Abnormality diagnosis system

Abstract

To provide an abnormality diagnosis system capable of catching a change of hydraulic behavior caused by an abnormality and determining occurrence of an abnormality in a hydraulic control device.SOLUTION: In a storage device 120 of an abnormality diagnosis system 100, reference cluster data obtained by classifying data on differential pressure when a hydraulic control device 240 with no abnormality is operated into a specified number of clusters is stored. A processing circuit 110 of the abnormality diagnosis system 100 prepares data indicating transition of differential pressure of the hydraulic control device 240 that is an evaluation target as evaluation data. The processing circuit 110 checks the data on differential pressure included in the evaluation data against the centroid of each cluster in the reference cluster data, and prepares evaluation cluster data obtained by determining which cluster in the reference cluster data each data of the evaluation data belongs to. The processing circuit 110 determines occurrence of an abnormality in the hydraulic control device 240 on the basis of deviation of a determination result of the cluster.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an abnormality diagnosis system for a hydraulic control device. [Background technology]

[0002] Patent Document 1 discloses an abnormality diagnosis system that identifies the cause of an abnormality in the shift control of an automatic transmission. The abnormality diagnosis system in Patent Document 1 determines the cause of the abnormality in the automatic transmission based on the manner of transient changes in the rotation speed of the automatic transmission. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-085480 Summary of the Invention [Problem to be solved by the invention]

[0004] When making a judgment based on observing fluctuations in the rotational speed of an automatic transmission, it may be difficult to identify the cause of the abnormality. For example, it is difficult to determine a malfunction of a part in the hydraulic control device of an automatic transmission from fluctuations in the rotational speed. [Means for solving the problem]

[0005] The means for solving the above problems and their effects will be described below. An abnormality diagnosis system for solving the above problem includes a processing circuit and a storage device. The storage device of the abnormality diagnosis system stores reference cluster data obtained by classifying differential pressure data at multiple times, included in data on the transition of the differential pressure between the hydraulic pressure and the target hydraulic pressure when a hydraulic control device that is not experiencing an abnormality is operated according to a predetermined diagnostic pattern, into a predetermined number of clusters using clustering, a type of machine learning. The processing circuit of the abnormality diagnosis system creates evaluation data, which is data indicating the transition of the differential pressure between the hydraulic pressure and the target hydraulic pressure recorded while the hydraulic control device to be evaluated is operated according to the diagnostic pattern. The processing circuit compares the differential pressure data at multiple times included in the evaluation data with the centers of gravity of each cluster in the reference cluster data, and creates evaluation cluster data by determining to which of the predetermined number of clusters in the reference cluster data each piece of data at multiple times in the evaluation data belongs. The processing circuit determines that an abnormality has occurred in the hydraulic control device based on a discrepancy between the cluster determination results in the reference cluster data and the evaluation cluster data. [Effects of the Invention]

[0006] The abnormality diagnosis system described above makes a judgment using data on fluctuations in the hydraulic pressure of the hydraulic control device, and therefore can detect changes in the behavior of the hydraulic pressure due to an abnormality and determine whether an abnormality has occurred in the hydraulic control device. [Brief explanation of the drawings]

[0007] [Figure 1] FIG. 1 is a schematic diagram showing an embodiment of an abnormality diagnosis system and a hydraulic control device to be diagnosed. [Figure 2] FIG. 2 is a schematic diagram showing the configuration of the hydraulic control device. [Figure 3] FIG. 3 is a graph showing an example of a diagnostic pattern. [Figure 4] FIG. 4 is a graph showing an example of clustering. [Figure 5] FIG. 5 is a flowchart showing the flow of processing executed by the abnormality diagnosis system. [Figure 6] FIG. 6 is a table showing the clustering results. [Figure 7] FIG. 7 is a table showing the relationship between the clustering results and the type of anomaly. DETAILED DESCRIPTION OF THE INVENTION

[0008] An embodiment of the abnormality diagnosis system 100 will be described below with reference to FIGS. <Configuration of Abnormality Diagnosis System 100> Fig. 1 shows an abnormality diagnosis system 100 and an automatic transmission 200 including a hydraulic control device 240 to be diagnosed. When diagnosing the hydraulic control device 240, the abnormality diagnosis system 100 is connected to the automatic transmission 200 as shown in Fig. 1. The abnormality diagnosis system 100 diagnoses the hydraulic control device 240 by operating the hydraulic control device 240 in a predetermined diagnostic pattern.

[0009] 1, the abnormality diagnosis system 100 includes a processing circuit 110, a storage device 120, and a display 130. The processing circuit 110 includes a CPU that executes processing according to a program, and a ROM in which the program is stored. The storage device 120 stores data. The abnormality diagnosis system 100 is, for example, a personal computer or a workstation.

[0010] <Configuration of automatic transmission 200> An automatic transmission 200 including a hydraulic control device 240 to be inspected is mounted on, for example, an automobile. FIG. 1 illustrates a continuously variable transmission as an example of the automatic transmission 200. The automatic transmission 200 includes a first pulley 210, a second pulley 220, and a belt 230 wound around the first pulley 210 and the second pulley 220. The first pulley 210 is connected to an input shaft 211. The second pulley 220 is connected to an output shaft 221. The automatic transmission 200 transmits driving force via the belt 230. In the automatic transmission 200, the hydraulic control device 240 controls a first hydraulic pressure Pp supplied to the first pulley 210 and a second hydraulic pressure Ps supplied to the second pulley 220. This changes the winding radii of the belt 230 around the first pulley 210 and the second pulley 220. In other words, the hydraulic control device 240 changes the gear ratio of the automatic transmission 200 by changing the winding radius of the belt 230 .

[0011] When diagnosing the hydraulic control device 240, a drive motor 300 is connected to the input shaft 211, and a load motor 400 is connected to the output shaft 221. A plurality of hydraulic sensors 500 that detect hydraulic pressure at various parts are connected to the hydraulic control device 240. Diagnosis of the hydraulic control device 240 is performed, for example, as a pre-shipment inspection after the automatic transmission 200 is manufactured. The drive motor 300 reproduces the input of driving force from the driving force source of the automobile to the automatic transmission 200. The load motor 400 reproduces the load acting on the automatic transmission 200.

[0012] <Configuration of hydraulic control device 240> 2, the hydraulic control device 240 includes a regulator valve 241, a modulator valve 242, a first control valve 247, and a second control valve 248. The hydraulic control device 240 also includes a first linear solenoid valve 243, a second linear solenoid valve 244, a first hydraulic damper 245, and a second hydraulic damper 246.

[0013] The regulator valve 241 adjusts the hydraulic pressure of the oil supplied from the oil pump. The hydraulic pressure adjusted by the regulator valve 241 is the line pressure PL. The oil adjusted to the line pressure PL is supplied to a first control valve 247 and a second control valve 248. The hydraulic pressure adjusted to the line pressure PL is also supplied to a modulator valve 242.

[0014] The first control valve 247 supplies oil adjusted to the line pressure PL to the first pulley 210. The first control valve 247 controls the amount of oil supplied to the first pulley 210. The first control valve 247 increases the first oil pressure Pp by supplying oil adjusted to the line pressure PL to the first pulley 210. The first control valve 247 controls the amount of oil discharged from the first pulley 210. The first control valve 247 decreases the first oil pressure Pp by discharging oil from the first pulley 210.

[0015] The second control valve 248 supplies oil adjusted to the line pressure PL to the second pulley 220. The second control valve 248 controls the amount of oil supplied to the second pulley 220. The second control valve 248 increases the second oil pressure Ps by supplying oil adjusted to the line pressure PL to the second pulley 220. The second control valve 248 also controls the amount of oil discharged from the second pulley 220. The second control valve 248 decreases the second oil pressure Ps by discharging oil from the second pulley 220.

[0016] Increasing the first hydraulic pressure Pp and decreasing the second hydraulic pressure Ps increases the winding radius of the belt 230 around the first pulley 210 and decreases the winding radius of the belt 230 around the second pulley 220. Increasing the second hydraulic pressure Ps and decreasing the first hydraulic pressure Pp increases the winding radius of the belt 230 around the second pulley 220 and decreases the winding radius of the belt 230 around the first pulley 210. In this manner, the hydraulic control device 240 changes the gear ratio of the automatic transmission 200.

[0017] The first control valve 247 and the second control valve 248 are each controlled by a signal oil pressure. The modulator valve 242 adjusts the line pressure PL to the signal oil pressure. The oil adjusted to the signal oil pressure is supplied to the first linear solenoid valve 243 and the second linear solenoid valve 244.

[0018] The first linear solenoid valve 243 controls the signal oil pressure supplied to the first control valve 247. The second linear solenoid valve 244 controls the signal oil pressure supplied to the second control valve 248. The signal oil pressure supplied to the first control valve 247 is controlled by opening and closing the first linear solenoid valve 243. The signal oil pressure supplied to the second control valve 248 is controlled by opening and closing the second linear solenoid valve 244.

[0019] A first hydraulic damper 245 is provided in the oil passage connecting the first linear solenoid valve 243 and the first control valve 247. A second hydraulic damper 246 is provided in the oil passage connecting the second linear solenoid valve 244 and the second control valve 248.

[0020] <Reference cluster data> Reference cluster data is stored in the storage device 120 of the abnormality diagnosis system 100. The reference cluster data is data resulting from classifying, into a predetermined number of clusters, differential pressure data at a plurality of times included in data on the transition of the differential pressure between the hydraulic pressure and the target hydraulic pressure when a hydraulic control device 240 having no abnormality is operated in a predetermined diagnostic pattern. To create the reference cluster data, the hydraulic pressure is measured while a hydraulic control device 240 having no abnormality is operated in a predetermined diagnostic pattern. For example, of the hydraulic pressures at various locations in the hydraulic control device 240, the line pressure PL, the first hydraulic pressure Pp, and the second hydraulic pressure Ps are measured.

[0021] 3 shows the transition of the target hydraulic pressure Ppt of the first hydraulic pressure Pp in a predetermined diagnostic pattern. The predetermined diagnostic pattern is defined by the target hydraulic pressure PLt of the line pressure PL, the target hydraulic pressure Ppt of the first hydraulic pressure Pp, and the target hydraulic pressure Pst of the second hydraulic pressure Ps. The predetermined diagnostic pattern is defined so that abnormalities in each part of the hydraulic control device 240 can be diagnosed from the transition of the measured hydraulic pressures by varying the line pressure PL, the first hydraulic pressure Pp, and the second hydraulic pressure Ps over a predetermined period. In the predetermined diagnostic pattern, the target hydraulic pressure PLt of the line pressure PL, the target hydraulic pressure Ppt of the first hydraulic pressure Pp, and the target hydraulic pressure Pst of the second hydraulic pressure Ps each have a different change pattern.

[0022] The actual hydraulic pressure fluctuates with a delay relative to fluctuations in the target hydraulic pressure. The actual hydraulic pressure may overshoot the target hydraulic pressure. The actual hydraulic pressure may not immediately converge to the target hydraulic pressure, but may fluctuate above and below the target hydraulic pressure until it converges to the target hydraulic pressure. Data showing the trend in the differential pressure between the target hydraulic pressure and the measured hydraulic pressure includes information on the delay, overshoot, and oscillation of the actual hydraulic pressure relative to the target hydraulic pressure. Such delay, overshoot, and oscillation also occur in a hydraulic control device 240 that is not experiencing any abnormalities. The reference cluster data is created by clustering, a machine learning method. For example, the k-means method is used as a clustering algorithm. The k-means method is a clustering algorithm that classifies data into a pre-specified number of clusters. The clustering algorithm is not limited to the k-means method.

[0023] By performing clustering, data at each time included in the data showing the transition of differential pressure can be classified into clusters of data with similar characteristics. Figure 4 is a graph showing an example of clustering data on differential pressure transitions at multiple times into three clusters using the k-means method, using two explanatory variables included in the data on differential pressure transitions. In the example shown in Figure 4, the differential pressure ΔPp of the first hydraulic pressure Pp and the differential pressure ΔPp of the second hydraulic pressure Ps are each used as explanatory variables. In Figure 4, each piece of differential pressure data at multiple times is represented by a white symbol.

[0024] In Figure 4, these differential pressure data are shown in a two-dimensional space with the differential pressure ΔPp and the differential pressure ΔPs as the coordinate axes. Figure 4 shows an example of differential pressure data at multiple times being clustered into three clusters. In Figure 4, the coordinates of data classified into the first cluster are shown with open circles. In Figure 4, the coordinates of data classified into the second cluster are shown with open squares. In Figure 4, the coordinates of data classified into the third cluster are shown with open triangles. Furthermore, in Figure 4, the center of gravity of each cluster is shown with a cross. Center of gravity C_1 is the center of gravity of the first cluster. Center of gravity C_2 is the center of gravity of the second cluster. Center of gravity C_3 is the center of gravity of the third cluster.

[0025] The reference cluster data is data of the transition of differential pressure with labels indicating the clustering results. Specifically, the reference cluster data is created by assigning a label that identifies the cluster into which the data is classified to each piece of data that is represented by a white symbol in the coordinate space. The reference cluster data created in this way is stored in the storage device 120 of the abnormality diagnosis system 100.

[0026] The abnormality diagnosis system 100 performs abnormality determination processing using reference cluster data stored in a storage device 120 . <Series of processes executed by the abnormality diagnosis system 100> The abnormality diagnosis system 100 performs diagnosis using data on the transition of hydraulic pressure when the hydraulic control device 240 to be diagnosed is operated in a predetermined diagnostic pattern. The predetermined diagnostic pattern is the same as the diagnostic pattern used when operating a hydraulic control device 240 that is not experiencing any abnormalities when creating the reference cluster data.

[0027] The flow of a series of processes for abnormality diagnosis executed by the abnormality diagnosis system 100 will be described with reference to Fig. 5. The series of processes shown in Fig. 5 are executed by the processing circuit 110 of the abnormality diagnosis system 100.

[0028] 5, the processing circuit 110 first measures the hydraulic pressure while operating the hydraulic control device 240 in a predetermined diagnostic pattern in the process of step S100. As in the case of creating the reference cluster data, the processing circuit 110 measures the line pressure PL, the first hydraulic pressure Pp, and the second hydraulic pressure Ps while operating the hydraulic control device 240 in accordance with the predetermined diagnostic pattern. The processing circuit 110 records the measured hydraulic pressure data in the storage device 120.

[0029] In the processing of step S110, the processing circuit 110 creates, as evaluation data, data indicating the transition of the differential pressure between the oil pressure recorded in the storage device 120 and the target oil pressure. The processing circuit 110 records the created evaluation data in the storage device 120.

[0030] In the process of step S120, the processing circuit 110 compares the evaluation data with the reference cluster data to cluster the evaluation data. Specifically, the processing circuit 110 compares the differential pressure data at multiple times included in the evaluation data with the centers of gravity of each cluster in the reference cluster data to determine to which cluster each piece of data at multiple times in the evaluation data belongs.

[0031] For example, the processing circuit 110 calculates the distance between each piece of differential pressure data at multiple times in the evaluation data and the center of gravity of each cluster. Then, the processing circuit 110 determines that each piece of data belongs to the cluster with the center of gravity that is closest to it. The distance may be calculated using any distance calculation method, such as Euclidean distance, Mahalanobis distance, or Manhattan distance. Any calculation method suitable for the diagnosis may be used.

[0032] In FIG. 4, the coordinates of one piece of differential pressure data at multiple times in the evaluation data are indicated by a black square symbol. In the example shown in FIG. 4, the center of gravity closest to the coordinates of this data is center of gravity C_2. Therefore, in this example, the processing circuit 110 determines that this data belongs to the second cluster. If the differential pressure data is significantly far from the center of gravity of any cluster, the processing circuit 110 determines that the data does not belong to any cluster. In this way, the processing circuit 110 determines to which cluster each piece of differential pressure data at multiple times included in the evaluation data belongs. The processing circuit 110 creates evaluation cluster data by assigning labels to each piece of differential pressure data at multiple times included in the evaluation data, identifying the cluster to which the data is classified. In this way, the evaluation cluster data is data obtained by assigning labels to the differential pressure transition data for the hydraulic control device 240 to be diagnosed. The processing circuit 110 stores the evaluation cluster data, which is the result of comparison with the reference cluster data, in the storage device 120.

[0033] In the process of step S140, the processing circuit 110 executes an abnormality determination process. In this abnormality determination process, the processing circuit 110 determines that an abnormality has occurred in the hydraulic control device 240 based on the fact that the cluster determination results between the reference cluster data and the evaluation cluster data are different.

[0034] Specifically, the processing circuit 110 compares the reference cluster data with the evaluation cluster data. As shown in FIG. 6, the processing circuit 110 compares the labels assigned to the data at each time in the evaluation cluster data with the labels assigned to the data at each time in the reference cluster data. In FIG. 6, label information in the reference cluster data is displayed in the "Reference Data" column, and label information in the evaluation cluster data is displayed in the "Evaluation Data" column. In this comparison, labels corresponding to the same time are compared. Then, the processing circuit 110 identifies areas where the labels assigned to the evaluation cluster data do not match the labels assigned to the reference cluster data. In FIG. 6, the label of the first cluster is shown as "1," the label of the second cluster is shown as "2," and the label of the third cluster is shown as "3." In FIG. 6, if the cluster does not belong to any of the clusters, the label is shown as "-."

[0035] 6, the labels at time T11 do not match. The processing circuit 110 stores and records the comparison result in the storage device 120. The processing circuit 110 then identifies the location of the abnormality depending on the time when the labels did not match.

[0036] When an abnormality occurs in the hydraulic control device 240, the transition of the hydraulic pressure fluctuation when operated according to the predetermined diagnostic pattern deviates from the transition of the fluctuation when no abnormality occurs. Therefore, the labels in the evaluation cluster data, which are the result of clustering, also differ from the labels in the reference cluster data.

[0037] As described above, the predetermined diagnostic pattern is defined so that abnormalities in each location of the hydraulic control device 240 can be diagnosed from the transition of the measured hydraulic pressures by varying the line pressure PL, the first hydraulic pressure Pp, and the second hydraulic pressure Ps over a predetermined period. In other words, the predetermined diagnostic pattern is defined so that it can be diagnosed at which location an abnormality has occurred based on information about the time when a difference occurs between the label in the evaluation cluster data and the label in the reference cluster data.

[0038] For example, if an abnormality occurs in the first hydraulic damper 245, the label in the reference cluster data and the label in the evaluation cluster data will not match at time T11.

[0039] 7 shows, for each type of anomaly, the time at which the labels in the reference cluster data and the labels in the evaluation cluster data become mismatched. In FIG. 7, white circles are displayed at the times at which the labels in the reference cluster data and the labels in the evaluation cluster data become mismatched. Black circles are displayed at the times at which the labels in the reference cluster data and the labels in the evaluation cluster data become mismatched. The storage device 120 of the anomaly diagnosis system 100 stores information for diagnosing these types of anomalies.

[0040] The example shown in Fig. 7 indicates that when an abnormality occurs in the second hydraulic damper 246, the labels in the reference cluster data and the labels in the evaluation cluster data do not match at time T23. The example shown in Fig. 7 indicates that when an abnormality occurs in the first control valve 247, the labels in the reference cluster data and the labels in the evaluation cluster data do not match at time T41. The example shown in Fig. 7 indicates that when an abnormality occurs in the second control valve 248, the labels in the reference cluster data and the labels in the evaluation cluster data do not match at time T25 and time T45.

[0041] If no abnormality occurs in the hydraulic control device 240 to be diagnosed, the transition of hydraulic pressure fluctuations when operated according to a predetermined diagnostic pattern will not differ significantly from the transition of fluctuations when the reference cluster data was created. Therefore, the labels in the evaluation cluster data, which are the result of clustering, will match the labels in the reference cluster data. Therefore, if no abnormality occurs in any location, the labels in the reference cluster data will match the labels in the evaluation cluster data at any time.

[0042] 7 is stored in the storage device 120 as information for diagnosing the types of abnormalities described above. In the abnormality determination process in step S140, the processing circuit 110 refers to this information stored in the storage device 120 to diagnose the presence or absence of an abnormality and the type of abnormality.

[0043] In the process of step S150, the processing circuit 110 outputs the diagnosis result obtained through the abnormality determination process. Specifically, the processing circuit 110 displays the diagnosis result on the display 130.

[0044] <Operation of this embodiment> The processing circuit 110 of the abnormality diagnosis system 100 creates evaluation data that indicates a transition in the differential pressure of the hydraulic control device 240 to be evaluated (step S110). The processing circuit 110 creates evaluation cluster data by determining to which cluster in the reference cluster data each piece of data in the evaluation data belongs (step S120). The processing circuit 110 determines that an abnormality has occurred in the hydraulic control device 240 based on the fact that the cluster determination results are different (step S140).

[0045] If an abnormality occurs in the hydraulic control device 240, the hydraulic pressure deviates from the target hydraulic pressure. Therefore, data on the transition of the differential pressure between the hydraulic pressure and the target hydraulic pressure when the hydraulic control device 240 is operated in a predetermined diagnostic pattern will show a change corresponding to the presence or absence of an abnormality. The abnormality diagnosis system 100 described above compares the reference cluster data with the evaluation cluster data. Then, the abnormality diagnosis system 100 determines whether an abnormality has occurred in the hydraulic control device 240 based on the deviation between the cluster determination results of the reference cluster data and the evaluation cluster data.

[0046] <Effects of this embodiment> (1) The abnormality diagnosis system 100 makes a judgment using data on fluctuations in the hydraulic pressure of the hydraulic control device 240, and therefore can detect changes in the behavior of the hydraulic pressure due to an abnormality and determine whether an abnormality has occurred in the hydraulic control device 240.

[0047] (2) The predetermined diagnostic pattern divides time periods into periods according to the type of abnormality to be detected, and sequentially controls the hydraulic pressures controlled by the hydraulic control device 240 in a pattern that makes it easy to detect each abnormality. The processing circuit 110 of the abnormality diagnosis system 100 detects the type of abnormality occurring in the hydraulic control device 240 based on information about the period of time in which the reference cluster data and the evaluation cluster data diverge.

[0048] The abnormality diagnosis system 100 controls multiple hydraulic pressures according to a diagnosis pattern so that each abnormality can be easily diagnosed. Therefore, if the time period during which the reference cluster data and the evaluation cluster data diverge is known, it can be determined that an abnormality corresponding to that time period has occurred. The abnormality diagnosis system 100 determines the type of abnormality occurring in the hydraulic control device 240 based on information indicating the time period during which the reference cluster data and the evaluation cluster data diverge. The abnormality diagnosis system 100 can determine the type of abnormality that has occurred.

[0049] <Example of change> This embodiment can be modified as follows: This embodiment and the following modifications can be combined and implemented within the scope of technical compatibility.

[0050] The abnormality diagnosis system 100 does not need to determine the type of abnormality. For example, it outputs the presence or absence of an abnormality and the label information at each time shown in FIG. That is, as in the above embodiment, the diagnostic pattern divides time periods according to the type of abnormality to be determined, and controls the multiple hydraulic pressures controlled by the hydraulic control device 240 in sequence using patterns that make it easier to determine each abnormality.

[0051] The processing circuit 110 outputs information indicating the time period during which the reference cluster data and the evaluation cluster data diverge, along with information on the determination result as to whether or not an abnormality has occurred in the hydraulic control device 240. In this case, the abnormality diagnosis system 100 can provide information that serves as material for determining not only whether or not an abnormality has occurred, but also the type of abnormality that has occurred.

[0052] The above-described abnormality diagnosis system 100 determines whether or not there is an abnormality in the hydraulic control device 240 that controls the hydraulic pressure supplied to the first pulley 210 and the second pulley 220 of a continuously variable transmission. The hydraulic control device 240 to be diagnosed is not limited to the hydraulic control device 240 of a continuously variable transmission. The diagnosis target may also be a hydraulic control device of a stepped transmission. Furthermore, the diagnosis target is not limited to a hydraulic control device of an automatic transmission.

[0053] In the above embodiment, the abnormality diagnosis system 100 includes a processing circuit 110 and a storage device 120 and executes software processing. However, this is merely an example. For example, the abnormality diagnosis system 100 may include a dedicated hardware circuit (e.g., an ASIC) that processes at least part of the software processing executed in the above embodiment. That is, the abnormality diagnosis system 100 may have any of the following configurations (A) to (C). (A) The abnormality diagnosis system 100 includes an execution device that executes all processing in accordance with a program and a storage device that stores the program. That is, the abnormality diagnosis system 100 includes a software execution device. (B) The abnormality diagnosis system 100 includes an execution device that executes part of the processing in accordance with a program and a storage device. Furthermore, the abnormality diagnosis system 100 includes a dedicated hardware circuit that executes the remaining processing. (C) The abnormality diagnosis system 100 includes a dedicated hardware circuit that executes all processing. Here, there may be multiple software execution devices and / or dedicated hardware circuits. That is, the processes may be performed by processing circuitry that includes one or more software executing devices and / or one or more dedicated hardware circuits. Storage devices, i.e., computer-readable media, that store the programs include any available media that can be accessed by a general-purpose or special-purpose computer. [Explanation of symbols]

[0054] 100... Abnormality diagnosis system, 110... Processing circuit, 120... Storage device, 130... Display, 200... Automatic transmission, 210... First pulley, 211... Input shaft, 220... Second pulley, 221... Output shaft, 230... Belt, 240... Hydraulic control device, 241... Regulator valve, 242... Modulator valve, 243... First linear solenoid valve, 244... Second linear solenoid valve, 245... First hydraulic damper, 246... Second hydraulic damper, 247... First control valve, 248... Second control valve, 300... Drive motor, 400... Load motor, 500... Hydraulic sensor

Claims

1. a processing circuit and a storage device; The storage device stores reference cluster data obtained by classifying differential pressure data at a plurality of times included in data on the transition of the differential pressure between the hydraulic pressure and the target hydraulic pressure when a hydraulic control device in which no abnormality has occurred is operated in a predetermined diagnostic pattern into a predetermined number of clusters by clustering, which is machine learning; the processing circuitry creating, as evaluation data, data indicating a transition of a differential pressure between a target hydraulic pressure and a hydraulic pressure recorded while the hydraulic control device to be evaluated is operated in accordance with the diagnostic pattern; creating evaluation cluster data by comparing differential pressure data at a plurality of times included in the evaluation data with the center of gravity of each cluster in the reference cluster data, and determining to which of the predetermined number of clusters in the reference cluster data each piece of data at a plurality of times in the evaluation data belongs; It is determined that an abnormality has occurred in the hydraulic control device based on the discrepancy between the cluster determination results of the reference cluster data and the evaluation cluster data. Abnormality diagnosis system.

2. The diagnostic pattern is a pattern in which a time period is divided according to the type of abnormality to be determined, and a plurality of hydraulic pressures controlled by the hydraulic control device are controlled in order in a pattern that makes it easy to determine each abnormality, The processing circuit outputs information indicating a time period during which the reference cluster data and the evaluation cluster data diverge, along with information on the determination result as to whether or not an abnormality has occurred in the hydraulic control device. The abnormality diagnosis system according to claim 1 .

3. The diagnostic pattern is a pattern in which a time period is divided according to the type of abnormality to be determined, and a plurality of hydraulic pressures controlled by the hydraulic control device are controlled in order in a pattern that makes it easy to determine each abnormality, The processing circuit determines the type of abnormality occurring in the hydraulic control device based on information about a time period in which the reference cluster data and the evaluation cluster data deviate from each other. The abnormality diagnosis system according to claim 1 .

4. The present invention determines whether or not there is an abnormality in a hydraulic control device that controls hydraulic pressure supplied to a first pulley and a second pulley in a continuously variable transmission. The abnormality diagnosis system according to claim 1 .

5. The diagnostic pattern is a pattern in which a time period is divided according to the type of abnormality to be determined, and a plurality of hydraulic pressures controlled by the hydraulic control device are controlled in order in a pattern that makes it easy to determine each abnormality, The plurality of hydraulic pressures controlled by the hydraulic control device include a first hydraulic pressure that is a hydraulic pressure at the first pulley and a second hydraulic pressure that is a hydraulic pressure at the second pulley. The abnormality diagnosis system according to claim 4 .

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