result 050) is transmitted from the vehicle-side device 001 to the on-site diagnosis and determination device 002 in step 102.

In step 103, the data 051 stored in the failure sign detection result database 012, i.e., information on failure signs of other vehicles, is referenced. In step 104, the degree of agreement between the failure sign detection result 050 sent from the vehicle-side device 001 and the information on failure signs of other vehicles obtained from the failure sign detection result database 012 is diagnosed. As described above, a combination pattern of multiple abnormal signals is used to diagnose the degree of agreement.

Then, in step 105, the parts with the highest degree of match are narrowed down to candidates for the faulty part. Here, there may be multiple candidates.

Next, in step 106, the data 052 stored in the on-site diagnosis compatible parts information database 014 is referenced, and in step 107, multiple parts that are candidate faulty parts (parts with a high degree of match in step 105) are compared with the data 052 in the on-site diagnosis compatible parts information database 014. In step 108, it is determined whether all of the candidate faulty parts with a high degree of match are parts that are compatible with on-site diagnosis. If all of the candidate faulty parts are parts that are compatible with on-site diagnosis, the process proceeds to step 118. If even one of the candidate faulty parts is not a part that is compatible with on-site diagnosis, the process proceeds to step 110, and on-site diagnosis is not proposed.

In step 118, the data 057 stored in the on-site diagnosis time database 024 is referenced for the faulty part candidates with a high degree of match, and in step 119, the on-site diagnosis time (total time) for all of the multiple faulty part candidates is calculated.

Meanwhile, in step 121, the user inputs the desired time required for the on-site diagnosis. In one example, the on-site diagnosis determination device 002 inquires of the user about the desired time required via the mobile terminal 040, and the user responds to this inquiry by inputting the desired time required. Basically, the time required for the diagnosis correlates with the fee borne by the user, so this desired time required can also be considered as the cost that the user is willing to accept for the on-site diagnosis.

Then, in step 122, it is determined whether the total time required for on-site diagnosis is within the user's desired time. If it is within the desired time, the process proceeds to step 109, and an on-site diagnosis is proposed via the mobile terminal 040 held by the user. When proposing an on-site diagnosis, it is desirable to also present the calculated required time.

On the other hand, if the desired required time is exceeded, the process proceeds to step 129, where additional options are presented to the user via the mobile terminal 040. Additional options may include, for example, a charge for the amount of time that is exceeded, a discount for a longer period of time, or an additional charge for increasing the number of workers required to complete the diagnosis within the desired time. In step 130, it is determined whether the user has accepted the additional options presented in this manner. The user can input whether or not the additional options are acceptable via the display/input unit 041 of the mobile terminal 040. If the user accepts the additional options, the process proceeds to step 109, where an on-site diagnosis is proposed, and if the user does not accept the additional options, the process proceeds to step 110, where an on-site diagnosis is not proposed.

Next, a third embodiment of the present invention will be described with reference to Figs. 11 and 12. The third embodiment differs from the second embodiment in that, when the calculated required time exceeds a predetermined time, the probability that an on-site diagnosis will be completed within the predetermined time is calculated and presented to the user, and the user is then asked whether or not he or she wishes to have an on-site diagnosis.

Figure 11 is a configuration diagram showing a third embodiment of the on-site diagnosis proposal system according to the present invention. The on-site diagnosis proposal system is composed of a vehicle-side device 001 that is installed in each vehicle, an on-site diagnosis determination device 002 that is a server or cloud computer installed in a dealer or the like, and a mobile terminal 040 that is owned by the user.

The vehicle-side device 001 is similar to that of the first embodiment and includes a diagnostic device 003, a memory 004, a display unit 005, an ECU (Electronic Control Unit) 006, a communication unit 007, and a failure prediction unit 008.

As in the second embodiment, the mobile terminal 040 includes a display/input unit 041 that serves as an interface with the user, and a communication unit 042 that can send and receive information to and from the server, i.e., the communication unit 009 of the on-site diagnosis determination device 002. Information sent from the on-site diagnosis determination device 002, including a proposal for on-site diagnosis and the probability described below, is displayed on the display/input unit 041 of the mobile terminal 040, which is a smartphone or the like owned by the user. The user can input the user's desired time required for the on-site diagnosis via the display/input unit 041. Furthermore, if the desired time required is exceeded, the user can input whether or not they wish to have an on-site diagnosis, taking into account the probability described below, via the display/input unit 041.

The on-site diagnosis determination device 002 is basically the same as that in the second embodiment, and includes a communication unit 009, a faulty part identification unit 010, a fault sign consistency diagnosis unit 011, a fault sign detection result database 012, an on-site diagnosis feasibility determination unit 013, an on-site diagnosis compatible parts information database 014, an on-site diagnosis proposal unit 015, an on-site diagnosis required time calculation unit 025, and an on-site diagnosis required time database 024. In the third embodiment, it further includes a diagnosis probability calculation unit 026 that calculates the probability that the diagnosis will be completed within the required time desired by the user. When the required time desired by the user is exceeded, the on-site diagnosis proposal unit 015 considers the user's desire for an on-site diagnosis in response to this diagnosis probability and determines whether to propose an on-site diagnosis.

Figure 12 is a flowchart showing the process flow of the on-site diagnosis proposal system of the third embodiment. The basic process flow is the same as in the second embodiment, and in step 101, the diagnosis device 003 and failure prediction unit 008 of the vehicle-side device 001 predict failure (detection of signs) based on vehicle data. The failure prediction information detected here (failure sign detection result 050) is transmitted from the vehicle-side device 001 to the on-site diagnosis and determination device 002 in step 102.

In step 103, the data 051 stored in the failure sign detection result database 012, i.e., information on failure signs of other vehicles, is referenced. In step 104, the degree of agreement between the failure sign detection result 050 sent from the vehicle-side device 001 and the information on failure signs of other vehicles obtained from the failure sign detection result database 012 is diagnosed. As described above, a combination pattern of multiple abnormal signals is used to diagnose the degree of agreement.

Then, in step 105, the parts with the highest degree of match are narrowed down to candidates for the faulty part. Here, there may be multiple candidates.

Next, in step 106, the data 052 stored in the on-site diagnosis compatible parts information database 014 is referenced, and in step 107, multiple parts that are candidate faulty parts (parts with a high degree of match in step 105) are compared with the data 052 in the on-site diagnosis compatible parts information database 014. In step 108, it is determined whether all of the candidate faulty parts with a high degree of match are parts that are compatible with on-site diagnosis. If all of the candidate faulty parts are parts that are compatible with on-site diagnosis, the process proceeds to step 118. If even one of the candidate faulty parts is not a part that is compatible with on-site diagnosis, the process proceeds to step 110, and on-site diagnosis is not proposed.

In step 118, the data 057 stored in the on-site diagnosis time database 024 is referenced for the defective part candidates with a high degree of match, and in step 119, the on-site diagnosis time (total time) for the multiple defective part candidates is calculated.

Meanwhile, in step 121, the user inputs the desired time required for the on-site diagnosis. In one example, the on-site diagnosis determination device 002 inquires of the user about the desired time required via the mobile terminal 040, and the user responds to this inquiry to input the desired time required.

Then, in step 122, it is determined whether the total time required for on-site diagnosis is within the user's desired time. If it is within the desired time, the process proceeds to step 109, and an on-site diagnosis is proposed via the mobile terminal 040 held by the user. When proposing an on-site diagnosis, it is desirable to also present the calculated required time.

On the other hand, if the desired required time is exceeded, the process proceeds to step 123, where the probability that the on-site diagnosis will be completed within the user's desired required time is calculated, and this probability is notified to the user's mobile terminal 040. For example, even if the on-site diagnosis required time calculated in step 118 as the sum of the required times for multiple faulty part candidates exceeds the desired required time, if the degree of match of one faulty part candidate with respect to the contents of the failure symptom is sufficiently high and the degree of match of other faulty part candidates is relatively low, in the actual diagnostic work, it may be possible to identify the part that needs to be repaired or replaced by only diagnosing the parts with a high degree of match. Also, if the calculated on-site diagnosis required time slightly exceeds the desired required time, the actual diagnostic work may be completed within the desired required time. Taking such various conditions into consideration, the probability that the on-site diagnosis will be completed within the desired required time is calculated. By receiving such a probability, the user can obtain information on whether or not to perform an on-site diagnosis.

In step 124, it is determined whether or not the user desires an on-site diagnosis based on the probability presented in this manner. The user can input a desire for an on-site diagnosis via the display/input unit 041 of the mobile terminal 040. If the user desires an on-site diagnosis, the process proceeds to step 109 where an on-site diagnosis is proposed, and if the user does not actively desire an on-site diagnosis, the process proceeds to step 110 where an on-site diagnosis is not proposed.

Next, a fourth embodiment of the present invention will be described with reference to Figures 13 and 14. The fourth embodiment differs from the first embodiment in that failure prediction is performed not by the vehicle-side device 001 but by an on-site diagnosis and determination device 002 managed by a dealer or the like, and in that a diagnosis is made as to whether the vehicle is in a condition in which it can be driven to a repair shop and appropriate suggestions are made.

Figure 13 is a configuration diagram showing a fourth embodiment of the on-site diagnosis proposal system according to the present invention. The on-site diagnosis proposal system is composed of a vehicle-side device 001 that is installed in each vehicle, and an on-site diagnosis determination device 002 that is a server or cloud computer installed at a dealer or the like. Although not shown, it may also include a mobile terminal 040 owned by a user, as in the second and third embodiments.

The vehicle-side device 001, like the first embodiment, includes a diagnostic device 003, a memory 004, a display unit 005, an ECU (Electronic Control Unit) 006, and a communication unit 007. However, the vehicle-side device 001 does not include a failure prediction unit 008.

The on-site diagnosis determination device 002 is basically configured to include a communication unit 009, a faulty part identification unit 010, a fault sign consistency diagnosis unit 011, a fault sign detection result database 012, an on-site diagnosis feasibility determination unit 013, an on-site diagnosis compatible part information database 014, an on-site diagnosis proposal unit 015, an on-site diagnosis required time calculation unit 025, and an on-site diagnosis required time database 024, as in the first embodiment. In the fourth embodiment, the fault prediction unit 008 is configured as a part of the on-site diagnosis determination device 002. The fault prediction unit 008 detects that a part is a sign of failure based on one or more abnormal signals sent from the diagnosis device 003 of the vehicle side device 001 via the communication units 007 and 009. When the fault prediction unit 008 detects a fault sign, data of the fault sign detection result 050 as shown in FIG. 2 is generated. This is basically the same data as in the first embodiment, and as shown in FIG. 2, it includes, for example, the vehicle identification number, the date and time when the symptom was detected, and the contents of the abnormal signal (in the illustrated example, it includes three abnormal signals a, b, and c). It may also include other additional information.

The on-site diagnosis and determination device 002 of the fourth embodiment further includes a vehicle condition diagnosis unit 027 that diagnoses the condition of the vehicle based on the vehicle data (including abnormality signals) sent from the vehicle-side device 001. In this embodiment, the vehicle condition diagnosis unit 027 mainly diagnoses whether the vehicle is in a condition to be driven.

Figure 14 is a flowchart showing the process flow of the on-site diagnosis proposal system of the fourth embodiment. The basic process flow is the same as in the first embodiment, but in step 101, the on-site diagnosis determination device 002 predicts failure (detects signs of failure) based on the vehicle data sent from the vehicle-side device 001.

In step 103, the data 051 stored in the failure sign detection result database 012, i.e., information on failure signs of other vehicles, is referenced. In step 104, the degree of agreement between the failure sign detection result 050 output by the failure prediction unit 008 and the information on failure signs of other vehicles obtained from the failure sign detection result database 012 is diagnosed. As described above, a combination pattern of multiple abnormal signals is used to diagnose the degree of agreement.

Then, in step 105, the parts with the highest degree of match are narrowed down to candidates for the faulty part. Here, there may be multiple candidates.

Next, in step 106, the data 052 stored in the on-site diagnosis compatible parts information database 014 is referenced, and in step 107, multiple parts that are candidate faulty parts (parts with a high degree of match in step 105) are compared with the data 052 in the on-site diagnosis compatible parts information database 014. In step 108, it is determined whether all of the candidate faulty parts with a high degree of match are parts that are compatible with on-site diagnosis. If all of the candidate faulty parts are parts that are compatible with on-site diagnosis, the process proceeds to step 118. If even one of the candidate faulty parts is not a part that is compatible with on-site diagnosis, the process proceeds to step 110, and on-site diagnosis is not proposed.

In step 118, the data 057 stored in the on-site diagnosis time database 024 is referenced for the faulty part candidates with a high degree of match, and in step 119, the on-site diagnosis time (total time) for all of the multiple faulty part candidates is calculated.

Then, in step 120, it is determined whether the total time required for on-site diagnosis is within a predetermined time for on-site diagnosis. If it is within the predetermined time, the process proceeds to step 109, and an on-site diagnosis is proposed via the vehicle's display unit 005 or a mobile terminal 040 held by the user. When proposing an on-site diagnosis, it is desirable to also present the calculated required time.

On the other hand, if the predetermined time has been exceeded, the process proceeds to step 125, where it is diagnosed whether the vehicle is in a condition to be driven. If the vehicle is in a condition to be driven, the process proceeds to step 126, and an on-site diagnosis is not suggested. The process then proceeds to step 127, and it is suggested via the vehicle's display unit 005 or the user's mobile terminal 040, etc., that the user bring the vehicle to a dealer for diagnosis (dealer visit diagnosis).

If it is determined in step 125 that the vehicle is not in a condition to be driven, the process proceeds to step 128, where a suggestion is made via the vehicle's display unit 005 or the user's mobile terminal 040, etc., that the vehicle be transported to the dealer by tow truck for diagnosis and repair. In this case, if the user accepts the suggestion, the dealer will make arrangements for a tow truck, etc.

In addition, when determining whether or not to drive in step 125, the user may be asked about his or her convenience and intentions and these may be taken into consideration in addition to the vehicle's condition.

According to this fourth embodiment, the vehicle's condition and other factors are taken into consideration and appropriate measures, such as an on-site diagnosis, a dealer visit diagnosis, or transportation by tow truck, are suggested to the user.

Next, FIG. 15 is a flowchart showing a modified example of the process for identifying candidate faulty parts. This corresponds to the process of steps 103 to 105 of the first embodiment (FIG. 6). In step 201, the fault prediction unit 008 included in the vehicle-side device 001 or the on-site diagnosis and determination device 002 predicts a fault based on vehicle data. Here, data of the fault sign detection result 050 as shown in FIG. 2 is generated. In step 203, the data of the current fault sign detection result 050 detected in step 201 is added to the fault sign detection result history database 061 shown in FIG. 17. In other words, the current fault sign detection result 050 is stored. The fault sign detection result history database 061 created in this way is a database related to fault signs of the vehicle.

In step 202, information on the signs of failure of other vehicles is referenced from the signs of failure detection result database 062. The signs of failure detection result database 062 in this embodiment basically corresponds to the signs of failure detection result database 012 (FIG. 3) in the first embodiment, but also includes the history of several past abnormal signals for each vehicle, as shown in FIG. 16.

In the next step 204, the degree of agreement between the failure sign information in the failure sign detection result database 062 and the failure sign history in the failure sign detection result history database 061 related to the vehicle is diagnosed. In this agreement diagnosis, a combination pattern of abnormal signals including past history is used. For example, if the combination pattern of abnormal signals in the failure sign detection result history database 061 of the vehicle is "a, b, c" for the first time, "a, b" for the second time, and "a, b, c" for the third time (see FIG. 17), the combination pattern of "a, b, c" and the combination pattern of "a, b" are determined as matching patterns among the combination patterns including the abnormal signal history in the failure sign detection result database 062 of the other vehicle. On the other hand, combination patterns that include signals other than a, b, and c, such as "a, b, c, f," and combination patterns that do not include a and b are determined as non-matching patterns. In the example of FIG. 16, the part surrounded by a thick solid line is a matching pattern.

Then, in step 205, the parts with the highest degree of match are narrowed down to candidates for the faulty part. Here, there may be multiple candidates.

Next, FIG. 18 is a flow chart showing another modified example of the process for identifying candidate faulty parts. This corresponds to the process of steps 103 to 105 in the first embodiment (FIG. 6). In step 201, the fault prediction unit 008 included in the vehicle-side device 001 or the on-site diagnosis and determination device 002 performs fault prediction based on vehicle data. Here, data of the fault sign detection result 050 as exemplified in FIG. 2 is generated. In step 202, information on the fault signs of other vehicles is referenced from the fault sign detection result database 012 (FIG. 3). In step 204, the degree of agreement between the fault sign detection result 050 of the vehicle itself and the information on the fault signs of other vehicles obtained from the fault sign detection result database 012 is diagnosed. As described above, a combination pattern of multiple abnormal signals is used to diagnose the degree of agreement.

In step 206, information in a signal importance database (also called a signal importance list) 063 that summarizes the importance information of abnormal signals is referenced, and in the next step 207, the priority is assigned taking into account the importance information of the abnormal signals, and the parts with a high degree of match (potential faulty parts) are narrowed down. Figure 19 shows an example of the signal importance database 063. Also, Figure 20 is an explanatory diagram showing the results of the ranking as a table 064.

In the example of FIG. 19, in the signal importance database 063, for example, when the precursor detection content is a plug, the combination patterns of abnormal signals are "a, b, c" and "a, b", and the importance of the abnormal signal is recorded as "a, b>c". In other words, the importance of signal c is low. On the other hand, when the precursor detection content is an injector, the combination patterns of abnormal signals are "a, b, c, f" and "a, b, f", and the importance of the abnormal signal is recorded as "a, f>b>c".

Therefore, in this example, if the combination of abnormal signals that are detected as a sign of failure is "a, b, c," it is determined that the combination does not include signal f, which is a signal with high importance for the injector, and therefore the degree of match with the injector is lower than with the plug when narrowing down the candidates for the faulty part (see Figure 20).

Next, FIG. 21 is a flow chart showing yet another modified example of the process for identifying candidate faulty parts. This corresponds to the process of steps 103 to 105 of the first embodiment (FIG. 6). In step 201, the fault prediction unit 008 included in the vehicle-side device 001 or the on-site diagnosis and determination device 002 predicts a fault based on vehicle data. Here, data of the fault sign detection result 050 as shown in FIG. 2 is generated. In step 202, information on the fault signs of other vehicles is referenced from the fault sign detection result database 012 (FIG. 3). In step 204, the degree of agreement between the fault sign detection result 050 of the vehicle itself and the information on the fault signs of other vehicles obtained from the fault sign detection result database 012 is diagnosed. As described above, a combination pattern of multiple abnormal signals is used to diagnose the degree of agreement.

In step 208, the information in the signal feature database 065, which summarizes information related to the feature quantities of abnormal signals, is referenced, and in the next step 209, a priority is assigned to the parts that had a relatively high degree of match in step 204. FIG. 22 shows an example of the signal feature database 065. FIG. 23 is an explanatory diagram showing the results of the ranking in the form of a table 066.

In the example of FIG. 22, the signal feature database 065 records, for example, that when the precursor detection content is a plug, there are abnormal signals a, b, and c, and the feature of signal a is the kurtosis of 10 minutes of data, the feature of signal b is the variance of 10 minutes of data, and the feature of signal c is the difference value of 1 minute of data.

On the other hand, when the precursor detection content is an injector, the abnormal signals are a, b, c, and f, and it is recorded that the feature of signal a is the kurtosis of 10 minutes of data, the feature of signal b is the variance of 10 minutes of data, the feature of signal c is the average value of 1 minute of data, and the feature of signal f is the variance of 10 minutes of data.

Therefore, for example, the feature amount of the abnormal signal c of a malfunction sign detected by the vehicle-side device 001 is the difference value of one minute of data for plugs and the average value of one minute of data for injectors, which are different from each other, so by focusing on the abnormal signal c, it is possible to determine the degree of match with higher accuracy. In other words, by diagnosing the degree of match using the type of signal that has a high contribution to the abnormality classification among the signals in which an abnormality is detected, the accuracy of identifying candidate malfunctioning parts is increased. In addition, when an on-site diagnosis is actually performed, the probability of completing the diagnosis within the scheduled time is increased.

The signal importance database 063 and signal feature database 065 described above can be constructed relatively easily by using accumulated data from the past. Even if the failure mechanism of a part is not clear, the importance of the signal, etc. can be known from past data.

Description of the Reference Numerals 001... Vehicle-side device 002... On-site diagnosis determination device 003... Diagnosis device 005... Display unit 007... Communication unit 008... Failure prediction unit 009... Communication unit 010... Failure part identification unit 011... Failure sign coincidence diagnosis unit 012... Failure sign detection result database 013... On-site diagnosis feasibility determination unit 014... On-site diagnosis compatible part information database 015... On-site diagnosis proposal unit 024... On-site diagnosis required time database 025... On-site diagnosis required time calculation unit 040... Mobile terminal

Claims (22)

Detects signs of failure in vehicle parts,
Based on the symptoms, one or more candidate faulty parts are identified,
Calculate the time required for on-site diagnosis of potential faulty parts,
If the required time is within a predetermined time, a proposal for on-site diagnosis is made to the user.
How to propose on-site vehicle diagnostics. moreover,
Determine whether each defective part can be diagnosed by on-site inspection,
Propose an on-site diagnosis to the user, provided that on-site diagnosis is possible.
The method for proposing on-site diagnosis of a vehicle according to claim 1. Proposing on-site diagnostics via the vehicle's display or the user's mobile device;
The method for proposing on-site diagnosis of a vehicle according to claim 1. The calculated required time is presented to the user when proposing an on-site diagnosis.
The method for proposing on-site diagnosis of a vehicle according to claim 1. Identifying potential faulty parts by referring to diagnostic data that compiles past signs and diagnostic results for other vehicles;
The method for proposing on-site diagnosis of a vehicle according to claim 1. The diagnostic data includes the contents of past signs and the correspondence between repaired and replaced parts that have shown good results after repair and replacement in response to the signs,
Extracting, as candidate faulty parts, parts to be repaired or replaced that have a high degree of match between the contents of the detected signs and the contents of past signs in the diagnostic data;
The method for proposing on-site diagnosis of a vehicle according to claim 5. Based on the degree of match between the detected symptoms and past symptoms in the diagnostic data, multiple candidate faulty parts with a relatively high degree of match are extracted;
The method for proposing on-site diagnosis of a vehicle according to claim 6. Based on preset information on parts that can be diagnosed on-site, it is determined whether each of the defective parts can be diagnosed on-site.
The method for proposing on-site diagnosis of a vehicle according to claim 2. The on-site diagnosis compatible parts information is updated using data from an actual on-site diagnosis.
The method for proposing on-site diagnosis of a vehicle according to claim 8. Propose an on-site diagnosis to the user on the condition that all of the multiple defective part candidates can be diagnosed on-site.
The method for proposing on-site diagnosis of a vehicle according to claim 2. determining a probability that each of a plurality of candidate faulty parts is a part that needs to be repaired or replaced;
If the probability of the candidate faulty parts being able to be diagnosed by on-site diagnosis is equal to or greater than a predetermined threshold, an on-site diagnosis is proposed even if the candidate faulty parts being able to be diagnosed by on-site diagnosis is included.
The method for proposing on-site diagnosis of a vehicle according to claim 2. A plurality of faulty part candidates are arranged in order of priority, and if the faulty part candidates in a predetermined upper range are capable of being diagnosed on-site, an on-site diagnosis is proposed even if the lower range includes faulty part candidates that cannot be diagnosed on-site.
The method for proposing on-site diagnosis of a vehicle according to claim 2. The system detects signs of failure in vehicle parts on the vehicle side,
This is sent to the server as predictive information.
This server identifies potential faulty parts, calculates the required time, and proposes on-site diagnosis.
The method for proposing on-site diagnosis of a vehicle according to claim 1. If the calculated required time exceeds a predetermined time, a choice including additional conditions required for receiving an on-site diagnosis is presented to the user.
The method for proposing on-site diagnosis of a vehicle according to claim 1. If the calculated required time exceeds a predetermined time, a probability that the on-site diagnosis will be completed within the predetermined time is calculated and presented to the user.
The method for proposing on-site diagnosis of a vehicle according to claim 1. If the calculated time exceeds a certain time, we will suggest that you take the vehicle to a dealer.
The method for proposing on-site diagnosis of a vehicle according to claim 1. Furthermore, it is determined whether the vehicle can be driven to a repair shop,
If the vehicle is not drivable, we will suggest transporting it to a dealer.
The method for proposing on-site diagnosis of a vehicle according to claim 16. The above-mentioned predetermined time is an upper limit of the operation time for the on-site diagnosis that is arbitrarily set by the user.
The method for proposing on-site diagnosis of a vehicle according to claim 1. The contents of the above-mentioned signs include a plurality of abnormal signals,
A history of abnormal signals detected in the vehicle is stored,
Considering the history of the abnormal signals, the degree of agreement between the contents of the detected signs and the contents of past signs is calculated.
The method for proposing on-site diagnosis of a vehicle according to claim 6. The contents of the above-mentioned signs include a plurality of abnormal signals,
The importance of each of these abnormal signals is determined in advance.
Considering the importance of each signal, the degree of agreement between the contents of the detected signs and those of past signs is calculated.
The method for proposing on-site diagnosis of a vehicle according to claim 6. The contents of the above-mentioned signs include a plurality of abnormal signals,
When the feature amount of the abnormal signal is different for each of the plurality of components, the abnormal signal is determined as an abnormal signal having a high contribution to the abnormality classification;
The abnormal signal with the highest contribution is given priority, and the degree of agreement between the contents of the detected sign and the contents of past signs is calculated.
The method for proposing on-site diagnosis of a vehicle according to claim 6. A failure prediction unit that detects a sign of failure of a vehicle component;
a faulty part identifying unit that identifies one or more faulty part candidates based on the symptom;
an on-site diagnosis required time calculation unit that calculates a required time for on-site diagnosis of a candidate defective part;
an on-site diagnosis suggestion unit that suggests an on-site diagnosis to a user if the required time is within a predetermined time;
The vehicle on-site diagnosis proposal device comprises:

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