Vehicle breakdown symptom detection device

Abstract

To detect a symptom of a breakdown through a unique operation pattern of a vehicle 10, when this pattern is executed with a risk of causing the breakdown in any component of the vehicle, in a way that can draw the driver's attention.SOLUTION: A vehicle breakdown symptom detection device uses a discriminator to detect presence or absence of a symptom of a breakdown in any component of a vehicle. In the vehicle breakdown symptom detection device, the discriminator acquires a feature, which produces a significant difference between a value obtained from normal travel data and a value obtained from pre-breakdown travel data in a similar way to the normal travel data, from instantaneous travel data representing an instantaneous vehicle state based on an algorithm of machine learning using the preliminarily prepared normal travel data on the vehicle in a normal state and the pre-breakdown travel data on a vehicle state before the breakdown occurs in any component as training data. The discriminator is also configured to detect the symptom of the breakdown when the difference between the feature of the instantaneous travel data and the feature of the normal travel data exceeds a predetermined threshold.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a device for detecting signs of a malfunction in a vehicle such as an automobile, and more specifically, to a device for detecting when behavior that may lead to a malfunction (signs of a malfunction) is present in various driving data while the vehicle is running. [Background technology]

[0002] In order to detect failures in various devices, various configurations have been proposed in which a failure detection model is constructed using data prepared in advance for normal times and data prepared in abnormal times for a part of the device to be monitored, and such a failure detection model is used to detect abnormalities in data for the part to be monitored while the device is in operation or working. For example, Patent Document 1 proposes a configuration in which a distribution (normal time distribution) of detection value vectors obtained in normal times by a plurality of sensors in a part to be monitored of the device is obtained, and the presence or absence of an abnormality and a sensor having an abnormality are estimated from the deviation of the detection value vectors of the plurality of sensors obtained during the operation of the device from the normal time distribution. Patent Document 2 discloses a configuration in which the minimum fuel pressure in a fuel pipe in a fuel supply system of a vehicle or the like and data indicating the state when the minimum fuel pressure was stored are used to determine the location of a failure related to a drop in fuel pressure in the fuel pipe according to a machine learning algorithm such as a decision tree, and a system abnormality is diagnosed. Patent Document 3 proposes a configuration in which parameters such as total acid number are calculated from sensor values ​​such as the relative dielectric constant and electrical conductivity of the hydraulic oil of the device, and a symptom of failure of the device and parameters of its cause are estimated from the parameters using a decision tree structure algorithm. Patent Document 4 discloses a configuration for identifying the cause of a fault and a faulty part by using a knowledge base that stores knowledge information such as symptoms required for diagnosing faults in an automobile and artificial intelligence. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2020-98373 [Patent Document 2] Patent Publication No. 2021-195871 [Patent Document 3] Patent Publication No. 2021-167791 [Patent Document 4] JP 3-79445 Summary of the Invention [Problem to be solved by the invention]

[0004] However, when the driving manner of a vehicle is different from normal, a breakdown may occur in some part of the vehicle. When a breakdown occurs in some part of the vehicle due to such a driving manner that is different from normal (peculiar driving manner), behavior that leads to a breakdown due to the peculiar driving manner, i.e., a breakdown sign, appears in various driving data of the vehicle at a stage before the breakdown occurs. Therefore, if the driving data of the vehicle is monitored and a breakdown sign due to a peculiar driving manner is detected therein, it is possible to notify the driver that the driving manner is a peculiar manner that leads to a breakdown, and the driving manner is improved thereafter, and it is expected that a breakdown can be avoided. A breakdown sign due to a peculiar driving manner in the driving data of the vehicle can be detected by a classifier configured using a machine learning algorithm.

[0005] Thus, the main object of the present invention is to provide a device that detects signs of failure due to peculiar driving behavior in a vehicle's driving data, so that when an anomalous driving behavior that may lead to a failure in any part of the vehicle is being performed, the driver can be alerted to that fact. [Means for solving the problem]

[0006] According to the present invention, the above problem is solved by a vehicle failure symptom detection device, A real-time running data acquisition means for acquiring real-time running data which is running data representing a real-time state of the vehicle; a fault sign detection means for detecting the presence or absence of a fault sign in each part of the vehicle in response to the input of the real-time driving data; a failure sign notifying means for notifying the fact when a failure sign is detected in any part of the vehicle; Including, This is achieved by an apparatus in which the failure sign detection means is a classifier configured according to a machine learning algorithm that uses, as training data, normal driving data, which is driving data of a vehicle in a normal state that has been prepared in advance, and pre-failure driving data, which is driving data of a vehicle before a failure occurs in any part, and obtains from the real-time driving data a feature value in which the magnitude of the difference between the value obtained from the normal driving data and the value obtained in a similar manner from the pre-failure driving data is significant, and identifies that there is a failure sign when the magnitude of the difference between the feature value of the real-time driving data and the feature value of the normal driving data exceeds a predetermined magnitude, thereby detecting the presence or absence of a failure sign in any part of the vehicle.

[0007] In the above configuration, the "driving data representing the state of the vehicle" may be any data representing the state of each part of the vehicle, and specifically, for example, vehicle speed, accelerator opening or required driving force, shift lever position, transmission oil temperature, engine water temperature, motor temperature, mode selection switch position, output torque (MG1, MG2), etc., are state quantities related to driving and driving detected in each part of the vehicle, and may be time-series data (values ​​from moment to moment). The "real-time driving data" is driving data obtained in real-time while the vehicle is being driven (it may be time-series data over a predetermined time period that may be set appropriately). The "various parts of the vehicle" may be various parts of the vehicle, such as the engine / motor or each part thereof, the power transmission mechanism or transmission or each part thereof, the braking system or each part thereof, and each part of other on-board devices, and one or more parts may be selected as appropriate. As already mentioned, the "failure sign" is the behavior (fluctuation, distribution) of values ​​that appear in the driving data and lead to a failure (here, the "failure" may be caused by a specific driving mode, but is not limited to this). The "normal driving data, which is driving data of a vehicle in a normal state prepared in advance, and the pre-failure driving data, which is driving data of a vehicle with a failure in any part before the failure occurs" may each be data collected in advance by any method, for example, by experiments, simulations, etc. Furthermore, the "normal driving data" and the "pre-failure driving data" may be collected from vehicles in a state in which conditions such as the period of use or the mileage are approximately the same. The "machine learning algorithm" used in the "classifier" of the "failure sign detection means" may be any algorithm that can configure a classifier that outputs a normal or fault label corresponding to the normal driving data and the pre-failure driving data when the normal driving data and the pre-failure driving data are input using teacher data in which the feature values ​​obtained from the normal driving data and the pre-failure driving data are the explanatory variables and the corresponding normal label or fault label of any part of the vehicle are the objective variables. The "feature amount" is a value calculated or determined in various ways from the driving data, and the calculation or determination method may be appropriately configured or selected through experiments, etc. The real-time driving data acquisition means, the failure sign detection means, and the failure sign notification means are realized by an operation of a computer device according to a program.

[0008] In the above-described configuration of the device of the present invention, the "failure sign detection means" is configured to use a classifier configured according to the above-described machine learning algorithm to detect behaviors that may lead to a failure, i.e., failure signs, in the vehicle's moment-to-moment driving data, and when such a failure sign is detected, the driver, etc. is notified of the detection. With this configuration, the driver, etc. can know that there is a risk of failure if the vehicle is driven or driven in the same state before a failure actually occurs in any part of the vehicle, and it is expected that the driver, etc. will improve the peculiar driving behavior and inspect each part of the vehicle, which is expected to extend the life of the vehicle.

[0009] In the above embodiment of the present invention, the failure sign detection means may be provided for detecting a failure sign for each of a plurality of parts in a vehicle. In this case, when the difference between the feature amount of the real-time driving data and the feature amount of the normal driving data for the feature amount having the largest difference from the corresponding feature amount of the normal driving data, the feature amount having the largest rate of change, or the feature amount having a rate of change exceeding a predetermined value among the feature amounts obtained from the real-time driving data for each of the plurality of parts exceeds a predetermined value, it may be detected that there is a failure sign in the part corresponding to the feature amount. Also, as described in the embodiment section below, the damage rate or remaining life representing the degree of deterioration of each part of the vehicle may be estimated, and when the difference between the feature amount of the real-time driving data and the feature amount of the normal driving data for the feature amount having the largest damage rate or the smallest remaining life exceeds a predetermined value, it may be detected that there is a failure sign in the part corresponding to the feature amount. Effect of the Invention

[0010] Thus, according to the device of the present invention, when a malfunction sign (particularly a malfunction sign due to a peculiar driving pattern) is detected in the vehicle's driving data, the driver or the like is notified of the malfunction, which enables various measures to be taken to avoid malfunctions even when each part of the vehicle is not actually malfunctioning. Therefore, the device of the present invention is expected to contribute to extending the usable life of the vehicle as much as possible.

[0011] Other objects and advantages of the present invention will become apparent from the following description of preferred embodiments of the invention. [Brief description of the drawings]

[0012] [Figure 1] FIG. 1 is a diagram illustrating the outline of the configuration of a vehicle failure sign detection device according to this embodiment. [Diagram 2] Fig. 2(A) shows a schematic example of driving data (time series data) monitored by the vehicle failure sign detection device of the present embodiment. Fig. 2(B) and (C) show a schematic frequency distribution of driving data (accelerator opening, oil temperature) monitored by the vehicle failure sign detection device of the embodiment. In the figure, the distribution during normal operation and before failure are shown by dotted lines and solid lines, respectively. [Diagram 3] Fig. 3(A) is a diagram showing a typical distribution in a feature space of feature vectors of driving data monitored by the vehicle failure sign detection device of this embodiment. Fig. 3(B) is a diagram showing a typical change over time in the damage rate, which indicates the degree of deterioration of one part of a vehicle. "Normal" is a case where deterioration progresses in a normal state in a vehicle. [Figure 4] FIG. 4 shows, in the form of a flowchart, the process of creating a failure sign detection model used in the vehicle failure sign detection device of this embodiment. [Diagram 5] FIG. 5 shows, in the form of a flowchart, the process of detecting a sign of a vehicle failure in the vehicle failure sign detection device of this embodiment. [Explanation of symbols]

[0013] 10…Vehicle BEST MODE FOR CARRYING OUT THEINVENTION

[0014] The present invention will now be described in detail with reference to some preferred embodiments thereof with reference to the accompanying drawings, in which like reference numerals indicate like parts.

[0015] Device configuration In short, as shown in Fig. 1, the failure sign detection device of this embodiment is configured to obtain data (travel data) indicating the state of each part of the vehicle 10 while driving an arbitrary vehicle 10, compare the travel data with travel data (normal travel data) obtained (in advance) from a normal vehicle in a normal or general state, i.e., without any failure, collected from a network or other database, and, if there is a significant difference between the two, determine that there is a failure sign, i.e., that any part of the vehicle 10 is in a state that may lead to a failure (failure sign detection), and notify the driver, etc. of the fact. Therefore, the failure sign detection device of this embodiment is basically configured of a means for obtaining various travel data (real-time travel data) from each of a group of sensors provided in each part of the vehicle 10, a failure sign detection unit having a discriminator that receives the real-time travel data and detects a failure sign in any part of the vehicle 10, and a failure sign alarm that notifies the failure sign in any part detected by the failure sign detection unit. Here, the failure sign detection unit may be provided with a classifier that detects failure signs for each part of the vehicle that is to be monitored for failure signs (i.e., a plurality of classifiers may be configured). The failure sign detection unit is also provided with a means for collecting normal driving data from a database or the like for comparison with real-time driving data in a manner described in detail below. The failure sign alarm may be configured using any display or speaker that can visually or audibly detect a failure sign and notify the part where it is detected. The operation of the failure sign detection unit and the failure sign alarm is realized by operation in accordance with a program by a computer device.

[0016] Operation of the device (1) Configuration of failure prediction detection In the device of this embodiment, real-time driving data measured in each part of the target vehicle (hereinafter referred to as the "host vehicle"), such as data (which may be time-series data) on vehicle speed, accelerator opening or required driving force, shift lever position, transmission oil temperature, engine water temperature, motor temperature, mode selection switch position, and output torque (MG1, MG2), are referenced, and if the behavior of the data or the data is significantly different from that of the corresponding (prepared in advance) normal driving data and may be linked to a malfunction, a malfunction is detected as being pre-existing.

[0017] More specifically, for example, as shown in FIG. 2(A), various driving data (in the illustrated example, accelerator opening, vehicle speed, and oil temperature are included, but are not limited to these) change from moment to moment, and when looking at the frequency distribution of the values ​​of these data, as shown in FIGS. 2(B) and (C), when a driving manner that may cause a malfunction in any part of the vehicle (for example, a drive system device) is being performed, the frequency distribution of the values ​​of the data (solid line) at the stage before the malfunction occurs may differ from that in the case of normal driving conditions (dotted line). Therefore, in this embodiment, a feature (which may be a vector having multiple values) is calculated or extracted appropriately from driving data before the breakdown (pre-failure driving data) and normal driving data, and a feature that shows a significant difference or deviation between the pre-failure driving data and the normal driving data is calculated or extracted from the real-time driving data of the vehicle, and the degree of deviation of the feature of the real-time driving data from the feature of the normal driving data is evaluated.If the degree reaches a predetermined level that is set appropriately, it is determined that there is a sign of a failure.

[0018] The evaluation of the degree of deviation of the feature of the real-time driving data from the feature of the normal driving data may be performed in various ways. As one example, as shown in FIG. 3(A), a distribution of the feature of the normal driving data (normal distribution) is defined in the feature space of the driving data, and when the distance of the feature of the real-time driving data from the normal distribution exceeds a predetermined distance, more specifically, when the plot of the feature of the real-time driving data is so far away from the normal distribution that it is included in the distribution of the feature of the (pre-fault) pre-fault driving data (prepared in advance), it may be determined that a failure sign appears in the real-time driving data.

[0019] Generally, each part of a vehicle deteriorates with the increase in the period of use or the distance traveled, and the degree of deterioration is reflected in the driving data. When the degree of deterioration is expressed as a "damage rate" which is the ratio of the degree of deterioration until the part is deteriorated to the endurance limit (a state in which replacement or repair is required), as shown in Fig. 3(B) as a schematic diagram, when the vehicle is used in a normal manner, the damage rate progresses over time to reach S at time Tov, as shown by the dashed line (normal). On the other hand, if the damage rate Sh of the host vehicle at a certain time Tc is significantly higher than the normal damage rate St at the same time (if ΔS is significant), it can be determined that the part of the host vehicle is deteriorated earlier than normal and is at risk of breaking down. Therefore, in the detection of signs of failure in this embodiment, normal driving data and pre-failure driving data are prepared for each period of use or mileage of the vehicle, or for each section thereof, and feature values ​​obtained from the normal driving data and pre-failure driving data that show a significant difference in value are selected.In detecting signs of failure in the vehicle, the distance of the feature value distribution of the normal driving data from the state (point) of the real-time driving data for the selected feature values ​​for each period of use or mileage of the vehicle, or for each section thereof, may be evaluated as described above.

[0020] As already described, the failure sign detection unit of the device of this embodiment may be configured with a classifier for detecting the failure sign as described above for each part. In this embodiment, the evaluation of the feature vector may be performed for the part with the highest damage rate or the shortest remaining life, or the part with the highest rate of change (amount of change per unit time) of the feature, or the part with the rate of change exceeding a predetermined value that may be set appropriately, among the parts of the vehicle.

[0021] (2) Machine learning processing (construction of a classifier) ​​that constitutes a failure sign detection model In this embodiment, the machine learning process for constructing the failure sign detection model may be executed, for example, according to the processing procedure illustrated in Fig. 4. Note that the machine learning process may be executed for each part of the vehicle for which it is desired to detect a failure sign, and a classifier for each part may be prepared.

[0022] Referring to the figure, first, the driving data of a vehicle in which a failure has occurred (failure driving data) is collected by any method (preferably together with information on the period of use or mileage of each vehicle) (step 1), and data before the occurrence of a failure (pre-failure driving data) is extracted from the collected data (step 2). The failure driving data may be collected by an experiment or the like, or may be collected through a communication network from a database in which such data is accumulated. The failure driving data is preferably collected from a vehicle of the same type as the vehicle itself. The time width of the pre-failure driving data may be set appropriately depending on suitability. In addition, normal driving data is also collected by any method, similar to the failure driving data (step 3). The normal driving data may be data obtained from a vehicle that is in a state similar to the period of use or mileage of each vehicle in the pre-failure driving data and is not broken down. A part of the pre-failure driving data and the normal driving data collected up to this point are used to configure a failure sign detection model, and the other part is used to verify the configured model. Here, the pre-fault driving data and the normal driving data are explanatory variables in the classifier, and the state of each driving data, whether it is normal or indicates a fault symptom, is the objective variable.

[0023] Next, various feature quantities, which may be vectors, are calculated from the normal driving data and the pre-failure driving data (step 4), and the distance between the distribution of the feature quantities of the normal driving data and the distribution of the feature quantities of the pre-failure driving data in the feature quantity space may be calculated (step 5). The distance between the distributions may be calculated using a statistical value of the distribution, such as the average value, the median value, the variance value, the standard deviation value, etc. In addition, the processing of steps 4 and 5 may calculate the distance between the distributions and the degree of separation of the distributions while automatically preparing a combination of various feature quantities using a computer device. In this regard, a combination of various feature quantities may be prepared for each state, such as the driving mode, the period of use, or the mileage of the vehicle, and the distance between the distributions and the degree of separation of the distributions may be calculated. Thereafter, a feature quantity that separates the group of normal driving data and the group of pre-failure driving data as clearly as possible is selected with reference to the calculated distance between the distributions and the degree of separation of the distributions for each state, such as the driving mode, the period of use, or the mileage of the vehicle (step 6). Such a selection process may also be automatically performed using a computer device.

[0024] Thereafter, in order to verify whether a fault sign can be detected using the feature quantities selected by the above process, the feature quantities selected in the above step 6 are calculated from the pre-fault driving data and normal driving data for verification, and then the distances of state points represented by these feature quantities in the feature quantity space from the distribution of the normal driving data selected in the above step 6 are calculated, and it is confirmed whether each point of the feature quantities of the normal driving data and the pre-fault driving data for verification is included in the distribution of the normal driving data and the distribution of the pre-fault driving data (step 6), respectively (step 7). Then, if the proportion of cases in which the data for verification is correctly determined (estimation accuracy) exceeds a predetermined value (A%) that may be set appropriately, a model that uses the feature quantities selected in step 6 to discriminate the driving data is adopted as a fault sign detection model (steps 8 and 9), and if the estimation accuracy does not meet the predetermined value, modification of the model is considered (steps 8 and 10). By processing the machine learning algorithm as described above, a classifier is constructed that returns a result indicating that normal driving data is normal, and returns a result indicating that a fault is present in pre-fault driving data.

[0025] (3) Failure prediction detection process The process of detecting a fault sign in the vehicle in the device of this embodiment may be executed, for example, according to the procedure illustrated in FIG. 5. More specifically, while the vehicle is being driven, real-time travel data is acquired (step 11), and the feature of the fault sign detection model adopted in step 9 of FIG. 4 is calculated for the real-time travel data (step 12). The real-time travel data is acquired with the same time width as the normal travel data and the pre-fault travel data when the above-mentioned classifier is created. Then, in the feature space, the distance of the state point of the feature of the real-time travel data from the feature distribution of the normal travel data is calculated (step 13), and when the distance exceeds the distance (predetermined distance) from the feature distribution of the normal travel data to the tail of the distribution of the pre-fault travel data (step 14), it is determined that a fault sign appears in the real-time travel data, and the fault sign or even the driving mode at that time is pointed out as being peculiar (step 15). Such a point may be conveyed to the driver or the like through a fault sign alarm as already described. When the distance of the state point of the feature of the real-time driving data from the feature distribution of the normal driving data is less than the predetermined distance, no indication (notification) is executed (step 16).

[0026] The above-described failure sign detection process may be executed for each part of the vehicle, and when a failure sign is detected in any part, the failure sign may be notified. At that time, it may also be notified in which part the failure sign was detected.

[0027] Thus, according to the configuration of the present embodiment, before a breakdown occurs in a vehicle, a fault sign in any part can be detected in the driving data, and in that case, it is pointed out to the driver that the current driving behavior is a specific driving behavior that may lead to a breakdown. This is expected to encourage the driver to improve the driving behavior or inspect the part where the fault sign was detected, and the service life of the vehicle is expected to be extended.

[0028] The above description has been given in relation to the embodiment of the present invention, but it will be apparent to those skilled in the art that many modifications and changes can be easily made thereto, and that the present invention is not limited to the embodiment exemplified above, but can be applied to various devices without departing from the concept of the present invention.

Claims

[Claim 1] A vehicle failure sign detection device, A real-time running data acquisition means for acquiring real-time running data which is running data representing a real-time state of the vehicle; a fault sign detection means for detecting the presence or absence of a fault sign in each part of the vehicle in response to the input of the real-time driving data; a failure sign notifying means for notifying the fact when a failure sign is detected in any part of the vehicle; Including, The failure sign detection means is a classifier configured according to a machine learning algorithm that uses, as training data, normal driving data, which is driving data of a vehicle in a normal state that has been prepared in advance, and pre-failure driving data, which is driving data of a vehicle before a failure occurs in any part, and obtains from the real-time driving data a feature value in which the magnitude of the difference between the value obtained from the normal driving data and the value obtained in a similar manner from the pre-failure driving data is significant, and a device configured to detect the presence or absence of a failure sign in any part of the vehicle using the classifier that identifies the presence or absence of a failure sign in any part of the vehicle when the magnitude of the difference between the feature value of the real-time driving data and the feature value of the normal driving data exceeds a predetermined value.

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