CN109689470B - Abnormality diagnosis device, abnormality diagnosis method, and computer program - Google Patents
Abnormality diagnosis device, abnormality diagnosis method, and computer program Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
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Abstract
Embodiments of the present invention enable high accuracy diagnostics. According to one embodiment, an abnormality diagnostic device includes an abnormality detector and a diagnotor. The abnormality detector performs abnormality detection of the deceleration performance of the vehicle based on the control command value for the brake device and the prediction model for the deceleration of the vehicle. The abnormality detector performs abnormality detection of the brake device based on the control command value and a prediction model of the braking force of the brake device. The diagnosing device diagnoses the vehicle based on the abnormality detection result of the deceleration performance and the abnormality detection result of the braking device.
Description
Technical Field
Embodiments of the present invention relate to an abnormality diagnostic device, an abnormality diagnostic method, and a computer program.
Background
Maintenance management and inspection of railway vehicles needs to be performed daily to maintain safety and reliability of railway operations. For example, when a brake apparatus of a railway vehicle malfunctions, the braking force obtained by the brake apparatus is reduced so that the vehicle cannot stop at a desired position, which may result in a reduction in convenience or, in the worst case, an accident. Therefore, vehicle maintenance management is of paramount importance to railway operators.
Conventional maintenance management has been focused on regular inspections of railway vehicles. However, recently, a technology for achieving early detection of brake abnormality by collecting and utilizing vehicle information such as sensors and control values acquired from each railway vehicle has been developed.
However, with railway vehicles, the running conditions dynamically change in a time-series manner due to, for example, the gradient of the railway track, weather changes, passengers getting on and off, and driver's operations. Therefore, it is difficult to make an accurate diagnosis of such a vehicle.
Embodiments of the present invention provide an abnormality diagnostic device, an abnormality diagnostic method, and a computer program that achieve highly accurate diagnosis.
Disclosure of Invention
According to one embodiment, an abnormality diagnostic device includes an abnormality detector and a diagnotor.
The abnormality detector performs abnormality detection of the deceleration performance of the vehicle based on the control command value for the brake device and the prediction model for the deceleration of the vehicle. The abnormality detector performs abnormality detection of the brake device based on the control command value and a prediction model of the braking force of the brake device.
The diagnosing device diagnoses the vehicle based on the abnormality detection result of the deceleration performance and the abnormality detection result of the braking device.
Drawings
Fig. 1 is a block diagram of an abnormality diagnostic system according to an embodiment of the present invention.
Fig. 2 is a diagram showing an exemplary configuration of a brake groove, a brake, and an air spring of a railway vehicle.
Fig. 3 is a diagram showing an exemplary configuration of a power generation brake and a regenerative brake of a railway vehicle.
Fig. 4 is a diagram illustrating an exemplary table related to measurement information and environment information.
Fig. 5 is a diagram illustrating an exemplary table related to measurement information.
Fig. 6 is a diagram illustrating an exemplary conversion table.
FIG. 7 is a diagram illustrating an exemplary model database.
Fig. 8 is a diagram illustrating an exemplary method of determining a threshold value by using a normal distribution.
Fig. 9 is a diagram illustrating an exemplary operation of the abnormality detector.
Fig. 10 is a diagram showing an exemplary detection result database.
Fig. 11 is a diagram illustrating an exemplary diagnostic rule database.
Fig. 12 is a diagram showing an exemplary display screen of the diagnosis output information.
Fig. 13 is a diagram illustrating another exemplary diagnostic rule database.
Fig. 14 is a diagram illustrating another exemplary diagnostic rule database.
Fig. 15 is a diagram showing a hardware configuration of the abnormality diagnostic apparatus according to the present embodiment of the invention.
FIG. 16 is a flow diagram of a diagnostic process according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described below with reference to the accompanying drawings. Any identical components between the drawings are denoted by the same reference numerals, and the description thereof will be omitted as appropriate.
Fig. 1 is a block diagram illustrating an exemplary abnormality diagnostic system according to an embodiment of the present invention.
The abnormality diagnostic system shown in fig. 1 includes an abnormality diagnostic device 100, a vehicle system 200, an environmental information system 300, a terminal 400, an input device 500, and a screen display device 600. An outline of the abnormality diagnosis system will be described below.
The abnormality diagnostic device 100 has a learning mode and an operation mode. In the learning mode, the model generator 140 generates an abnormality detection model of the deceleration performance of the railway vehicle (hereinafter, vehicle) based on at least one of the measurement information of the vehicle acquired from the vehicle system 200 and the environmental information of the vehicle acquired from the environmental information system 300. Information including at least one of the measurement information of the vehicle and the environment information of the vehicle is referred to as travel information. The vehicle may be included in a vehicle consist that is a plurality of vehicles that are connected.
The model generator 140 also generates an abnormality detection model of a brake device of the vehicle based on the travel information. The present embodiment assumes an air brake as the braking device, and generates an abnormality detection model for the air brake. In the case of a vehicle consist, an abnormality detection model of the brake device may be generated for each vehicle provided with the brake device. Instead of generating an abnormality detection model for each individual brake apparatus, an abnormality detection model commonly applied to a plurality of brake apparatuses may be generated.
The model generator 140 also generates an anomaly detection model for a braking system (hereinafter referred to as a consist brake). The consist brake includes brake devices provided in a plurality of vehicles, and the abnormality detection model for the consist brake is an abnormality detection model for all the brake devices.
Various anomaly detection models generated by model generator 140 are stored in model database 102.
In the operation mode, the abnormality detector 150 performs abnormality detection of deceleration performance by using a deceleration performance abnormality detection model. The abnormality detector 150 also performs abnormality detection of each air brake by using an abnormality detection model for the air brake. The abnormality detector 150 also performs abnormality detection of the consist brake by using an abnormality detection model for the consist brake. Anomaly detection determines whether an anomaly exists. The abnormality detection is also referred to as abnormality determination. The results of the detection of the abnormality of the deceleration performance, the air brake, and the train set brake are stored in the detection result database 103.
The diagnoser 160 performs vehicle diagnosis based on the abnormality detection result of the deceleration performance, the abnormality detection result of the air brake, and the abnormality detection result of the group brake. For example, when deceleration performance is normal, the air brakes are abnormal, and the consist brakes are abnormal, the diagnostics 160 diagnose that all of the air brakes are in evidence of degradation. When the deceleration performance is abnormal, the consist brakes are normal, and one of the air brakes is abnormal, the diagnoser 160 diagnoses that the deceleration performance has an abnormality attributable to deterioration of one brake device. Such diagnostics use various diagnostic rules stored in the diagnostic rules database 104.
The diagnotor 160 generates diagnosis output information according to the diagnosis result, and displays the generated diagnosis output information on the screen display device 600. The display supports supervision by e.g. railway maintenance personnel or drivers.
The learning mode and the operation mode may be switched automatically or by an instruction by a maintenance person or the like, or may be executed in parallel.
The air brake of the vehicle according to the present embodiment, its peripheral configuration, and the brake groove are described below. Fig. 2 shows an exemplary configuration of a brake groove, and an air brake and an air spring for a specific wheel of a vehicle. The brake groove is actually located in the cab of the consist.
The braking operation on the vehicle is not limited to the operation performed by the driver. For example, in a vehicle mounted with an automatic train unit stop (ATS), an automatic train unit control (ATC), or an automatic train unit operation (ATO), a braking operation is performed by the device instead of the driver in some cases. In this case, for example, the brake command output from the device corresponds to the control command value.
Fig. 2 shows a wheel 30 of a vehicle travelling on a railway 20. One device for braking a vehicle with a brake is a tread brake 42, which is an air brake. In this example, only one wheel is shown for simplicity of description, but actually, a plurality of pairs of left and right wheels are provided. For example, one air brake is included in each vehicle. However, some vehicles may not include air brakes.
The tread brake 42 uses a cylinder as a power source. When the brake cylinder pressure, which is the pressure inside the cylinder 43, increases, the brake pad 41 is pressed against the tread, which is the surface of the wheel 30 that contacts the rail. The frictional force between the wheel 30 and the brake pad 41 serves as the braking force of the tread brake 42.
Since the tread brake uses the frictional force of the brake pad in this way, the brake pad is worn due to continuous use, which may reduce the braking force. A tread brake is an exemplary mechanical brake for a vehicle, and another scheme uses a disc brake that obtains a braking force by pressing a disc fixed to a wheel shaft against a wheel with, for example, a brake pad (pad). The braking force of the brake varies with, for example, the state of wear of the brake pads or shoes. When an abnormality in the air brake is detected by the present abnormality diagnostic apparatus, a worker or the like can check, for example, the brake pads or disks of the air brake and check the actual presence of the abnormality.
In addition to wear of the components of the air brake, the braking force of the air brake also varies with the load on the vehicle. The load response device 50 is mounted on the vehicle shown in fig. 2. The load responding unit 50 includes an air spring 51, and measures the load on the vehicle by detecting the air spring pressure of the air spring 51. To control the braking of the vehicle, in addition to the operation of the brake lever 10, the braking force of the brake may be adjusted in response to the air spring pressure detected by the load-responsive device 50. Thus, a desired deceleration can be achieved regardless of changes in the load on the vehicle.
In order to supplement the braking force of the mechanical brake, an electric brake may additionally be used. Alternatively, a mechanical brake may be used to supplement the braking force of an electric brake. The electric brake will be described with reference to fig. 3. Fig. 3 shows an exemplary configuration of a power generation brake and a regenerative brake of a vehicle. The vehicle is one vehicle in a consist and is connected to other vehicles before and after the vehicle.
The main motors 60a and 60b are mounted on the vehicle shown in fig. 3. When the power-generating brake is used, the main motors 60a and 60b form a closed circuit with the resistor 70 to convert the electric power of the main motors into thermal energy.
When the regenerative brakes are applied, the electric power generated by the main motors 60a and 60b is transmitted to the line 90 through the pantograph 80. When the secondary battery is mounted on a vehicle, the generated electric power may be used to charge the secondary battery. In this way, the regenerative brake obtains braking force by converting kinetic energy into electric power using the main motors 60a and 60b as generators.
The mechanical brake and the power generation brake are exemplary, and the abnormality diagnostic device 100 may perform abnormality detection and diagnosis on any other scheme of the brake used.
Since the brake has a relatively complicated configuration and the characteristics of the brake vary depending on a number of factors and conditions, it is difficult to perform accurate abnormality detection on the brake of the vehicle.
For example, as described above, a plurality of versions of the brake having different characteristics are used in the vehicle. Further, as described above, the braking force of the vehicle brake varies with the load. For example, with a passenger vehicle, the number of passengers varies greatly with time slots and operation intervals, and therefore the braking force of the brake varies greatly within a short duration. For freight vehicles, the load varies greatly with the amount of freight. Further, the deceleration when the braking operation is performed may vary between the travel route and the interval of the vehicle having different tendencies of gradient and inclination. Furthermore, any differences in rainfall weather conditions, atmospheric temperature, etc. may change the physical properties of the brake components, thereby affecting the characteristics of the brake. As other factors, the driver performs the braking operation in different ways, and the vehicle is manufactured with different braking characteristics.
In the present embodiment, the abnormality detection model for deceleration performance (abnormality detection model for braking performance) and the abnormality detection model for group brakes are used together with the abnormality detection model for air brakes to reduce the risk of erroneous diagnosis, thereby achieving early abnormality detection and safe and reliable railway operation.
The abnormality diagnostic device 100 shown in fig. 1 will be described in further detail below. In the following description, it is assumed that the abnormality diagnosis target vehicle is a railway vehicle, but is not limited thereto. The abnormality diagnosis target vehicle may be an optional vehicle including wheels, such as an automobile, a construction machine, or an airplane.
The abnormality diagnostic apparatus 100 shown in fig. 1 includes a vehicle information collector 110, an environmental information collector 120, a data processor 130, a model generator 140, an abnormality detector 150, a diagnotor 160, and an alarm 170.
The vehicle information collector 110 acquires measurement information (also referred to as measurement data) related to the vehicle from various sensors of the vehicle system 200 inside the vehicle. Examples of sensors include: a sensor configured to sense a brake operation or the like of the vehicle as a control command value; a sensor configured to detect a deceleration of the vehicle; a sensor configured to detect a running speed; and a sensor configured to measure a load applied to the vehicle. Various other sensors may be included. The measurement information includes a sensed value of the sensor (e.g., a control command value) and a measured value of the sensor (e.g., a driving speed, a load, or a deceleration). When the vehicle system 200 calculates the deceleration from the value of the speed sensor, the calculated deceleration may be acquired as the measured value of the deceleration.
The kind of the measurement information to be acquired (the kind of the sensor or the kind of the control command value) may be optionally set. The measurement information may be acquired in an optionally set period. For example, the measurement information related to the running speed of the vehicle is acquired in a short sampling period in milliseconds. The value of a sensor configured to measure a load applied to the vehicle is acquired in a sampling period in units of minutes.
The environmental information collector 120 acquires environmental information of the vehicle from the environmental information system 300. Examples of the environmental information include information related to an operation route and information related to weather. Examples of the information related to the operation route include a gradient and a slope (a difference in height between inner and outer rails of a railway) at each interval. Examples of weather-related information include weather, atmospheric temperature, precipitation, wind speed, and atmospheric pressure. The acquisition of the environmental information may be acquisition of information accumulated in a database in the ground system or acquisition of information distributed from an external server. The kind of the environmental information to be acquired and the frequency of acquisition may be optionally set.
The abnormality diagnostic device 100 may be installed as a ground device outside the vehicle, for example, at a facility or an operation command center of a railway operation management company, or may be installed on the vehicle as an on-vehicle device. The abnormality diagnostic device 100 is not limited to a specific mounting manner.
When the abnormality diagnostic device 100 is installed as a ground device outside the vehicle, measurement information and the like of the vehicle system 200 inside the vehicle are received through, for example, on-vehicle components, transponder ground components, and a ground information network. Specifically, the vehicle system 200 transmits data to the ground information network through a ground element or the like, and the abnormality diagnostic apparatus 100 receives the data through the ground information network. The terrestrial information network may use, for example, a metal cable, a coaxial cable, an optical cable, a telephone line, a wireless device, or ethernet (registered trademark), but is not limited to a specific scheme. The abnormality diagnostic device 100 receives data from the environmental information system 300 through the ground information network.
When the abnormality diagnostic device 100 is an in-vehicle device, the abnormality diagnostic device 100 acquires data from the vehicle system 200 through an information network inside the vehicle. The information network inside the vehicle is, for example, an ethernet or a wireless Local Area Network (LAN), but may be implemented in any other scheme. The abnormality diagnostic device 100 can acquire data from the environmental information system 300 connected to the ground information network using the on-vehicle component and the transponder ground component.
The input device 500 provides an interface for the operation of a maintenance person. The input device 500 includes a mouse, a keyboard, a voice recognition system, an image recognition system, a touch pad, or a combination thereof. The maintenance person can input various commands or data to the abnormality diagnostic apparatus 100 through the input device 500 to perform operations.
The screen display device 600 displays data or information output from the abnormality diagnostic device 100 as a still image or a moving image. The screen display device 600 is, for example, a Liquid Crystal Display (LCD), an organic electroluminescence display, or a Vacuum Fluorescent Display (VFD), but may be a display device in any other scheme.
Each of the input device 500 and the screen display device 600 may be one of a plurality of installed devices. For example, the input device 500 and the screen display device 600 may be installed at each of the operation command center and the operation console of the vehicle.
The input device 500 and the screen display device 600 may be one integrated device. For example, when a display having a touch panel function is used, a single device may be used as the input device 500 and the screen display device 600.
The abnormality diagnostic device 100 includes databases of an information database 101, a model database 102, a detection result database 103, and a diagnostic rule database 104.
The databases 101, 102, 103, and 104 are all disposed inside the abnormality diagnostic apparatus 100 shown in fig. 1. However, the arrangement of the database is not limited to a specific method. For example, the partial database may be arranged in an external server or storage device. Each database may be implemented by a relational database management system and various NoSQL systems, but may be implemented in any other scheme. Each database may be in the storage format of XML, JSON, or CSV, or any other format, such as a binary format. Not all databases within the abnormality diagnostic apparatus 100 need to be implemented by the same database system and the same storage format, but the databases may be implemented in a mixture of various schemes.
The information database 101 stores the measurement information acquired by the vehicle information collector 110 and the environmental information acquired by the environmental information collector 120. The travel information according to the present embodiment includes at least one of measurement information and environment information. A storage medium such as a memory device storing the running information may be inserted into the abnormality diagnostic device 100 and used as the information database 101.
Fig. 4 and 5 show an exemplary information database 101. For example, the running information (measurement information and environmental information) is stored in the form of a table 101a shown in fig. 4 and a table 101b shown in fig. 5. In this example, the travel information is stored in two separate tables according to the sampling frequency.
The "time" column of the table 101a shown in fig. 4 stores the generation time of the entry. In this example, an entry is generated at each constant sample time. However, the entries may be generated at intervals preset to the railroad track or at any other criteria.
The "consist/vehicle" column of table 101a stores values identifying the consist and the vehicle. The present embodiment assumes a case in which one braking device is arranged in each vehicle. However, some vehicles may not include a braking device. A plurality of braking devices may be included in one vehicle.
The "weather" column of the table 101a stores information related to weather acquired from the environmental information system 300.
The "atmospheric temperature" column of table 101a stores information related to the atmospheric temperature acquired from the environmental information system 300. The information related to the atmospheric temperature may be an actual value or a label classifying the actual value. In the example shown in fig. 4, the column "atmospheric temperature" stores a label of any one of categories T1, T2, T3, T4, T5, T6, and T7 into which the atmospheric temperature as a real number is converted by using the conversion table 101c shown in fig. 6. For example, an atmospheric temperature of-11 ℃ is converted into the category T1, an atmospheric temperature of 15 ℃ is converted into the category T4, and an atmospheric temperature of 33 ℃ is converted into the category T6.
Columns other than the "atmospheric temperature" column may also store processing information obtained by performing calculation or conversion on measurement information or environmental information.
The "occupancy rate" column of table 101a stores the occupancy rate as an index of the load applied to the vehicle in percentage. Another indicator may be used to indicate load. The occupancy is defined by, for example, the ratio of the vehicle capacity to the number of passengers in the vehicle. Ride rate is typically estimated based on the air spring pressure of the load-responsive device. In this case, the air spring pressure can be directly used as an index.
The air spring pressure is an actual value of the sensor, which is not a value indirectly estimated by using, for example, a conversion table or a formula, unlike the ride rate, and thus can be used to reduce an error in model generation. However, the value of the air spring pressure depends on the manufacturer and the model of the load response device mounted on the vehicle, and thus lacks versatility. Therefore, when a commonly used index such as a riding rate is used, in some cases, a difference between vehicles due to different load response devices thereof can be absorbed.
The "grade" column of table 101a stores the grade of the route in values expressed in units of thousandths (permils). The permil ratio (permil) is a value obtained by using a height difference representing a horizontal distance of 1000 meters. The "grade" column may store another unit of value, with the per mil (Permil) being exemplary.
The "slope" column of table 101a stores the slope in millimeters, but may store another unit of value.
Table 101a also includes a "wind speed" column and a "barometric pressure" column. The table 101a may include columns that store other information such as the current location and current section on the railroad track.
The table 101b shown in fig. 5 stores time information, brake grooves, actual values of deceleration of the vehicle, and actual values of air brake pressures of the air brakes 1 to N provided in the vehicle. The entries in table 101b are generated at shorter time intervals than the time intervals of table 101 a. The generation interval (sampling interval) of the entries in table 101b may be the same as that of table 101 a. The table 101b and the table 101a may be integrated with each other. The deceleration may be a value of an acceleration sensor, or may be calculated from a value of a speed sensor.
Data stored in the information database 101 may be processed. For example, the data processor 130 causes the screen display device 600 to display the contents of each table stored in the information database 101. The maintenance person or the driver performs a processing operation on the data by using the input device 500. The data processor 130 performs data processing according to the processing operation.
The interval at which the vehicle information collector 110 or the environmental information collector 120 acquires information or data may be adjusted. For example, the data processor 130 receives an operation of designating an acquisition interval from a maintenance person or a driver through the input device 500, and adjusts the acquisition interval according to the content of the operation.
The model generator 140 generates an abnormality detection model for deceleration performance, an abnormality detection model for a brake device (air brake), and an abnormality detection model for a consist brake by using data stored in the information database 101.
The abnormality detection model for deceleration performance includes a deceleration prediction model (hereinafter referred to as deceleration model), and a threshold value (hereinafter referred to as deceleration threshold value) related to a residual of a predicted value from the deceleration model. The deceleration threshold is used for abnormality detection of deceleration of the vehicle by the abnormality detector 150. Specifically, the deceleration threshold value is used for comparison with a residual that is a difference between a predicted value of the deceleration model and an actual value of the deceleration.
The abnormality detection model for the brake apparatus includes a braking force prediction model, and a threshold value (hereinafter referred to as an individual braking threshold value) related to a residual of a predicted value from the prediction model. The braking force of the air brake corresponds to the air brake pressure. In the present embodiment, it is assumed that the braking force prediction model is an air brake pressure prediction model (hereinafter referred to as an air brake pressure model). The individual braking threshold value is used for abnormality detection of each braking device by the abnormality detector 150. Specifically, the individual brake threshold value is used for comparison with a residual that is a difference between a predicted value of the air brake pressure model and an actual value of the air brake pressure.
The abnormality detection model for the consist brake includes a prediction model based on values of braking forces of a plurality of brake devices (hereinafter referred to as a consist brake model), and a threshold value related to a residual of predicted values from the prediction model (hereinafter referred to as a consist brake threshold value). In the present embodiment, it is assumed that the brake device is an air brake, and a predictive model for the sum of air brake pressures is assumed. The sum of the braking forces may be replaced by a statistical value, such as an average or median value, or any other value based on the braking forces of a plurality of braking devices. In particular, the consist brake threshold value is used for comparison with a residual error which is the difference between a predicted value of the consist brake model and the sum of the actual values of the air brake pressure.
These anomaly detection models generated by model generator 140 are stored in model database 102.
Fig. 7 shows an example of the model database 102. Each anomaly detection model is identified by a model ID. The prediction model column stores data indicating a prediction model or an address (pointer) of a memory storing the prediction model. The threshold column stores the threshold set to the predictive model.
The generation of the abnormality detection model is performed in the learning mode, for example, when the abnormality diagnostic apparatus 100 is started or when a vehicle is newly added as a diagnostic target. When there are a plurality of diagnostic targets, an abnormality detection model is generated for each diagnostic target. The model generator 140 may copy the anomaly detection model periodically or in response to, for example, an instruction by a maintenance person, and update the previous anomaly detection model with the copied anomaly detection model.
The method of generating the abnormality detection model is described in detail below. The abnormality detection model is generated by using data samples (feature vectors) extracted from the information database 101.
An example in which the abnormality detection model for deceleration is generated will be described below, but an abnormality detection model for an air brake and an abnormality detection model for a consist brake may be generated in a similar manner.
The data samples (feature vectors) include one or more interpretation variables. An example of the explanatory variable is a value (control command value) of the brake groove in table 101 b. Any other values (e.g., speed) in the running information and specifications of the vehicle (e.g., the size and weight of the vehicle) may be additionally used as the explanatory variable. The explanatory variable may be generated by calculating a plurality of items included in the travel information. In this example, the target variable of the predictive model is deceleration. A data sample may be generated for each entry in the brake information table 101b or one data sample may be generated by collecting a plurality of consecutive entries with a lower time granularity.
A method of generating a predictive model (in this example, a deceleration model) is described below. Assume a case in which a regression model is used as a prediction model. The model generator 140 obtains a feature vector X ═ X (X) having an explanatory variable as an element by using the information database 1011,x2,x3,...,xn)。
Subsequently, the model generator 140 performs multiple regression analysis to calculate the formula (1) that predicts the deceleration as the target variable.
[ expression 1]
y＝b0+b1x1+b2x2+b3x3+...+bnxn(1)
In this formula, y represents a target variable, xnRepresents an explanatory variable, and bnPartial regression coefficients (parameters) are represented. The parameters may be calculated by maximum likelihood estimation or the like. To absorb the difference in measurement units between the explanatory variables, a standard partial regression coefficient may be used as the partial regression coefficient b by normalizing the target variable and all explanatory variables to the average value 0 and the degree of dispersion 1n. The number of interpretation variables may be one or more.
Model generation by multivariate regression analysis is exemplary, and a predictive model of the target variable may be generated by any other method, such as support vector regression or autoregressive.
Cross-validation may be used when generating the predictive model. For example, the data samples may be divided into sets such that at least one set is used as test data for validation and the other sets are used for model generation. This allows the performance of the generated model to be checked.
For example, when only the control command value is used as the explanatory variable, sufficient estimation accuracy cannot be obtained by using a simple relational formula. This is due to transient response such as deceleration or external factors such as grade of the railway track. Thus, to build a more accurate predictive model, parameter estimation can be performed by considering whether the deceleration is in transient response or stationary, the switching pattern of the brake grooves, and the effect of the railway track grade.
The above description is about exemplary generation of an abnormality detection model for deceleration. When generating the abnormality detection model for the air brake, for example, the control command value (brake groove) may be used as the explanatory variable, and the air brake pressure may be used as the target variable. For example, when generating an abnormality detection model for a consist brake, a control command value (brake groove) may be used as an explanatory variable, and the sum of air brake pressures of a plurality of air brakes may be used as a target variable. Any interpretation variable other than the control command value may be added.
It is assumed that in the learning mode, the information database 101 stores information acquired when the brake device (air brake) is normal. Therefore, various prediction models (a deceleration model, an air brake pressure model, and a consist brake model) generated in the learning mode are modeled based on the assumption that these air brakes are normal. However, the prediction model is applicable to a case where the information database 101 stores some measurement information of the faulty air brake.
The method of determining the threshold value set to the predictive model is described below. In the following description, the prediction model means any one of a deceleration model, an air brake pressure model, and a consist brake model. Similarly, the threshold value means any one of a deceleration threshold value, an individual braking threshold value, and a consist braking threshold value.
The threshold value is used to determine that an abnormality exists when a difference between a predicted value of the target variable (for example, a predicted value of deceleration) and a measured value (actual value) of deceleration calculated based on the prediction model exceeds the threshold value. The abnormality determination is also referred to as abnormality detection. The difference between the predicted value and the actual value is called the residual. The actual value may be greater or less than the predicted value, and thus the value of the residual may be positive or negative. When the sign is not important, the residual may be defined as the absolute value of the difference, which is the absolute value of the distance from the predictor.
FIG. 8 is used to describe an exemplary threshold determination method using a normal distribution. Fig. 8 shows a graph of a normal distribution 401 of residuals. The horizontal axis represents the residual and the vertical axis represents the probability density. Assuming that these residuals follow a normal distribution, a plurality of residuals between the predicted values and the actual values of the prediction model are obtained to generate the normal distribution 401. The data used to obtain the residual may be data samples used to generate the prediction model, test data, driving information unrelated to the generation of the prediction model, or a selectable combination thereof. When the residuals have a large variance, the distribution has a wider skirt, as shown by the normal distributions 402 and 403 shown in the figure with dashed lines.
The normal distribution 401 is used to set a threshold for the prediction model. For example, when the standard deviation is represented by σ, a multiple of the standard deviation, such as 2 σ or 3 σ, is set as the threshold value. When the residual exceeds a threshold set to 2 σ, an abnormality is detected in abnormality detection. When such a threshold value is set, about 95% of the actual value is determined as no abnormality (normal). As another exemplary threshold setting, a residual value corresponding to a predetermined probability (e.g., the highest X percentage point or the lowest X percentage point) or an absolute value thereof may be set as a threshold. The above threshold determination method is exemplary, and any other method may be used. For example, the threshold value may be determined by assuming a distribution other than the normal distribution, or may be determined by a person such as a maintenance person or a driver based on his experience.
Any of the deceleration threshold, the individual braking threshold, and the consist braking threshold may be determined by the methods described above.
In the operation mode, the abnormality detector 150 performs abnormality detection by using various abnormality detection models stored in the model database 102 and the travel information stored in the information database 101. For example, anomaly detection may be performed by generating a feature vector for each entry in table 101b or table 101a shown in fig. 5, may be performed by selecting entries at constant time intervals and generating a feature vector from the selected entries, or may be performed by generating a feature vector from entries within a constant duration. Alternatively, anomaly detection may be performed by generating a feature vector from, for example, an entry at a time specified by a maintenance person or an entry in a duration specified by a maintenance person.
The abnormality detector 150 performs abnormality detection of deceleration performance, abnormality detection of each air brake, and abnormality detection of the consist brake.
In abnormality detection of deceleration performance, a feature vector (e.g., a control command value) is generated from the travel information used in the abnormality detection, and deceleration is predicted by using the generated feature vector and a deceleration model. The residual between the predicted deceleration and the actual deceleration (e.g., retrieved from table 101 b) is compared to the deceleration threshold. When the residual is equal to or less than the deceleration threshold, the deceleration performance is determined to be normal. When the residual is greater than the deceleration threshold, the deceleration performance is determined to be abnormal.
In the abnormality detection of each air brake, a feature vector (e.g., a control command value) is generated from the running information used in the abnormality detection, and the air brake pressure is predicted by using the generated feature vector and an air brake pressure model. The residual between the predicted and actual airbrake pressures (e.g., obtained from table 101 b) is compared to the individual braking thresholds. When the residual is equal to or less than the individual braking threshold, the air brake is determined to be normal. When the residual is greater than the individual braking threshold, the air brake is determined to be abnormal.
In abnormality detection of a consist brake, a feature vector (e.g., a control command value) is generated from travel information used in the abnormality detection, and the sum of air brake pressures of a plurality of air brakes is predicted by using the generated feature vector and a consist brake model. The residual between the predicted airbrake pressure summation and the actual airbrake pressure summation is compared to a consist brake threshold. When the residual is equal to or less than the consist brake threshold, the consist brake is determined to be normal. When the residual is greater than the consist brake threshold, the consist brake is determined to be abnormal.
A specific exemplary operation of the deceleration performance abnormality detection by the abnormality detector 150 is described below.
Fig. 9 is a diagram for describing an exemplary operation of the abnormality detector 150. The upper part of fig. 9 shows the detent groove. The middle part of which shows the braking deceleration. The lower part shows the residual between the actual value and the predicted value of the prediction model. In the deceleration model, the brake groove corresponds to the explanatory variable, and the deceleration corresponds to the target variable.
At time t1, an operation is performed to set the brake groove to groove "4". Upon receiving the operation, each air brake applies a braking force to the vehicle. Therefore, the deceleration of the vehicle is increased and thereafter maintained at an approximately constant value for a while. There is a slight difference between the predicted value of deceleration and its measured value (actual value), but essentially transitions at the same value, and the residual between the predicted value and the actual value is less than the deceleration threshold.
Thereafter, the residual exceeds the deceleration threshold at three times of times t2, t3, and t4, and the abnormality detector 150 detects an abnormality at each time.
At time t5, an operation to change the brake groove from groove "4" to groove "2" is performed. Upon receiving this operation, each air brake reduces the braking force applied to the vehicle, and therefore, the deceleration of the vehicle decreases.
At time t6, an operation to cancel braking is performed. Upon receiving this operation, each air brake further reduces the braking force applied to the vehicle, and therefore, the deceleration of the vehicle further decreases.
After the abnormality is detected at the above time t4, the residual is within the range of the deceleration threshold, and therefore no abnormality is detected.
Although the above description is an exemplary operation regarding deceleration performance abnormality detection, abnormality detection of the air brake of each vehicle and abnormality detection of the consist brake may be performed in a similar manner.
The abnormality detector 150 stores information in the detection result database 103 based on the abnormality detection result of the deceleration performance, the abnormality detection result of the air brake, and the abnormality detection result of the group brake.
Fig. 10 shows an exemplary detection result database 103. The detection result database 103 stores the brake groove, the abnormality detection result of the deceleration performance, the abnormality detection result of the air brake of each vehicle, and the abnormality detection result of the vehicle group brake for a specific vehicle group in a time series manner. In this example, the abnormality detection result in any one of the first to sixth items indicates no abnormality, but an abnormality is detected at the air brake 1 in the seventh and eighth items.
When a predetermined abnormality is detected, the alarm 170 transmits an abnormality notification message to the terminal 400 used by a railway operator, a driver, or a maintenance person. The predetermined anomaly may optionally be defined as, for example, an anomaly detected in deceleration performance, an anomaly detected in a certain number of air brakes, or an anomaly detected in a consist brake.
The message notification may be performed by sending an email, displaying a pop-up message on an operation screen of the terminal 400, or performing notification by a predetermined tool management protocol, or may be performed by any other means. The notification includes detailed information of the abnormality (for example, a point on the map where the abnormality occurs (current point), or an identifier of the vehicle where the abnormality occurs). The operator or maintenance personnel can learn about the detection of an anomaly and its details by receiving a notification.
The diagnotor 160 performs vehicle diagnosis based on the detection result database 103 and the diagnosis rule database 104.
The diagnostic rule database 104 holds diagnostic rule data defining a diagnostic result for each combination of an abnormality detection result for each air brake (brake device), an abnormality detection result for deceleration performance, and an abnormality detection result for the group brakes.
Fig. 11 illustrates an exemplary diagnostic rules database 104. In this example, there are eight diagnostic rules, each having a diagnostic rule number 1 to 8. Each circle in fig. 11 means that the abnormality detection result indicates no abnormality. Each intersection in fig. 11 means that the abnormality detection result indicates an abnormality.
The diagnostic rule 1 defines a diagnostic result when all the results of the abnormality detection of the air brakes 1 to N indicate no abnormality, the abnormality detection result of the group brakes indicates no abnormality, and the abnormality detection result of the deceleration performance indicates no abnormality. Specifically, the diagnosis result of the diagnosis rule 1 indicates a normal state.
The diagnostic rule 2 defines a diagnostic result when all the results of the abnormality detection of the air brakes 1 to N indicate no abnormality, the abnormality detection result of the group brakes indicates no abnormality, and the abnormality detection result of the deceleration performance indicates an abnormality. Specifically, the diagnosis result of the diagnosis rule 2 indicates an abnormality in the state of the brake pad, the wheel, or the road surface.
The diagnostic rule 3 defines a diagnostic result when all the results of the abnormality detection of the air brakes 1 to N indicate no abnormality, the abnormality detection results of the group brakes indicate an abnormality, and the abnormality detection results of the deceleration performance indicate no abnormality. Specifically, the diagnostic result of the diagnostic rule 3 indicates an indication that the air brake as a whole is deteriorated.
The diagnostic rule 4 defines a diagnostic result when all the results of the abnormality detection of the air brakes 1 to N indicate no abnormality, the abnormality detection results of the group brakes indicate an abnormality, and the abnormality detection results of the deceleration performance indicate an abnormality. Specifically, the diagnostic result of the diagnostic rule 4 indicates an abnormality in deceleration performance due to deterioration of the air brake as a whole.
The diagnostic rule 5 defines a diagnostic result when at least one of the abnormality detection results of the air brakes 1 to N indicates an abnormality, the abnormality detection result of the group brakes indicates no abnormality, and the abnormality detection result of the deceleration performance indicates no abnormality. Specifically, the diagnostic result of the diagnostic rule 5 indicates deterioration of the air brake with abnormality or an indication thereof.
The diagnostic rule 6 defines a diagnostic result when at least one of the abnormality detection results of the air brakes 1 to N indicates an abnormality, the abnormality detection result of the group brake indicates no abnormality, and the abnormality detection result of the deceleration performance indicates an abnormality. Specifically, the diagnostic result of the diagnostic rule 6 indicates an abnormality of the deceleration performance, which is attributable to the air brake having the abnormality.
The diagnostic rule 7 defines a diagnostic result when all the results of the abnormality detection of the air brakes 1 to N indicate abnormality, the abnormality detection results of the group brakes indicate abnormality, and the abnormality detection results of the deceleration performance indicate no abnormality. Specifically, the diagnostic result of the diagnostic rule 7 indicates an indication of an abnormality in deceleration performance due to deterioration of the air brake as a whole.
The diagnostic rule 8 defines a diagnostic result when all the results of the abnormality detection of the air brakes 1 to N indicate abnormality, the abnormality detection results of the group brakes indicate abnormality, and the abnormality detection results of the deceleration performance indicate abnormality. Specifically, the diagnostic result of the diagnostic rule 8 indicates an abnormality in deceleration performance due to deterioration of the air brake as a whole.
Diagnostic rules other than diagnostic rules 1 through 8 may be defined. For example, as a modification of the diagnostic rule 7, the diagnostic result may be defined to indicate an indication of an abnormality in deceleration performance attributable to the deterioration of the air brake determined to have an abnormality when the number of air brake abnormality detection results indicating an abnormality is equal to or greater than a predetermined number. As a modification of the diagnostic rule 8, the diagnostic result may be defined as indicating an abnormality attributable to the deceleration performance determined to have the deterioration of the air brake having the abnormality when the number of the air brake abnormality detection results indicating the abnormality is equal to or greater than a predetermined number. Any other diagnostic rule may be defined.
The diagnotor 160 determines which of the diagnostic rules 1 to 8 each entry stored in the detection result database 103 matches, and generates diagnostic output information according to the diagnostic result indicated by the matching diagnostic rule. The matching may be performed using a plurality of diagnostic rules. The diagnotor 160 causes the screen display device 600 to display the generated diagnostic output information. When the diagnosis rule 1 indicating the normal diagnosis result matches, the diagnosis output information is not generated nor displayed.
When an abnormality in deceleration performance is detected, the diagnoser 160 may calculate error information from a difference between a predicted value of the deceleration model and a measured value of the deceleration, and output the calculated error information as diagnostic output information. In an exemplary calculation of the error information, a difference between the braking distance when the deceleration is at the predicted value and the braking distance when the deceleration is at the measured value may be calculated. The braking distance is the distance from the start of braking until the vehicle stops or until a desired deceleration or speed is reached. This helps to understand how much the air brake degradation or consist brake degradation affects deceleration compared to normal. Therefore, the braking distance difference is information on the degree of influence of the abnormality.
Fig. 12 shows an exemplary display screen (diagnosis result screen) of the diagnosis output information displayed by the screen display device 600. The screen displays the results of the diagnostics performed on consist A. In this example, the screen display device 600 is installed in a command room of a management and monitoring consist.
The uppermost portion of the diagnosis result screen shows that the diagnosis target consist is consist a. The second section displays the number of the matching diagnostic rule among the plurality of diagnostic rules. The third section displays the diagnostic result indicated by the matching diagnostic rule. In this example, the diagnostic rule 6 matches, and therefore shows that there is an abnormality in deceleration performance attributable to the deterioration of the individual airbrakes. The fourth section displays information specifying the air brakes to which the abnormality determined in the deceleration performance is attributable. The air brake may be determined by, for example, specifying an air brake whose abnormality detection result indicates an abnormality in the detection result database. The fifth section shows the difference in braking distance compared to the normal case as the degree of influence of the abnormality.
The diagnostic output information described above is exemplary and may be displayed in various formats depending on the application. The diagnostic output information may be sequentially recorded in a separately prepared database. In this case, a maintenance person or the like who selects a record from the database and performs a display instruction operation can display the diagnosis output information on the screen display device 600.
The above-described diagnostic rule shown in fig. 11 is defined by using three kinds of the air brake, deceleration, and consist brake abnormality detection results, but may be defined by using two kinds of these results. For example, the diagnostic rule may be defined by using the deceleration abnormality detection result and the air brake abnormality detection result.
For example, the air brake abnormality detection result and the deceleration abnormality detection result may be combined to define a diagnostic rule as shown in fig. 13. Alternatively, the consist brake anomaly detection result and the deceleration anomaly detection result may be combined to define a diagnostic rule as shown in fig. 14.
The abnormality diagnostic device according to the present embodiment can perform a high-accuracy vehicle diagnosis by combining three or two of the air brake, deceleration, and consist brake abnormality detection results. For example, the sign of the air brake abnormality may be detected based on the diagnostic rule 3, the diagnostic rule 5, or the diagnostic rule 7. Further, when a desired deceleration performance (braking performance) cannot be obtained based on the diagnostic rule 2, the external environment of the vehicle may be specified as a factor.
Fig. 15 shows a hardware configuration of the abnormality diagnostic apparatus according to the present embodiment. The abnormality diagnostic apparatus according to the present embodiment is implemented by the computer apparatus 100. The computer device 100 includes a CPU 151, an input interface 152, a display device 153, a communication device 154, a main storage device 155, and an external storage device 156. These components are interconnected by a bus 157.
A Central Processing Unit (CPU)151 executes an abnormality diagnostic program as a computer program on a main storage 155. The abnormality diagnostic program is a computer program that realizes each of the above-described functional components of the abnormality diagnostic apparatus. Each functional unit is realized by the CPU 151 executing an abnormality diagnostic program.
The input interface 152 is a circuit for inputting an operation signal from an input device such as a keyboard, a mouse, or a touch panel to the abnormality diagnostic device.
The display device 153 displays data or information output from the abnormality diagnostic device. The display device 153 is, for example, a Liquid Crystal Display (LCD), a Cathode Ray Tube (CRT), or a Plasma Display Panel (PDP), but is not limited thereto. Data or information output from the computer device 100 may be displayed by the display device 153.
The communication device 154 is a circuit that allows the abnormality diagnostic device to communicate with an external device in a wireless or wired manner. The measurement information may be input from an external device through the communication device 154. The measurement information input from the external device may be stored in the information database 101.
The main storage 155 stores, for example, an abnormality diagnostic program, data necessary for executing the abnormality diagnostic program, and data generated by executing the abnormality diagnostic program. The exception diagnostic program is loaded onto main storage 155 and executed. The main memory device 155 is, for example, a RAM, a DRAM, or an SRAM, but is not limited thereto. The information database 101, the model database 102, the test result database 103, and the diagnosis rule database 104 may be constructed on the main storage 155.
The external storage device 156 stores, for example, an abnormality diagnostic program, data necessary for executing the abnormality diagnostic program, and data generated by executing the abnormality diagnostic program. When the abnormality diagnosis program is executed, these program and data are read into the main storage 155. The external storage device 156 is, for example, a hard disk, an optical disk, a flash memory, or a magnetic tape, but is not limited thereto. The information database 101, the model database 102, the detection result database 103, and the diagnosis rule database 104 may be built on the external storage device 156.
The abnormality diagnostic program may be installed in advance on the computer apparatus 100, or may be stored in a storage medium such as a CD-ROM. Alternatively, the anomaly diagnostic program may be uploaded on the internet.
The computer apparatus 100 may include one or more CPUs 151, one or more input interfaces 152, one or more display devices 153, one or more communication devices 154, and one or more main storage devices 155, and may be connected to a peripheral device such as a printer or a scanner.
The abnormality diagnostic apparatus may be implemented by a single computer apparatus 100, or may be configured as a system including a plurality of computer apparatuses 100 connected to each other.
Fig. 16 is a flowchart of a diagnostic process performed in an operational mode according to an embodiment of the present invention. The process of the flowchart shown in fig. 16 may be executed at the time of diagnosing a specific operation of the target vehicle, may be executed in a constant cycle, may be executed upon receiving an instruction from a user such as a maintenance person, or may be executed at any other time. In this example, it is assumed that the diagnosis target vehicle is a vehicle consist.
In step S101, the abnormality detector 150 acquires the running information related to the diagnosis target vehicle consist from the information database 101 (refer to fig. 5 and 4).
In step S102, the abnormality detector 150 acquires an abnormality detection model related to the diagnosis target vehicle consist from the model database 102. Specifically, an abnormality detection model for deceleration, an abnormality detection model for an air brake mounted on each vehicle, and an abnormality detection model for a consist brake are obtained. An abnormality detection model for the air brake may be provided for each air brake, but in this example, a common abnormality detection model is provided for a plurality of air brakes.
The abnormality detection model for deceleration includes a deceleration model and a deceleration threshold. The abnormality detection model for the air brake includes an air brake pressure model and an individual brake threshold value. The anomaly detection model for the consist brake includes a consist brake model and a consist brake threshold.
In step S103, the abnormality detector 150 performs abnormality detection by using the information database 101 and various abnormality detection models stored in the model database 102. Specifically, the abnormality detector 150 generates a feature vector (e.g., a control command value) for deceleration abnormality detection from the running information, and predicts deceleration by using the generated feature vector and a deceleration model. The residual between the predicted deceleration and the actual deceleration is compared to a deceleration threshold. When the residual is equal to or less than the deceleration threshold, the deceleration is determined to be normal. When the residual is greater than the deceleration threshold, the deceleration is determined to be abnormal.
Similarly, the abnormality detector 150 also generates a feature vector (e.g., a control command value) for air brake abnormality detection from the running information, and predicts the air brake pressure by using the generated feature vector and an air brake pressure model. The residual between the predicted airbrake pressure and the actual airbrake pressure is compared to the individual brake threshold. When the residual is equal to or less than the individual braking threshold, the air brake pressure is determined to be normal. When the residual is greater than the individual braking threshold, the air brake pressure is determined to be abnormal.
The abnormality detector 150 also generates a feature vector (e.g., a control command value) for the train set brake abnormality detection from the traveling information, and predicts the sum of air brake pressures of the plurality of air brakes by using the generated feature vector and the train set brake model. The residual between the predicted airbrake pressure summation and the actual airbrake pressure summation is compared to a consist brake threshold. When the residual is equal to or less than the consist braking threshold, the sum is determined to be normal. When the residuals are greater than the consist brake threshold, the sum is determined to be abnormal.
In step S104, the diagnostic 160 diagnoses the diagnosis target vehicle consist by using the deceleration abnormality detection result, the air brake abnormality detection result, the consist brake abnormality detection result, and a plurality of diagnostic rules stored in the diagnostic rule database. The diagnotor 160 specifies a diagnostic rule matching these abnormality detection results, and determines a diagnostic result indicated by the diagnostic rule.
In step S105, the diagnotor 160 generates diagnosis output information according to the diagnosis result, and displays the diagnosis output information on the screen of the screen display device 600.
In the processing of the present flowchart, the diagnosis is performed by using three kinds of the air brake, the deceleration, and the train set brake abnormality detection result, but the diagnosis may be performed by using two kinds of these.
While certain embodiments have been described, these embodiments have been presented by way of example only, and are not intended to limit the scope of the invention. Indeed, the novel embodiments described herein may be embodied in various other forms; furthermore, various omissions, substitutions and changes in the form of the embodiments described herein may be made without departing from the spirit of the inventions.
[ description of reference numerals ]
10 brake lever
20 railway
30 wheel
41 brake pad
42 tread brake
43 air cylinder
50 load response device
51 air spring
60a, 60b main motor
70 resistor
80 pantograph
90 line
100 abnormality diagnosis device
101 information database
102 database of models
(deceleration model, threshold)
(air brake pressure model, threshold)
(vehicle set brake model, threshold)
103 database of test results
110 vehicle information collector
120 environment information collector
130 data processor
140 model generator
150 anomaly detector
160 diagnostic device
170 alarm
151 CPU
152 input interface
153 display device
154 communication device
155 main storage device
156 external storage device
157 bus
200 vehicle system
300 environment information system
400 terminal
401, 402, 403 normal distribution
500 input device
600 screen display device
Claims (14)
1. An abnormality diagnostic device comprising:
an abnormality detector for detecting an abnormality of the vehicle,
is configured to perform abnormality detection of deceleration performance of the vehicle based on a control command value input for a brake operation by a driver for the brake device and a prediction model for deceleration of the vehicle, the prediction model having the control command value as an explanatory variable and a predicted value of the deceleration as a target variable, and
configured to perform abnormality detection of the brake device based on the control command value and a prediction model for a braking force of the brake device, the prediction model having the control command value as an explanatory variable and a predicted value of the braking force as a target variable; and
a diagnosing device configured to diagnose the vehicle based on an abnormality detection result of the deceleration performance and an abnormality detection result of the braking device.
2. The abnormality diagnostic device according to claim 1,
the control command values are for a plurality of brake devices provided in the plurality of connected vehicles,
the abnormality detector performs abnormality detection of the brake device, and
the diagnosing device diagnoses the vehicle based on the abnormality detection result of the deceleration performance and the abnormality detection result of the braking device.
3. The abnormality diagnostic device according to claim 2, wherein the diagnostic device diagnoses the vehicle based on diagnostic rule data that defines a diagnostic result for each combination of the abnormality detection result of the deceleration performance and the abnormality detection result of the brake device.
4. The abnormality diagnostic device according to claim 2 or 3,
the abnormality detector performs abnormality detection of a brake system including the brake device based on a prediction model for a value based on a braking force of the brake device, and
the diagnostic device diagnoses the vehicle by using the abnormality detection result of the brake system.
5. The abnormality diagnostic device according to claim 4, wherein the value based on the braking force of the brake device is a sum of the braking forces of the brake devices.
6. The abnormality diagnostic device according to claim 4, wherein the diagnostic device diagnoses the vehicle based on diagnostic rule data that defines a diagnostic result for each combination of an abnormality detection result of deceleration performance, an abnormality detection result of the brake device, and an abnormality detection result of the brake system.
7. The abnormality diagnostic device according to any one of claims 1 to 3, wherein the abnormality detector performs abnormality detection of deceleration performance based on a difference between a predicted value of the prediction model for deceleration and a measured value of deceleration.
8. The abnormality diagnostic device according to any one of claims 1 to 3, wherein the abnormality detector performs abnormality detection of the brake device based on a difference between a predicted value of the predictive model for the braking force and a measured value of the braking force.
9. The abnormality diagnostic device according to claim 8, wherein the diagnoser calculates the abnormality influence degree information according to a difference between a predicted value of the prediction model for deceleration and a measured value of deceleration when the abnormality is detected in the brake device.
10. The abnormality diagnostic device according to claim 9, wherein the diagnoser calculates a difference between a braking distance of the deceleration at the predicted value and a braking distance of the deceleration at the measured value as the abnormality influence degree information.
11. The abnormality diagnostic device according to any one of claims 1 to 3, wherein the control command value is a command value relating to braking force.
12. The abnormality diagnostic device according to any one of claims 1 to 3,
the braking means being air brakes, and
the braking force is an air brake pressure.
13. An abnormality diagnostic method comprising:
performing abnormality detection of deceleration performance of the vehicle based on a control command value input for a brake operation by a driver for a brake device and a prediction model for deceleration of the vehicle, the prediction model having the control command value as an explanatory variable and a predicted value of deceleration as a target variable;
performing abnormality detection of the brake device based on the control command value and a prediction model for a braking force of the brake device, the prediction model having the control command value as an explanatory variable and having a predicted value of the braking force as a target variable; and
the vehicle is diagnosed based on the abnormality detection result of the deceleration performance and the abnormality detection result of the brake device.
14. A computer program for causing a computer to execute:
abnormality detection of deceleration performance of the vehicle is performed based on a control command value input for a brake operation by a driver for the brake device and a prediction model for deceleration of the vehicle, the prediction model having the control command value as an explanatory variable and a predicted value of deceleration as a target variable:
performing abnormality detection of the brake device based on the control command value and a prediction model for a braking force of the brake device, the prediction model having the control command value as an explanatory variable and a predicted value of the braking force as a target variable; and
the vehicle is diagnosed based on the abnormality detection result of the deceleration performance and the abnormality detection result of the brake device.
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Also Published As
| Publication number | Publication date |
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| JP6889057B2 (en) | 2021-06-18 |
| TW201908181A (en) | 2019-03-01 |
| TWI691420B (en) | 2020-04-21 |
| CN109689470A (en) | 2019-04-26 |
| WO2019012730A1 (en) | 2019-01-17 |
| JP2019022306A (en) | 2019-02-07 |
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