JP3274110B2 - Equipment deterioration diagnosis method and equipment deterioration diagnosis system - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

The present invention relates to a method and system for diagnosing equipment deterioration, and more particularly to a method and system for diagnosing equipment deterioration utilizing communication network technology.

[0002]

2. Description of the Related Art Conventional equipment deterioration diagnosis services for buildings (for example, condominiums) and their associated facilities require the renovation and renewal of facilities by building owners (residents of condominiums or management associations) (hereinafter referred to as users). In order to assist in gender judgment, equipment contractors are conducted in-house or commissioned as part of sales activities. Therefore, the user does not have sufficient confidence in the result of the equipment deterioration diagnosis which is a part of the business activity of the equipment contractor, and
In many cases, they have an impression that the diagnosis cost is expensive. In some cases, the equipment contractor outsources the diagnostic work to a third-party diagnostic company. Even in such a case,
Since the equipment company is a customer for the diagnostic company, it is also difficult to give the user a sense of trust based on the diagnostic results of such a diagnostic company.

[0003] On the other hand, equipment suppliers often regard diagnostic services as cost centers for securing profits that can be expected by repairing or updating equipment, rather than securing profits through diagnostic services. Therefore, the equipment contractor
Even if the company acknowledges the necessity of the diagnostic work itself as a business tool, it does not lead to actions that require investment, such as the introduction of new technologies or the increase of personnel.
As a result, there has been little progress in diagnostic work in the direction of improving user satisfaction.

[0004]

By the way, when the diagnosis service is regarded as a service, the ultimate need of the user who pays for the service is maintenance of the owned equipment (repair + diagnosis).
Cost (equipment maintenance cost). For this need, the user currently has two options: (A) The equipment is regularly renovated in anticipation of the safety factor without performing costly diagnosis. (B) Diagnosis is performed periodically to judge the life of the equipment, and the equipment is not repaired until the end of its life, thereby extending the repair interval.

The decision of which of the two options to choose depends on how much the diagnosis will cost and the effect of deferring the refurbishment due to the diagnosis. For example, it is assumed that, for equipment that has been renovated for a certain period of time without performing a diagnosis, the renovation interval can be lengthened by introducing a diagnosis. In this case, the cost that can be used for diagnosis depending on how long the repair interval can be made longer than the conventional interval (marginal diagnosis cost)
Is determined.

[0006] The marginal diagnosis cost C is given by the following equation as an ideal model.

Limit diagnosis cost C (yen / year) = (n−1) r / T (1) n: ratio of repair interval extended by diagnosis to conventional interval r: repair cost (yen) T: repair Interval (years) In the formula (1), when n = 1, that is, when the repair interval is not extended by the diagnosis, the marginal diagnosis cost C = 0, and it is meaningless to perform the diagnosis.

In the case where n = 1.5 in the equation (1),
That is, if the repair interval is extended by 1.5 times due to the diagnosis, the marginal diagnosis cost C = 0.5r / T. Therefore, if the diagnosis cost is less than half of the amortization cost (r / T), the diagnosis will reduce the equipment maintenance cost, and the diagnosis will be effective.

Further, in the equation (1), when n = 2, that is, when the repair interval is doubled by the diagnosis,
The marginal diagnosis cost C = r / T. In this case, if the diagnosis cost is the amortization cost (r / T), the diagnosis reduces the equipment maintenance cost, and thus has the effect of performing the diagnosis.

As described above, if the diagnosis can be provided at less than the marginal diagnosis cost C given by the equation (1), there is a benefit to the user in performing the diagnosis. Therefore, by minimizing the total cost of diagnostics performed during rehabilitation, equipment maintenance costs are better when performing diagnostics and extending the renovation interval than when performing periodic renovations without performing diagnostics. The likelihood of being lowered increases. In order to reduce the total cost of diagnosis, in addition to reducing the cost of one diagnosis, it is necessary to reduce the number of diagnoses during renovation, that is, make the diagnostic interval as long as possible.

In order to extend the diagnosis interval, it is required to improve the accuracy of the prediction of the remaining life of the equipment by the diagnosis in order to make the remaining life of the equipment predicted by the diagnosis as long as possible.

Accordingly, an object of the present invention is to provide an equipment deterioration diagnosis method and equipment deterioration diagnosis system which can predict the remaining life of equipment with high accuracy and which is inexpensive.

[0013]

According to the present invention, a large amount of data such as a history of measurement data of a facility to be diagnosed and attribute data of the facility is acquired and the data is efficiently collected. By managing and processing, a highly accurate deterioration diagnosis method is provided.

For this purpose, a server for centrally managing and processing the large amount of data is provided. A measuring device brought to the site of the equipment to be diagnosed is connected to the server via a communication network, and the measured data is transferred to the server. And receives the diagnostic result from the server. As described above, by using the communication network, a diagnosis result can be obtained in real time at the time of a diagnosis operation, and the diagnosis operation can be simplified, so that the cost can be reduced.

It is preferable that the diagnostic work is performed while the reliability of the measurement data is determined in real time by the server. As a result, measurement errors can be eliminated, and necessary measurement data can be reliably obtained, so that highly accurate deterioration diagnosis can be realized.

Preferably, in order to achieve the above object, a method for diagnosing equipment deterioration according to the present invention comprises the steps of: measuring equipment to be diagnosed by a measuring device, and acquiring measurement data relating to deterioration of the equipment to be diagnosed; Transmitting data from the measurement device to the server via the communication line, processing the received measurement data in the server, performing a deterioration diagnosis of the equipment to be diagnosed, and providing a diagnosis result. It is characterized by.

Preferably, the present invention further comprises a step of, in the server, judging the reliability of the received measurement data, and a step of transmitting the judgment result to the measuring device via a communication line. I do.

Further, the present invention includes a step of storing the measurement data in a server, and the execution step predicts a remaining life of the equipment to be diagnosed based on a comparison of the measurement data at different measurement times. I do. Further, the present invention includes a step of storing the attribute data of the equipment to be diagnosed in the server, and the executing step predicts the remaining life of the equipment to be diagnosed based on the comparison of the measurement data at different measurement times and the attribute data. It is characterized by doing.

Further, the present invention is characterized in that it comprises a step of storing the attribute data of the equipment to be diagnosed in the server, and the execution step predicts a remaining life of the equipment to be diagnosed based on the attribute data.

Further, the equipment deterioration diagnosis system of the present invention for achieving the above object measures the equipment to be diagnosed,
A measuring device for acquiring measurement data related to deterioration of the equipment to be diagnosed, and a server connected to the measuring device via a communication line, the measuring device transmits the measurement data to the server, and the server receives the measurement data. And executing a deterioration diagnosis of the equipment to be diagnosed.

[0021]

Embodiments of the present invention will be described below. However, the technical scope of the present invention is not limited to the present embodiment.

In the present embodiment, a method of diagnosing equipment deterioration using a network is provided. In the present embodiment,
First, a method for predicting the remaining life of equipment with high accuracy in the equipment deterioration diagnosis method of the present invention will be described below.

FIG. 1 is a diagram showing the relationship between the remaining life prediction and its accuracy. Generally, the remaining life in diagnosis is as follows:
It is considered that one pattern can be predicted with high accuracy. (A) When the equipment is severely deteriorated and a fatal problem is expected to occur in a very short period of time (in an extreme case, if the piping has already been pierced, the judgment that the remaining life is zero is almost 100%. (B) If deterioration of the equipment is hardly recognized and it seems to be OK for the time being (for example, if no deterioration is seen, it is OK for several years because it is almost a new construction state) It can be considered that it is difficult to increase the accuracy of the remaining life prediction in the middle of the above two patterns (deterioration is recognized). This is because the rate at which deterioration progresses to the point where renovation is required
This is because it is considered to be intricately related to the environmental factors around the building.

Therefore, the accuracy of the remaining life prediction is conceptually expressed as
It is assumed that it follows a bathtub-like characteristic as shown in FIG.
Can be In FIG. 1, a period T_SIs its accuracy
Exceeds the allowable level (th) by the pattern (a).
It is the longest value of the life and the period T _LIs the accuracy of the pattern
The shortest service life that exceeds the allowable level (th) due to the
Value. Hereinafter, period T_SThe remaining life up to the short life expectancy,
Period T_LThe remaining life after that is called the long life.

FIG. 2 is a diagram for explaining a diagnostic interval between repairs. First, when the remaining life of the area of the pattern (b) in FIG. 1 is predicted, the predicted life is equal to the allowable level (th).
With the above accuracy (reliability), the next diagnosis is
It may be performed after the period T _L. Setting the diagnostic interval to a shorter period will only increase diagnostic costs. Accordingly, the diagnosis interval in the early stage of the renovation is sufficient for the period T _L.

On the other hand, when the estimated remaining life is shorter than the period T _L , the accuracy of the estimation is lower than the allowable level, so that the diagnosis needs to be performed frequently with a narrower diagnosis interval. The diagnosis frequency is eventually the interval of the period T _S in the region of the pattern (a) where the prediction accuracy is equal to or higher than the allowable level th. This is because even if the remaining life between the periods T _L and T _S is predicted,
This is because the predicted life cannot be trusted.

As described above, even when the diagnosis is performed during the repair, the diagnosis interval is adjusted based on the reliability (accuracy) of the expected life, instead of performing the diagnosis periodically as in the related art. Thereby, the diagnosis interval can be optimized without lowering the diagnosis accuracy. Then, at this time, the period T _S
And T _L as long as possible, it is possible to further reduce the number of diagnoses. To do so, the period T _S
Therefore, it is necessary to improve the accuracy of the predicted life so that T _L becomes longer.

FIG. 3 is a diagram for explaining a pattern for improving the accuracy of the predicted life. In order to further reduce the number of diagnoses between repairs, as shown in FIG. 3A, it is necessary to improve the prediction accuracy so that the periods T _S and T _L become longer as compared with the accuracy of the conventional life expectancy. There is. On the other hand, as shown in FIG. 3B, a pattern in which the lengths of the periods T _S and T _L do not change and the accuracy of the intermediate period is improved does not contribute to a reduction in the number of diagnoses. The accuracy is still below the acceptable level th and cannot be trusted. In this way, when aiming to improve the accuracy of the predicted life, the period T _S
It is indispensable to improve the accuracy of short-term remaining life prediction based on _TL and the accuracy of long-term remaining life prediction based on period _TL .

Next, a method for improving the accuracy of short-term remaining life prediction and a method for improving long-term remaining life prediction accuracy will be described.

[Improvement of Short-Term Remaining Life Prediction Accuracy] If the equipment has already deteriorated to some extent, the short-term remaining life is predicted as described above. The short-term remaining life can be estimated from the state of deterioration and the speed of progress. In this case, a single diagnosis cannot determine the progressing speed of the deterioration, even if the state of the deterioration can be determined. Estimation of the speed of progress of deterioration requires a diagnosis history for the same location. Therefore,
In order to effectively use the diagnosis history of the same location, it is necessary to manage the following information in addition to the measurement data obtained by the diagnosis.

1. 1. Information on which location (measurement site) of the building the measurement data is (for example, a pipe shaft provided on the first floor of the building). 2. Accurate location information of each measurement site (for example, a section of 10 cm to 1 m from the elbow part of the pipe shaft) Information such as setting conditions in measurement (for example, the sensitivity of a measuring device) And, in addition to the above information, whether the measurement data is valid data that can be compared with past measurement data (for example, It is necessary to have information for determining whether the deterioration position has a certain degree of correlation with past measurement data). Feedback of such information at the time of measurement is indispensable for maintaining data reliability.

The above three pieces of information are stored in a database in a system for executing the equipment deterioration diagnosis method of the present invention, which will be described later, and are used via a network at the time of measurement. Then, among the three pieces of information, 1. And 2. It is preferable to be able to easily identify the actual measurement location corresponding to the information stored in the database.

As a means for easily distinguishing the measurement site, for example, an identification label uniquely corresponding to the measurement site may be provided.
It is conceivable to attach it directly to the measurement site of equipment such as piping. The measurer goes to the measurement site based on the information on the approximate diagnosis location in the building. Then, by finding an identification label in the vicinity, the measurement site can be easily specified.

The identification label is provided with, for example, an ID number or a barcode. The ID number can be input from a keyboard of a portable terminal connected to the database via a network, or a barcode can be input by a barcode reader connected to the portable terminal. , It becomes possible to read out past measurement data at the measurement site from the database. In addition, information that is difficult to describe in a database, such as a measurement direction, can be displayed on the identification label.

FIG. 4 is a schematic view of a barcode label sheet. As shown in FIG.
Is prepared in advance, and the measurer brings it to the measurement site. Then, at the time of the first diagnosis, the measurer reads the barcode label 1 on the sheet 2 with a barcode reader for each measurement site, peels off the barcode label 1, and attaches the barcode label 1 to a predetermined position on the measurement site. Then, an ID corresponding to the read barcode is assigned to the measurement site and registered in the database. From the next diagnosis, at the measurement site, a label 1 attached to the equipment with a barcode reader
The information corresponding to the corresponding measurement site is extracted from the database by reading the barcode of the above or inputting the ID corresponding to the barcode of the label from the terminal.

As described above, in order to improve the accuracy of the short-term remaining life prediction, it is necessary to acquire effective measurement data that can be compared with past measurement data in order to accurately estimate the progression rate of deterioration. In the present invention, since the measurement site can be accurately specified by the barcode label 1, the same measurement site can be measured without errors in diagnosis at different times.

[Improvement of Long-Term Remaining Life Prediction Accuracy] With respect to long-term remaining life prediction, as described above, it is a prediction of how long it can be safely used in a state where there is almost no deterioration. Therefore, in order to predict the long life expectancy, not the measurement data but the attribute data of the facility of the building is necessary. Then, from the accumulated attribute data, data matching the attribute of the actual equipment to be diagnosed is extracted, and the long-term remaining life is estimated based on the data. Table 1 below shows an example of equipment attribute data.

[0038]

[Table 1] It is important to accumulate as much attribute data of the equipment as possible in order to improve the accuracy of long-term remaining life prediction estimated based on the attribute data. For this purpose, in the equipment deterioration diagnosis method of the present invention, attribute data input from individual measurement devices brought to the measurement site is centrally managed in a database in a server, and the individual measurement devices communicate with the database via a network. Accessible. The attribute data shown in Table 1 is accumulated for many types of buildings and has a huge data amount. Therefore, storing such a huge amount of attribute data in each measuring device is inefficient and leads to an increase in the size of the entire measuring device. On the other hand, by connecting the database storing the attribute data and the individual measuring devices via the network, the individual measuring devices can use the attribute data even if they do not accumulate the attribute data themselves, Individual measuring devices can be miniaturized.

Further, the accumulated attribute data can be used as auxiliary information for the estimated short-term life expectancy, as described later.

[System Configuration] FIG. 5 is a block diagram of the equipment deterioration diagnosis system according to the embodiment of the present invention. In FIG. 5, a server 10 and a portable information processing terminal (hereinafter, referred to as a portable terminal) 21 in the measuring device 20 are connected via a communication network. The mobile terminal 21 is, for example, a notebook computer. The mobile terminal 21 includes a sensor device 22 for measurement, and a barcode reader 2 for reading a barcode label attached to the equipment to be diagnosed.
3. A digital camera 24 for taking a picture of the site, a printer 25 for outputting a diagnosis result, and the like are connected. The digital camera may be built in the portable terminal 21. The sensor device 22 differs depending on the type of equipment to be diagnosed.
In the embodiment of the present invention, as an example, a sensor device 22 for detecting corrosion of piping equipment of a building using an eddy current flaw detection technique.
Is exemplified. Eddy current flaw detection technology detects flaws, cracks, corrosion, etc. inside the test object by detecting the interaction between the magnetic flux generated by the excitation coil and the test object as a change in eddy current induced by the magnetic flux. Is the way.

FIG. 6 is a view showing the principle configuration of the sensor device 22 according to the embodiment of the present invention. 6, the sensor device 22 includes an excitation coil 221 wound around a pipe.
a, a detection coil 221b, a reference coil 221c, and a drive processing circuit 222. When an alternating current i flows from the drive processing circuit 222 to the exciting coil 221a, the exciting coil 2
A magnetic flux f is generated from 21a. The magnetic flux f is detected by the detection coil 2
Induced currents is1 and is2 are induced in 21b and reference coil 221c, respectively. Further, the magnetic flux f induces an eddy current ie inside the pipe. Note that the detection coil 221b and the reference coil 221c are connected in series such that the induced currents is1 and is2 cancel each other out. Therefore, for example, when the magnitude of the magnetic flux f on the detection coil 221b side is the same as the magnitude of the magnetic flux f on the reference coil 221c side, no measurement signal is output.

On the other hand, in the range where the magnetic flux f penetrates into the pipe,
If there is a flaw or corrosion, the eddy current ie changes. Then, the magnetic flux due to the change of the eddy current ie changes the induced currents is1 and is2 of the detection coil 221b and the reference coil 221c. Therefore, the induced current is1 and the induced current i
The balance of the magnitude of s2 is lost, and a measurement signal having a predetermined voltage level is output. Thus, based on the voltage level of the measurement signal, it is possible to detect the position where the flaw or corrosion exists inside the pipe, and the degree (depth, width, etc.) thereof.

Conventionally, such a sensor device for eddy current detection requires an operation for winding a coil around a pipe, and the operation process is complicated. For this reason, it has not been widely used as a facility deterioration diagnosis for buildings. However, the applicant of the present invention has developed a small eddy current flaw detection sensor device provided with a coil element that does not require winding work on site (see Japanese Patent Application No. 10-189149). Therefore, by incorporating this sensor device into the equipment deterioration diagnosis method of the present invention, it is possible to provide easy and low-cost diagnosis.

In FIG. 6, the barcode label 1 attached to the pipe may have a measurement direction (arrow) other than the barcode. Bar code label 1
For example, it is pasted at the scanning start position, and the scanning direction is entered in the blank space. As a result, even when a different measurer performs measurement for each diagnosis, the measurement location is not mistaken. Therefore, the reliability of the measurement data increases. The scan direction is entered by hand by the measurer.

Returning to FIG. 5, the server 10 has a diagnostic software execution unit 11, a macro analysis execution unit 12, and a database 13. The diagnostic software execution unit 11 and the macro analysis execution unit 12 are a storage device that stores a diagnostic software and a macro analysis program that are programs for executing the equipment deterioration diagnosis method according to the present invention, and a control that executes each software (program). Equipment (CPU)
It is composed of Further, the database 13 stores each attribute data of the building to be diagnosed, past measurement data (diagnosis history), and the like.

FIG. 7 is a diagram showing the structure of the database 13. In FIG. 7, the database 13 has a two-layer structure of building level information and measurement site level information. In the building level information, for each building ID, various building information such as a floor plan, an address, a building age and a person in charge of the building, and the names of the sites (site names 1 and 2) to be diagnosed are given. The site name is given a site ID corresponding to the site name, for example, as a barcode.

Then, in the measurement site level information, the site I
For each D, various site information such as photograph data of the site, piping type, repair information, and the like, and a past deterioration diagnosis result as a diagnosis history are given. The deterioration diagnosis result is, for example, a predicted remaining life or analysis data described later.

FIG. 8 is a processing flowchart of the equipment deterioration diagnosis method according to the embodiment of the present invention. FIG.
14 to 14 are examples of screens displayed on the portable terminal 21 in accordance with the execution of the processing flowchart of FIG. FIG. 8 will be described with reference to FIGS. 9 to 14.

The following processing is started and executed by the diagnostic software and the macro analysis program stored in the server 10 connected to the mobile terminal 21 via the network by the operation of the mobile terminal 21 by the measurer. Is done. Further, data input by operation of the mobile terminal 21 and measurement data from the sensor device 22 are also transmitted to the server 10 via the network, and the server 10 executes processing. In each step of the processing, the data read from the database 13 and the processing results of the diagnostic software and the macro analysis program are stored in the portable terminal 2.
1 and displayed on the screen.

The measurer first starts the diagnostic software. FIG. 9 is an example of a startup screen of the diagnostic software. When the screen of FIG. 9 is displayed, the measurer inputs his / her ID (PIN) from the keyboard of the portable terminal 21 in step S10 of FIG. The measurer ID is transmitted to the server 10 via the network, and the server 1
0 performs ID collation. If the ID is correct, the screen shown in FIG. 10 is displayed.

FIG. 10 is an example of a building list screen. The building list displays the names, addresses, inspection dates, IDs, and the like of the buildings that have been diagnosed in the past. In the case of the second and subsequent diagnoses, the buildings to be diagnosed this time are listed. The measurer selects a building column to be diagnosed with a cursor, a pointer, or the like (step S11). Then, when the update button is clicked, a building information screen of the selected building is displayed. FIG. 11 shows an example of the building information screen. FIG.
In each column of 1, data input at the time of past diagnosis is displayed. If there is an additional item to be input for the building information, the measurer inputs it (step S1).
2).

On the other hand, in the case of the first diagnosis, since the building to be diagnosed this time is not listed, a new button on the screen of FIG. 10 is clicked. As a result, a screen (the screen in FIG. 11) of the building information in which each column in FIG. 11 is blank is displayed. Then, in step S11 in FIG. 8, the measurer inputs predetermined items for the building information. In addition,
The building ID is automatically assigned and displayed in a corresponding column.

The building information screen has a site list column (enclosed by a dotted line) 111. The site list column 111 lists past measurement sites in the building. Therefore, in the case of the second and subsequent diagnoses, the field of the measurement site to be diagnosed is selected with a cursor or a pointer (step S13). Then, when the update button is clicked, a site information screen of the selected measurement site is displayed.

FIG. 12 shows an example of a site information screen. FIG.
In each column, data input during the past diagnosis is displayed. The site information screen includes a signal waveform column 12 for displaying a signal waveform (measurement data) measured by the measuring device 20.
1. It has a field attribute field for displaying field attributes, a measurement condition field for displaying measurement conditions in past diagnosis, and the like. The measurer can measure while visually checking the signal waveform displayed in the signal waveform column 121. In addition, by performing measurement under the same measurement conditions as the contents of the measurement condition column, it is possible to achieve consistency with past measurement data and obtain effective measurement data that can be compared with past measurement data.
If you have additional fields to enter for site information,
The measurer inputs it (step S14).

On the other hand, in the case of the first diagnosis, since the site to be diagnosed this time is not listed, a new button in the site list column 111 in FIG. 11 is clicked. As a result, a site information screen in which each column in FIG. 12 is blank is displayed. Then, in step S12 of FIG. 8, the measurer inputs predetermined items for the site information. Also,
The site ID is given by the barcode label attached to the measurement site as described above. When the barcode is read by the barcode reader 23, the site ID is displayed in the ID column of the site information screen.

FIG. 13 shows an example of a search screen for searching for building information and site information. The search screen is shown in FIG.
Is displayed by clicking the search button on the building list screen of the above. On the search screen, the measurer inputs a keyword for building information and a keyword for site information, and clicks an execution button to execute a search.

FIG. 14 is an example of a search result screen. The search result screen displays a list of the IDs and names of the found buildings and the IDs and names of the found sites. Then, the measurer selects and clicks the column in which the ID or name of the desired building or site is displayed, and clicks.
The building information screen of FIG. 11 or the site information screen of FIG. 12 is displayed.

Returning to FIG. 8, after the site information screen is displayed, measurement is performed using the sensor device 22 in step S15. The measurer scans the eddy current flaw detection probe (coil) wound around the pipe by a predetermined distance in the longitudinal direction of the pipe. The measurement signal waveform from the sensor device 22 is monitored by the signal waveform display column 12-1 on the site information screen in FIG. After the scan, the measurer clicks the capture button, and the signal waveform displayed at that time is captured. Then, by clicking the update button, the acquired signal waveform data (measurement data) is transmitted to the server 10 via the network, and the signal waveform column 121 is cleared.

Then, in step S16, a reliability determination is made as to whether or not the transmitted signal waveform data is reliable data. As described above, in short-term remaining life prediction, it is necessary to acquire effective measurement data that can be compared with past data. In general, the signal waveform changes over time due to the progress of equipment deterioration,
The waveform does not change to a completely different shape. Therefore,
If the measured signal waveform is similar to the past signal waveform, the signal waveform data can be determined as reliable data. On the other hand, if they are not similar, it can be determined that the data is not reliable due to a measurement error.

For example, since the measurement is performed by manual scanning, the time axis (X axis) of the measurement data does not always correspond to the scanning distance of the pipe with high accuracy. It is assumed that the scan speed differs depending on the measurer, and that the speed changes during the scan.

FIG. 15 is a diagram showing an example of signals having different scan speeds. As shown, the positions of the peaks 1, 2 and 3 corresponding to the corroded portions in the signals 1 and 2, respectively, are shifted with respect to the time axis (X axis) due to the difference in the scanning speed.

Therefore, when performing the reliability judgment, it is necessary to normalize the time axis (X axis) of the transmitted signal waveform data. Normalization refers to a process of associating each peak point of a plurality of data at different measurement times. Such a process is a method generally used as a process for matching with a standard pattern while removing the influence of the speaking speed of the speaker in the speech recognition technology. In the present embodiment, a similar method is used. Can be used.

Then, the signal waveform data in the past measurement stored in the database 13 of the server 10 and
If the signal waveform data transmitted this time has not been sufficiently normalized, the server 10 determines that the data is unreliable and prompts for another measurement. If the data is sufficiently normalized, it is determined that the data is reliable, and the diagnostic processing in step S17 is executed based on the signal waveform data.

When the diagnosis is performed for the first time, the measurer performs, for example, measurements on the same site a plurality of times (for example, three times). The signal waveform data in each measurement is stored in the server 1
Sent to 0. The server 10 performs a normalization process on a plurality of signal waveform data for a certain site, and when sufficiently normalized, determines the measured signal waveform data as reliable data, and based on the signal waveform data. Then, the diagnostic processing in step S17 is executed. On the other hand, if the data has not been sufficiently normalized, the server 10 prompts another measurement, and the measurer continues the measurement until reliable data is obtained.

In the diagnostic processing in step S17, the transmitted signal waveform data is analyzed. Examples of data analysis items are listed below.

A) The total number of peaks estimated to be corrosion (pieces /
m) Thereby, the degree of corrosion of the entire pipe can be determined to some extent.

B) Distribution of Peaks Presumed to be Corrosion From this, the distribution of corrosion is known.

C) Analysis of each extracted peak The magnitude (V), phase (degree),
The degree of corrosion of each corroded portion can be determined from the width (m) and the like.

D) Parametric analysis of measured data (for example, Fourier analysis) By this, the density of corrosion and the classification of corrosion (such as pitting or erosion) can be roughly determined.

Further, the analysis data obtained by the above analysis is compared with the analysis data based on the past signal waveform data. By comparing the analysis data, it is possible to estimate the change state and progress rate of corrosion.

FIG. 16 is a diagram for explaining a comparative example of analysis data. The horizontal axis (X-axis) of each graph is time. FIG. 16A is a graph showing the signal intensity of the peak corresponding to the corroded portions 1, 2, and 3. The signal strength corresponds to the depth of corrosion. Therefore, according to FIG. 16A, it can be seen that the corroded portion 1 advances in the depth direction faster than the corroded portions 2 and 3. It can also be seen from the figure that the depth of the corroded portion 3 has hardly changed. FIG. 16 (b)
It is a graph which shows the signal width of the peak corresponding to the corrosion parts 1, 2, and 3. The signal width corresponds to the area of corrosion. Therefore,
According to FIG. 16 (b), although the areas of the corroded portions 1 and 2 hardly change, it can be seen that the corroded portion 3 gradually spreads. FIG. 16C is a graph showing the peak density, and the signal intensity of the peak is classified into, for example, three levels (large, medium, and small). According to FIG. 16 (c),
Although the number of corroded portions is increasing year by year, it can be seen that most of the corroded portions have low signal intensity and shallow corrosion.

FIG. 17 is a diagram for explaining a comparative example of signal waveform data. In FIG. 17A, when the signal waveform in the previous diagnosis and the signal waveform in the current diagnosis are compared, the peak width is widened without changing the peak intensity. Therefore, it can be seen that the corrosion corresponding to this peak has not progressed in the depth direction, but its area has expanded. Such corrosion reduces the thickness of the piping,
It is not fatal corrosion that causes holes in piping. Also,
In FIG. 17B, the peak width has not changed, but the peak intensity has increased. Therefore, it can be seen that the corrosion corresponding to this peak does not spread in area but proceeds in the depth direction. If such corrosion proceeds as it is, there is a risk of drilling holes in the piping,
Dangerous corrosion. Further, in FIG. 17C, both the peak width and the peak intensity are large. Therefore, it can be seen that the area of the corrosion corresponding to this peak has become wider and the depth has also become deeper. Such corrosion is the most dangerous corrosion because if it proceeds as it is, a large hole may be formed in the pipe.

As described above, the analysis and comparison (diagnosis processing) of the signal waveform data by the diagnostic software in step S17 in FIG.
The remaining life of the piping equipment at the site to be diagnosed is estimated from the change state. The remaining life is determined from the progression rate of corrosion determined by the above diagnosis, for example, as a period until the thickness of the pipe disappears and a hole is formed, or a period until the thickness decreases to the lower limit thickness.

Then, in step S18 in FIG.
The diagnosis result (change state, estimated remaining life, etc.) is further analyzed macroscopically using the attribute data of the equipment shown in Table 1 above. For example, when the amount of water flowing through the pipe is relatively large or when the flow velocity is high, the load on the pipe is large, so that corrosion is assumed to progress relatively quickly. Therefore, in such a case, a correction is made to slightly shorten the remaining life estimated based on the diagnosis result. That is, by correcting the estimated remaining life calculated by the diagnostic software based on various attribute data of the piping equipment by macro analysis, the remaining life can be estimated with higher accuracy. In this macro analysis, since the above-described technique of long-term remaining life prediction is used, when the remaining life obtained from the diagnosis result is relatively long (for example, several years), the estimation accuracy is further improved. On the other hand, when the estimated remaining life is relatively short (for example, several months), the accuracy of the estimated remaining life based on the diagnosis result is sufficiently high, so that the macro analysis may be omitted.

In step S19 of FIG. 8, the diagnosis result of step S17 or S18 is
Is printed out from As described above, according to the present invention, it is possible to provide a diagnosis result in real time at the measurement site.
Therefore, the measurer can explain the diagnosis result to the diagnosis requester on the spot. This is because the mobile terminal 21 placed at the measurement site is connected to the server 1 via the network.
0 and the server 10 processes the measurement data in real time. As described above, processing of measurement data requires server functions such as execution of diagnostic software and search of a database.
If a network is not used, server functions must be brought to the measurement site. It is practically impossible for the mobile terminal 21 to execute such a function.
As described above, by using the network, the analysis processing of the measurement data can be performed in real time.

The output of the diagnosis result is not limited to the print output from the printer, but may be, for example, a screen display of a portable terminal. Further, the diagnosis result may be transmitted from the measurement device 20 to another terminal remote from the measurement device 20. For example, it may be transmitted to a mail address of a terminal (for example, a personal computer) owned by the user who has requested the diagnosis. The diagnosis result is displayed on the screen of the terminal and printed out from a printer connected to the terminal. The diagnostic result may be printed out on the server 10 side and mailed to the user.

As described above, it is preferable to provide a plurality of options in the method of providing a diagnosis result, since the diagnosis result can be provided according to the circumstances of a diagnostic company or a user. Which method to use may be appropriately selected according to the circumstances of the diagnostic company and the user. For example, if the user does not need an immediate diagnosis result, the diagnosis result may be transmitted to a mail address of the user's personal computer (terminal) at a later date, or the diagnosis result may be mailed to the user. In addition, by displaying the diagnosis result on the screen of the mobile terminal 21, the general diagnosis result is immediately transmitted to the user, and further detailed analysis of the diagnosis result (for example, uniform analysis by the server is performed by human experience). Alternatively, a diagnostic result report that has been subjected to analysis or analysis may be created and transmitted or mailed again. Further, if the diagnostician wants to provide the user with a diagnosis result report in addition to the diagnosis result, a cover page containing the name of the company or a page containing other information, the diagnosis result report may be mailed or handed over.

When a network is used, whether or not the measured data is reliable data is determined in real time as described above, so that processing using the reliable data is guaranteed. You. However, when a network is not used, the reliability cannot be determined in real time. Therefore, the measurement data stored in the portable terminal 21 may be unreliable data due to a measurement error. If a measurement error is found later, another measurement is required. It is not efficient for a measurer to visit a measurement site many times for one diagnosis. in this case,
The measurer needs to visit the measurement site again for re-measurement. That is, since a lot of time for the measurer is restrained, labor costs are increased, and diagnostic costs are also increased.

As described above, according to the present invention, by performing the equipment deterioration diagnosis using the network, highly accurate diagnosis can be realized and the diagnosis cost can be reduced.

Although the equipment deterioration diagnosis method according to the embodiment of the present invention has been described by taking as an example the deterioration diagnosis of piping in a building, the invention is not limited to this. For example, the present invention is applicable to equipment that can be measured by a relatively simple sensor device, such as a fan coil in a building air conditioning system or concrete of a building. Furthermore, the equipment deterioration diagnosis method according to the embodiment of the present invention is not limited to a building, and can be applied to, for example, a building such as a bridge.

The piping in the present embodiment includes both a copper pipe which is a non-magnetic substance and a steel pipe which is a magnetic substance.

The scope of protection of the present invention is not limited to the above embodiments, but extends to the inventions described in the claims and their equivalents.

[0083]

As described above, according to the present invention, a large amount of data such as a history of measurement data of the equipment to be diagnosed and attribute data of the equipment is acquired and efficiently managed and processed, thereby achieving high accuracy. A degradation diagnosis method is provided.

For this purpose, a server for centrally managing and processing the large amount of data is provided. A measuring device brought to the site of the equipment to be diagnosed is connected to the server via a communication network, and the measured data is transferred to the server. And receives the diagnostic result from the server. As described above, by using the communication network, a diagnosis result can be obtained in real time at the time of a diagnosis operation, and the diagnosis operation can be simplified, so that the cost can be reduced.

Further, by performing a diagnostic operation while determining whether or not the reliability of the measurement data is determined by the server, measurement errors can be eliminated, and necessary measurement data can be obtained reliably.

[Brief description of the drawings]

FIG. 1 is a diagram showing the relationship between remaining life prediction and its accuracy.

FIG. 2 is a diagram for explaining a diagnostic interval between repairs.

FIG. 3 is a diagram illustrating a pattern for improving the accuracy of predicted life.

FIG. 4 is a schematic diagram of a barcode label sheet.

FIG. 5 is a block configuration diagram of a diagnostic system according to the embodiment of the present invention.

FIG. 6 is a principle configuration diagram of a sensor device 22 according to the embodiment of the present invention.

FIG. 7 is a diagram showing a structure of a database 13;

FIG. 8 is a processing flowchart of an equipment deterioration diagnosis method according to the embodiment of the present invention.

FIG. 9 is an example of a startup screen of the diagnostic software.

FIG. 10 is an example of a building list screen.

FIG. 11 is an example of a building information screen.

FIG. 12 is an example of a site information screen.

FIG. 13 is an example of a search screen.

FIG. 14 is an example of a search result screen.

FIG. 15 is a diagram illustrating an example of signals having different scan speeds.

FIG. 16 is a diagram for explaining a comparative example of analysis data.

FIG. 17 is a diagram illustrating a comparative example of signal waveform data.

[Explanation of symbols]

 Reference Signs List 10 server 11 diagnostic software execution unit 12 macro analysis execution unit 13 database 20 measuring device 21 mobile terminal 22 sensor device 25 printer

──────────────────────────────────────────────────続 き Continuation of the front page (56) References JP-A-11-259123 (JP, A) JP-A-11-64295 (JP, A) JP-A-2-4300 (JP, A) Patent 3247666 (JP, A B2) Simple Copper Pipe Inspection Equipment, 1994 Autumn Meeting Abstracts, Japan, Japan Non-Destructive Inspection Association, October 1, 1994, p. 147-p.150 Equipment Diagnosis System Utilizing Internet, Construction Work and Plumbing Work, Japan, Nippon Kogyo Publishing, October 1, 2000, Vol. 38, No. 11, p. 54-p. 58 (58) Fields surveyed (Int. Cl. ⁷ , DB name) G01N 27/90 G05B 23/02 G06F 17/40 JICST file (JOIS)

Claims (8)

(57) [Claims] 1. A facility degradation diagnosis method for diagnosing the deterioration of the equipment piping, a coil wound on the pipe, driving for driving the coil
Having a dynamic processing circuit and a mobile terminal communicating with the server
By a flow flaw detector, manually scanning the coil,
Of the pipe obtained by measuring the state of the pipe
Communication of measurement data on deterioration from the eddy current flaw detector
A first step of receiving by the server via line Ri by the prior SL server, and the measurement data, the measurement data
A second step of determining the reliability of the measurement data based on the similarity between the measurement data and the past measurement data obtained by normalizing the time axis of the past measurement data having different measurement times. When the determination result in the second step, is transmitted from the server to the eddy-current flaw detection device via a communication line, said-format
If the result is unreliable, the eddy current flaw detector
A third step of prompting the measurement, at the server, and stores the measurement data it is determined that there is reliability by the second step, a fourth step of storing, in the server, by the second step Between measured data determined to be reliable and past measured data
Based on the comparison, detects a change in state of the pipe, the distribution
A facility deterioration diagnosis method, comprising: a fifth step of performing a pipe deterioration diagnosis; and a sixth step of providing a diagnosis result of the deterioration diagnosis. 2. A method according to claim 1, wherein in the sixth step, the diagnosis result, the eddy-current flaw detection device or via said communication line from the server Uzuryusagu
A method for diagnosing equipment deterioration, wherein the diagnosis result is transmitted to a predetermined terminal remote from the wound device, and the diagnosis result is output from the eddy current terminal device or the predetermined terminal. 3. The eddy current probe according to claim 1, wherein in the sixth step, the diagnosis result is obtained by the eddy current detection.
A method for diagnosing equipment deterioration, which is output from a selected one of a flaw device, a terminal remote from the eddy current flaw detection device, or the server. 4. The method according to claim 1 , wherein the measurement data is obtained from the deterioration diagnosis in the fifth step.
Data and the previous measurement data,
Also based on the attribute data of the piping stored in the server
Detecting a change state of the pipe.
Deterioration diagnosis method. 5. The equipment degradation diagnostic system for diagnosing a deterioration of equipment piping, a coil wound on said pipe, said
A drive processing circuit for driving the coil, and the drive processing circuit;
A mobile terminal to be connected, and manually scan the coil.
An eddy current flaw detector that measures the state of the pipe by acquiring the measurement data related to the deterioration of the pipe , and a server that receives the measurement data from the eddy current flaw detector via a communication line. When the server receives the measurement data, the measurement data and the similarity between the measurement data and the past measurement data obtained by normalization of the time axis of the past measurement data having a different measurement time from the measurement data are determined. On the basis of,
Judging means for judging the reliability of the measurement data, and the judgment result by the judging means,
Sent to the eddy current flaw detector, and the judgment result is reliable
If no, repeat the first and second steps
Transmitting means for prompting , storing means for storing and storing the measurement data determined to be reliable by the determination means, and measurement data determined to be reliable by the determination means
And, based on a comparison with past measurement data,
An equipment deterioration diagnosis system comprising: a deterioration diagnosis execution unit that detects a change state and executes deterioration diagnosis of the pipe . 6. The method of claim 5, wherein the transmitting means transmits from the degradation diagnosis of the diagnostic result the eddy-current flaw detection device end or the eddy-current flaw detection device via a communication line to a remote given terminal, the eddy current testing An equipment deterioration diagnosis system, wherein the diagnosis result is output from an apparatus or the predetermined terminal. 7. The method of claim 5, wherein the eddy-current flaw detection device, either from a selected one of the remote terminal or the server from the eddy current flaw detection apparatus, the poor
An equipment deterioration diagnosis system, wherein a diagnosis result of the chemical diagnosis is output. 8. The apparatus according to claim 5 , wherein said deterioration diagnosis execution means includes a step of:
Stored in the server in addition to the comparison with the measurement data of
Changes in the piping based on the attribute data of the piping
Equipment deterioration diagnosis system characterized by detecting the state
M