Fault diagnosis system

Abstract

The present application improves the accuracy of vehicle failure diagnosis. A failure diagnosis system (1) includes a data acquisition unit (40) that acquires driving environment data and actual driving result data representing actual driving results based on the driving environment data, the driving environment data including driving operation data representing driving operations of a subject vehicle that is a subject of failure diagnosis; a proper range setting unit (42) that derives a proper range of actual driving results based on the driving environment data for a vehicle of the same type as the subject vehicle; and a failure determination unit that determines whether the subject vehicle has a failure by determining whether the actual driving result data is included in the proper range.

Description

Technical Field

[0001] This invention relates to a fault diagnosis system. Background Technology

[0002] For example, Patent Document 1 discloses a fault diagnosis device for diagnosing faults that occur in a vehicle.

[0003] Existing technical documents

[0004] Patent documents

[0005] Patent Document 1: Japanese Patent Application Publication No. 2006-349429 Summary of the Invention

[0006] Technical issues

[0007] Individual differences exist in vehicles due to manufacturing processes. Therefore, in vehicle fault diagnosis, misdiagnosis can sometimes occur depending on the specific vehicle.

[0008] Therefore, the object of the present invention is to provide a fault diagnosis system that can improve the accuracy of vehicle fault diagnosis.

[0009] Technical solution

[0010] To address the aforementioned issues, a fault diagnosis system according to one aspect of the present invention comprises: a data acquisition unit that acquires driving environment data by associating it with actual driving result data representing actual driving results based on the driving environment data, wherein the driving environment data includes at least driving operation data representing driving operations of a target vehicle that is the subject of fault diagnosis; an appropriate range setting unit that derives an appropriate range of actual driving results based on the driving environment data for vehicles of the same type as the target vehicle; and a fault determination unit that determines whether the target vehicle has a fault by determining whether the actual driving result data is included within the appropriate range.

[0011] In addition, the fault diagnosis system may also include: a vehicle simulation model that simulates the operation of a vehicle on a computer; and a fault determination unit that, when the fault determination unit determines that the target vehicle has a fault, repeatedly changes the parameters of the vehicle simulation model and performs a simulation with driving environment data set as the input data of the vehicle simulation model to derive virtual driving result data representing virtual driving results, and determines the part related to the changed parameters of the vehicle simulation model when the virtual driving result data is consistent with the actual driving result data as the fault part.

[0012] Technical effect

[0013] According to the present invention, the accuracy of vehicle fault diagnosis can be improved. Attached Figure Description

[0014] Figure 1 This is a schematic diagram showing the configuration of the fault diagnosis system of this embodiment.

[0015] Figure 2 It is a graph that illustrates the appropriate range of actual driving results data and the judgment of whether there is a fault. Figure 2 (A) represents an example of actual driving result data for other vehicles. Figure 2 (B) indicates an example of an appropriate range. Figure 2 (C) represents an example of the actual driving result data of the object vehicle.

[0016] Figure 3 This is a flowchart illustrating the process of processing data received from the vehicle.

[0017] Figure 4 This is a flowchart illustrating the workflow of the fault diagnosis department.

[0018] Figure 5 It is a diagram illustrating the vehicle simulation model.

[0019] Figure 6 It is a diagram illustrating the relationship between the vehicle simulation model and vehicle faults. Figure 6 (A) and Figure 6 (B) represents the relationship between the actual driving result data and the virtual driving result data when the object vehicle has no faults. Figure 6 (C) and Figure 6 (D) represents the relationship between the actual driving result data and the virtual driving result data when the object vehicle has a fault.

[0020] Figure 7 This is a diagram illustrating the relationship between the vehicle simulation model and the determination of the faulty component. Figure 7 (A) and Figure 7 (B) represents an example of the process for identifying the faulty part. Figure 7 (C) and Figure 7 (D) indicates an example where the faulty part has been identified.

[0021] Figure 8 This is a flowchart illustrating the workflow of the fault determination section.

[0022] Symbol Explanation

[0023] 1. Fault Diagnosis System

[0024] Vehicles 10, 10a, 10b, 10c, and 10d

[0025] 36 Vehicle simulation models

[0026] 40 Data Acquisition Department

[0027] 42 Appropriate range setting section

[0028] 44 Fault Diagnosis Department

[0029] 46 Fault Determination Section Detailed Implementation

[0030] The following description of embodiments of the present invention is provided in detail with reference to the accompanying drawings. The dimensions, materials, and other specific values ​​shown in these embodiments are merely examples for easy understanding of the invention and are not intended to limit the invention unless specifically stated otherwise. It should be noted that in this specification and the accompanying drawings, elements having the same function or structure are omitted from repeated description by using the same symbols, and elements not directly related to the present invention are omitted from illustration.

[0031] Figure 1 This is a schematic diagram showing the configuration of the fault diagnosis system 1 according to this embodiment. The fault diagnosis system 1 includes multiple vehicles 10 and a management server 12. The vehicles 10 can be motorized vehicles, electric vehicles, or hybrid vehicles. Figure 1 In the example, four vehicles 10a, 10b, 10c, and 10d are represented as multiple vehicles 10. Later, vehicles 10a, 10b, 10c, and 10d are sometimes collectively referred to as vehicle 10. The number of vehicles 10 is not limited to four; it can be multiple, including two, three, or more than five.

[0032] The fault diagnosis system 1 performs fault diagnosis on a predetermined vehicle 10 among multiple vehicles 10 to determine whether a fault has occurred. Afterwards, the vehicle 10 that will sometimes be the object of fault diagnosis is referred to as the object vehicle. Alternatively, other vehicles that are different from the object vehicle, i.e., vehicles that are not the object of fault diagnosis, are sometimes simply referred to as other vehicles. For example, in... Figure 1 In this context, the target vehicle is vehicle 10a, and the other vehicles are vehicles 10b, 10c, and 10d. It should be noted that the target vehicle is not limited to vehicle 10a, and can be arbitrarily selected from multiple vehicles 10.

[0033] The vehicle 10 includes a vehicle control unit 20 and a vehicle communication unit 22. The vehicle control unit 20 is composed of a semiconductor integrated circuit including a central processing unit, a ROM storing programs, and RAM serving as a working area.

[0034] The vehicle control unit 20 acquires data representing driving operations such as acceleration, deceleration, and steering of the vehicle via acceleration sensors, brake sensors, gear position sensors, or steering angle sensors (not shown). Additionally, the vehicle control unit 20 acquires data representing the external environment via various detection devices such as radar, infrared sensors, cameras, or temperature sensors. The external environment can be defined as various factors affecting the vehicle's driving, such as slope, road surface condition, weather, wind direction, wind speed, ambient temperature, or air pressure.

[0035] Subsequently, data representing driving operations is sometimes referred to as driving operation data. Additionally, data representing the external environment is sometimes referred to as external environment data. Furthermore, both driving operation data and external environment data are sometimes collectively referred to as driving environment data. Driving environment data only needs to include at least driving operation data; external environment data can be omitted.

[0036] The vehicle control unit 20 controls the overall operation of the vehicle 10, including driving, braking, and steering, based on driving environment data. Furthermore, the vehicle control unit 20 can acquire data representing actual driving results using various sensors, such as a speed sensor (not shown). Hereinafter, data representing actual driving results is sometimes referred to as actual driving result data. Examples of actual driving result data include wheel speed, vehicle 10 speed, and vehicle 10 acceleration. Additionally, actual driving result data can also be intermediate output results of the vehicle 10, such as engine speed. The vehicle control unit 20 correlates driving environment data with the actual driving result data obtained based on the driving environment data.

[0037] The vehicle communication unit 22 is capable of wireless communication with external communication devices such as the management server 12. The vehicle control unit 20 periodically sends driving environment data, actual driving result data, and vehicle identification data to the management server 12 via the vehicle communication unit 22. The vehicle identification data is data that identifies the vehicle itself. It includes vehicle type data indicating the model of the vehicle.

[0038] The management server 12 is set up by, for example, the administrator of the fault diagnosis system 1. The management server 12 includes a server communication unit 30, a data storage unit 32, a server control unit 34, and a vehicle simulation model 36. The server communication unit 30 is capable of wireless communication with, for example, each of a plurality of vehicles 10. The data storage unit 32 includes non-volatile storage elements.

[0039] The server control unit 34 is composed of a semiconductor integrated circuit including a central processing unit, a ROM storing programs, and RAM serving as a working area. The server control unit 34 executes programs, thereby functioning as a data acquisition unit 40, an appropriate range setting unit 42, a fault judgment unit 44, and a fault part determination unit 46.

[0040] The data acquisition unit 40 acquires driving environment data, actual driving result data, and vehicle identification data sent from each vehicle 10 by associating them with the server communication unit 30. For all vehicles 10, including the target vehicle, the data acquisition unit 40 acquires the driving environment data by associating it with the actual driving result data based on the driving environment data. The data acquisition unit 40 stores the acquired driving environment data, actual driving result data, and vehicle identification data in the data storage unit 32.

[0041] The appropriate range setting unit 42 derives an appropriate range for the actual driving result data of each driving environment data based on driving environment data and actual driving result data of one or more vehicles. The appropriate range setting unit 42 updates the appropriate range each time driving environment data and actual driving result data are acquired. The appropriate range of the actual driving result data becomes the benchmark for determining whether the target vehicle has a fault. The appropriate range will be described in detail later.

[0042] The fault diagnosis unit 44 determines whether the target vehicle has a fault by judging whether the actual driving result data of the target vehicle falls within an appropriate range. The fault diagnosis unit 44 will be described in detail later.

[0043] If a fault is determined to exist in the target vehicle, the fault determination unit 46 uses the vehicle simulation model 36 to determine the faulty part. The vehicle simulation model 36 is software that simulates the operation of the vehicle 10 on a computer. The vehicle simulation model 36 is stored on a storage medium not shown. The vehicle simulation model 36 is executed by hardware such as the server control unit 34, thereby enabling the simulation of the operation of the vehicle 10. The vehicle simulation model 36 and the fault determination unit 46 will be described in detail later.

[0044] Figure 2 It is a graph that illustrates the appropriate range of actual driving results data and the judgment of whether there is a fault. Figure 2 (A) represents an example of actual driving result data for other vehicles. Figure 2 In (A), the solid line A10b represents the actual driving result data of vehicle 10b, the solid line A10c represents the actual driving result data of vehicle 10c, and the solid line A10d represents the actual driving result data of vehicle 10d. Figure 2 (B) indicates an example of an appropriate range. Figure 2 (C) represents an example of actual driving result data for the object vehicle. As an example of actual driving result data, in Figure 2 (A) Figure 2 (C) represents the change in engine speed over time when the engine starts.

[0045] The data acquisition unit 40 categorizes the driving environment data and actual driving result data acquired from the vehicle 10 according to each vehicle type, and sequentially accumulates the driving environment data and actual driving result data acquired from the vehicle 10 into the data storage unit 32. Furthermore, the data acquisition unit 40 categorizes the acquired driving environment data and actual driving result data according to each scenario, and sequentially accumulates the acquired driving environment data and actual driving result data into the data storage unit 32.

[0046] The scenario is presumably a situation where malfunctions are likely to occur, such as when the engine starts or when driving at a constant low speed. The scenarios are preset by the administrator of the management server 12, etc., for each type of vehicle 10. Predetermined conditions representing representative examples of driving environment data are associated with each scenario and the preset scenario. These predetermined conditions serve as the judgment criteria for distinguishing driving environment data according to each scenario. When the acquired driving environment data satisfies the aforementioned predetermined conditions, the data acquisition unit 40 classifies the driving environment data within a predetermined time range that satisfies the predetermined conditions, as well as the actual driving result data, into the scenario corresponding to those predetermined conditions.

[0047] Figure 2 (A) represents an example of actual driving result data that is the same type of vehicle as the object vehicle and is associated with the driving environment data at the time of engine startup. Figure 2 In (A), for vehicle 10b, 10c, and 10d, the actual driving results data at engine start are presented in a consistent manner. For example... Figure 2 As shown in (A), since the vehicle type and driving environment data are the same, the actual driving results data of multiple vehicles 10 show a trend of being similar to each other.

[0048] The appropriate range setting unit 42 performs statistical processing on multiple actual driving result data with the same vehicle type and driving environment data, and derives an appropriate range for the actual driving result data. The appropriate range setting unit 42 derives an appropriate range for the actual driving result data for each vehicle type and driving environment data. Figure 2 In (B), the dashed line A20a represents an example of the upper limit of the appropriate range, and the dashed line A20b represents an example of the lower limit of the appropriate range. The appropriate range is the area between the dashed lines A20a and A20b. The appropriate range setting unit 42 can set the upper limit of the appropriate range as, for example, the value obtained by adding 3σ to the average of multiple actual driving result data, and can set the lower limit of the appropriate range as the value obtained by subtracting 3σ from the average. σ represents the standard deviation.

[0049] It should be noted that the upper and lower limits of the appropriate range are not limited to addition and subtraction of 3σ based on the average value, but can also be derived by any derivation method. In addition, the appropriate range setting unit 42 can also change the value of 3σ to, for example, 2σ or 4σ according to each vehicle type or driving environment data, and can also make the width of the appropriate range different according to each vehicle type or driving environment data.

[0050] like Figure 2 As shown in (C), the fault determination unit 44 compares the actual driving result data of the target vehicle, illustrated by solid line A10a, which is associated with driving environment data of a predetermined scenario such as engine start-up, with an appropriate range of actual driving result data based on driving environment data of the same scenario. If the actual driving result data of the target vehicle is included within the appropriate range, the fault determination unit 44 determines that there is no fault in the target vehicle. On the other hand, if at least a portion of the actual driving result data of the target vehicle deviates from the appropriate range within a predetermined time range corresponding to the scenario, the fault determination unit 44 determines that there is a fault in the target vehicle. Figure 2 In example (C), since the actual driving result data of the target vehicle contains a portion that exceeds the upper limit of the appropriate range, the fault judgment unit 44 judges that there is a fault in the target vehicle.

[0051] Figure 3 This is a flowchart illustrating the process of processing data received from vehicle 10. If driving environment data and actual driving result data are received from any vehicle 10, the data acquisition unit 40 performs... Figure 3 A series of processes.

[0052] First, the data acquisition unit 40 classifies the received driving environment data and actual driving result data according to each vehicle type based on the vehicle type data received together with the driving environment data and actual driving result data (S100).

[0053] Next, the data acquisition unit 40 classifies the received driving environment data and actual driving result data according to the driving environment data of each predetermined scenario (S110). Specifically, the data acquisition unit 40 classifies the driving environment data by determining whether the driving environment data meets the predetermined conditions of each scenario according to each predetermined condition.

[0054] Next, the data acquisition unit 40 stores the driving environment data and actual driving result data in the data storage unit 32 according to each vehicle type and driving environment (S120).

[0055] Next, the appropriate range setting unit 42 exports the appropriate range of actual driving result data under the classified vehicle type and driving environment data (S130). Specifically, the appropriate range setting unit 42 reads multiple actual driving result data associated with the classified vehicle type and driving environment data, performs statistical processing, and exports the appropriate range. Then, the appropriate range setting unit 42 stores the exported appropriate range in the data storage unit 32 and updates the appropriate range (S140).

[0056] Figure 4 This is a flowchart illustrating the operation of the fault diagnosis unit 44. For example, the administrator of the management server 12 designates vehicle 10, which requires fault diagnosis, as the target vehicle and issues a fault diagnosis start instruction to the server control unit 34. If the diagnosis start instruction is received, the fault diagnosis unit 44 performs... Figure 4 A series of processes.

[0057] First, the fault determination unit 44 reads the actual driving result data of the target vehicle for each predetermined scenario's driving environment data from the data storage unit 32 (S200). It should be noted that the fault determination unit 44 is not limited to reading the actual driving result data from the data storage unit 32; the fault determination unit 44 may also use newly obtained actual driving result data from the target vehicle for subsequent processing.

[0058] Next, the fault determination unit 44 reads an appropriate range of driving environment data for each predetermined scenario for the same vehicle type as the target vehicle from the data storage unit 32 (S210). Then, the fault determination unit 44 determines whether the actual driving result data of the target vehicle falls within the appropriate range of the same vehicle type and driving environment data as the target vehicle (S220).

[0059] If the actual driving result data is within an appropriate range, i.e., if the actual driving result data does not deviate from the appropriate range ("No" in S220), the fault determination unit 44 considers that there is no fault in the target vehicle and reports that there is no fault (S230). Conversely, if the actual driving result data deviates from the appropriate range ("Yes" in S220), the fault determination unit 44 considers that there is a fault in the target vehicle and reports that there is a fault (S240). The fault determination unit 44 may also display whether there is a fault on a display (not shown) of the management server 12, for example.

[0060] Figure 5This diagram illustrates the vehicle simulation model 36. The vehicle simulation model 36 includes models simulating various functions of the vehicle 10, such as an engine model, a transmission model, or a hybrid model. Furthermore, the models simulating these functions include one or both of a control model and a plant model. The control model is software identical to the control program used in the actual vehicle 10. The plant model is software simulating physical phenomena or mechanisms such as the operation of reciprocating cylinders. The vehicle simulation model 36 can be formed through machine learning, using driving environment data as input test data and actual driving result data as output test data.

[0061] The driving environment data of vehicle 10 is input into vehicle simulation model 36 as input data. As described above, the driving environment data includes driving operation data representing driving operations and external environment data representing the external environment. If driving environment data is input, vehicle simulation model 36 internally simulates various functions and outputs virtual driving result data representing virtual driving results.

[0062] In addition, various parameters are set in the vehicle simulation model 36. Parameters are data that are used directly or indirectly in the process of deriving virtual driving result data from driving environment data. Parameters can be variables that change according to driving environment data or constants inherent to the vehicle 10. Examples of parameters include throttle opening, engine ignition timing, fuel injection quantity, EGR flow rate, or clutch friction coefficient.

[0063] Depending on the specific type of parameter, the parameter is associated with a specific part of the vehicle 10. Hereinafter, the part of the vehicle 10 associated with the parameter is sometimes referred to as the parameter-associated part. It should be noted that multiple parameter-associated parts can be associated with a single parameter.

[0064] The parameter-related part refers to the various elements that constitute the vehicle 10, such as mechanisms, parts, components, circuits, or software. For example, if the parameter is the clutch friction coefficient, then the parameter-related part is the clutch. Similarly, if the parameter is, for example, the engine ignition timing, then the parameter-related part is the spark plug.

[0065] Figure 6 This is a diagram illustrating the relationship between the faults of vehicle simulation model 36 and vehicle 10. Figure 6 (A) and Figure 6 (B) represents the relationship between the actual driving result data and the virtual driving result data under the condition that there are no faults in the object vehicle. Figure 6 (C) and Figure 6 (D) represents the relationship between actual driving result data and virtual driving result data when a fault exists in the object vehicle.

[0066] like Figure 6 As shown in (A), vehicle simulation model 36 simulates a vehicle 10 that is fault-free and in normal condition. In this state, the parameters of vehicle simulation model 36 become normal values. Since the driving environment data reflects normal parameters, vehicle simulation model 36 outputs normal virtual driving result data.

[0067] Assuming there are no faults in the target vehicle, the actual driving results data obtained from the target vehicle should be normal values. Therefore, if the actual driving environment data provided to the target vehicle is the same as the driving environment data input into the vehicle simulation model, then... Figure 6 As shown in (A), the virtual driving result data is consistent with the actual driving result data.

[0068] For example, such as Figure 6 As shown in (B), for the driving environment data when the engine starts, the actual driving result data of the normal object vehicle shown by the solid line B10 is consistent with the virtual driving result data shown by the dashed line C10.

[0069] It should be noted that, regarding the consistency between virtual driving result data and actual driving result data, differences may exist within a predetermined range that allows for the measurement error of the actual vehicle 10 or the calculation error of the vehicle simulation model 36.

[0070] Figure 6 The vehicle simulation model 36 of (C) and Figure 6 Similarly, in vehicle simulation model 36 of (A), the parameters become normal values, and the output is normal virtual driving result data. However, in Figure 6 In (C), it is assumed that a fault exists in the target vehicle. In this case, the actual driving result data obtained from the target vehicle should differ from the normal data. Therefore, even if the actual driving environment data provided to the target vehicle is the same as the driving environment data input to the vehicle simulation model 36, such as Figure 6 As shown in (C), the virtual driving result data is inconsistent with the actual driving result data.

[0071] For example, such as Figure 6 As shown in (D), for the driving environment data when the engine starts, the actual driving result data of the faulty object vehicle shown by the solid line A10a is inconsistent with the normal virtual driving result data shown by the dashed line C10.

[0072] Based on these assumptions, if the virtual driving result data can be made consistent with the actual driving result data of the faulty vehicle, then the faulty vehicle can be simulated using vehicle simulation model 36. Therefore, the faulty part of the faulty vehicle can be determined subsequently.

[0073] Figure 7 This is a diagram illustrating the relationship between vehicle simulation model 36 and the determination of the faulty part. Figure 7 (A) and Figure 7 (B) represents an example of the process for identifying the faulty part. Figure 7 (C) and Figure 7 (D) indicates an example where the faulty part has been identified. Figure 7 (A) Figure 7 In (D), it is assumed that the driving environment data input to the vehicle simulation model 36 is the same as the actual driving environment data provided to the faulty object vehicle.

[0074] like Figure 7 As shown in (A), the fault determination unit 46 intentionally alters arbitrary parameters of the vehicle simulation model 36 from a normal state. This parameter alteration is equivalent to causing the vehicle simulation model 36 to simulate a state different from the normal state of the vehicle 10. Consequently, the vehicle simulation model 36 outputs virtual driving result data reflecting the altered parameters. This virtual driving result data reflects values ​​different from normal virtual driving result data.

[0075] The fault determination unit 46 determines whether the virtual driving result data after parameter changes is consistent with the actual driving result data of the faulty vehicle. Figure 7 The dashed line C20 in (B) represents an example of virtual driving result data after changing any parameters. Figure 7 In (B), although any parameter is changed, the virtual driving result data shown by the dashed line C20 is still inconsistent with the actual driving result data shown by the solid line A10a.

[0076] Therefore, as Figure 7 As shown in (C), the fault determination unit 46 further changes the parameters. Additionally, the fault determination unit 46 may also change other parameters. If the parameters are further changed, the virtual driving result data is also further changed. Furthermore, the fault determination unit 46 again determines whether the virtual driving result data after the parameter changes is consistent with the actual driving result data of the vehicle with the fault.

[0077] like Figure 7 As shown in (C), it is assumed that the virtual driving result data after parameter changes is consistent with the actual driving result data. For example, as Figure 7As shown in (D), assume that the virtual driving result data shown by the dashed line C30 is consistent with the actual driving result data shown by the solid line A10a. In this case, the vehicle simulation model 36 is considered to be simulating a faulty object vehicle. Therefore, the changed parameters of the vehicle simulation model 36 are equivalent to the parameters that have changed due to the fault.

[0078] Therefore, the fault determination unit 46 repeatedly changes the parameters of the vehicle simulation model 36 and derives virtual driving result data until the virtual driving result data matches the actual driving result data of the target vehicle. Furthermore, the fault determination unit 46 determines the portion of the virtual driving result data that matches the actual driving result data of the target vehicle and that is related to the changed parameters of the vehicle simulation model 36 as the fault portion.

[0079] Regarding the consistency between virtual driving result data and actual driving result data, differences may exist, for example, within a predetermined range that allows for the degree of measurement error of the target vehicle or the computational error of the vehicle simulation model 36.

[0080] It should be noted that if the absolute value of the value obtained by subtracting the actual driving result data from the virtual driving result data is within a predetermined range, the fault determination unit 46 can determine that the virtual driving result data and the actual driving result data are consistent. Furthermore, if the mean square value obtained by averaging the square of the value obtained by subtracting the actual driving result data from the virtual driving result data over a predetermined time range is less than a predetermined value, the fault determination unit 46 can also determine that the virtual driving result data and the actual driving result data are consistent.

[0081] In the fault diagnosis system 1, multiple test schemes are pre-set for the server control unit 34. Each test scheme is set according to a conceivable fault mode. In each test scheme, the types and amounts of parameters to be changed in the vehicle simulation model 36 are associated.

[0082] The fault determination unit 46 determines the priority order of test plans based on actual driving result data indicating a fault and driving environment data that forms the basis of that actual driving result data. For example, based on the driving environment data, the fault determination unit 46 roughly classifies the faults of the target vehicle into major categories such as whether it is a fault in the drive system, a fault in the steering system, or a fault in the electrical system. For example, if the driving environment data is from when the engine is starting, the fault determination unit 46 classifies the fault of the target vehicle as a fault in the drive system.

[0083] Multiple medium-sized items are associated with large-sized items. For example, transmission faults, gear shifting faults, and torque variation faults are associated with drive system faults. The fault determination unit 46 analyzes and judges actual driving result data that indicates the presence of faults, and classifies the faults of the target vehicle according to the medium-sized items.

[0084] For example, in the actual driving data of the target vehicle, if there is a mismatch in the rotational speeds of the various parts of the axle as estimated based on the power source, the fault determination unit 46 classifies the fault of the target vehicle as a transmission fault. Furthermore, if it is estimated that there is no transmission fault but rotational variation occurs in the transmission mechanism, the fault determination unit 46 classifies the fault of the target vehicle as a transmission fault. Additionally, if it is estimated that neither a transmission fault nor a transmission fault occurs, the fault determination unit 46 classifies the fault of the target vehicle as a torque variation fault.

[0085] Multiple sub-items are associated with intermediate items. For example, throttle, ignition, fuel, EGR, and variable valve timing are associated with torque variation faults. The fault determination unit 46 compares the rate of change of physical quantities in the actual driving result data of the target vehicle with the rate of change that can be physically generated in the sub-items. The fault determination unit 46 assigns a priority order to the sub-items according to their rate of change, from the rate of change closest to that of the target vehicle to the rate of change furthest from the target vehicle.

[0086] For example, assume the rate of change of the physical quantity of the actual driving result data of the target vehicle is the rate of change of engine speed, which is 3500 rpm / sec. Additionally, assume the rate of change of engine speed due to torque changes related to the throttle is 1000 rpm / sec. Assume the rate of change of engine speed related to ignition is 4000 rpm / sec. Assume the rate of change of engine speed related to fuel is 2500 rpm / sec. Assume the rate of change of engine speed related to EGR is 2000 rpm / sec. Assume the rate of change of engine speed related to variable valve timing is 1000 rpm / sec. In this example, the fault determination unit 46 determines the priority order according to the sequence of ignition, fuel, EGR, variable valve timing, and throttle.

[0087] These sub-items are associated with the test plan. That is, the fault determination unit 46 determines the priority of the test plans by assigning priority to the sub-items. In the previous example, the fault determination unit 46 prioritized the test plan related to ignition.

[0088] The fault location determination unit 46 executes the tests sequentially, starting with the highest priority test plans. Specifically, the fault location determination unit 46 changes the parameters shown in the highest priority test plans within the vehicle simulation model 36. For example, the fault location determination unit 46 changes the parameter shown in the ignition-related test plan, namely the ignition timing. Therefore, compared to randomly changing parameters, the fault location determination unit 46 can determine the fault location earlier.

[0089] Figure 8 This is a flowchart illustrating the operation of the fault determination unit 46. When the fault determination unit 44 determines that a fault exists in the target vehicle, the fault determination unit 46 performs... Figure 8 A series of processes.

[0090] First, the fault determination unit 46 determines the priority order of test plans based on actual driving result data indicating a fault and driving environment data that forms the basis of that actual driving result data (S300). Next, the fault determination unit 46 selects the test plan with the highest priority as the test plan to be executed (S310). Then, the fault determination unit 46 changes the parameters of the vehicle simulation model 36 corresponding to the test plan determined in step S310 (S320).

[0091] Next, the fault determination unit 46 inputs the driving environment data of the target vehicle into the vehicle simulation model 36, whose parameters were changed in step S320, and exports virtual driving result data (S330). Then, the fault determination unit 46 determines whether the actual driving result data of the target vehicle is consistent with the exported virtual driving result data (S340).

[0092] If the actual driving result data of the target vehicle is consistent with the virtual driving result data ("Yes" in S340), the fault part determination unit 46 determines the part related to the parameter changed in step S320 as the fault part (S350) and ends a series of processes.

[0093] If the actual driving result data of the target vehicle is inconsistent with the virtual driving result data ("No" in S340), the fault determination unit 46 determines whether the test plan in the execution process has ended (S360). If the test plan in the execution process has not ended ("No" in S360), the fault determination unit 46 returns to step S320 and further changes the parameters (S320).

[0094] If the test plan in the execution process ends ("Yes" in S360), the fault determination unit 46 determines whether there is a subsequent test plan (S370). If there is a subsequent test plan ("Yes" in S370), the fault determination unit 46 returns to the processing of step S310 and determines the test plan with the second highest priority as the test plan to be executed (S310).

[0095] If no further test plan is available ("No" in S370), the fault determination unit 46 reports that the faulty part cannot be determined (S380) and ends the series of processes.

[0096] As described above, the appropriate range setting unit 42 of the fault diagnosis system 1 in this embodiment derives an appropriate range of actual driving results based on driving environment data for vehicles of the same type as the target vehicle. Furthermore, the fault determination unit 44 determines whether the target vehicle has a fault by judging whether the actual driving result data of the target vehicle is included within the appropriate range. In the fault diagnosis system 1 of this embodiment, since a fault is not judged if the actual driving result data of the target vehicle is within the appropriate range, even if there are individual differences in the vehicle 10 due to manufacturing defects, misdiagnosis of faults can be suppressed.

[0097] Therefore, the fault diagnosis system 1 according to this embodiment can improve the accuracy of fault diagnosis of vehicle 10.

[0098] Furthermore, when a fault is determined in the target vehicle, the fault part determination unit 46 of the fault diagnosis system 1 in this embodiment changes the parameters of the vehicle simulation model and repeatedly derives virtual driving result data. The fault part determination unit 46 then determines the portion of the virtual driving result data that matches the actual driving result data, and that is related to the changed parameters of the vehicle simulation model, as the fault part.

[0099] Therefore, in the fault diagnosis system 1 of this embodiment, since it is possible not only to determine whether there is a fault, but also to determine the faulty part, the accuracy of fault diagnosis can be further improved.

[0100] It should be noted that the appropriate range setting unit 42 of this embodiment sequentially derives the appropriate range when obtaining driving environment data and actual driving result data from any vehicle 10. However, the appropriate range setting unit 42 may also omit the derivation of the appropriate range when obtaining driving environment data and actual driving result data, and instead derive the appropriate range when receiving the fault diagnosis start instruction.

[0101] Furthermore, the appropriate range setting unit 42 of this embodiment takes all vehicles 10, including the target vehicle, as objects and derives appropriate ranges for each vehicle type and driving environment data. Since the total set used to derive the appropriate range is large, even if the derived appropriate range reflects the actual driving result data of the target vehicle, the appropriate range can be derived with high accuracy. In addition, when the target vehicle is distinguished in advance, the appropriate range setting unit 42 can also take other vehicles besides the target vehicle as objects and derive appropriate ranges for each vehicle type and driving environment data.

[0102] While embodiments of the present invention have been described above with reference to the accompanying drawings, the present invention is not limited to these embodiments. It should be understood that those skilled in the art will readily recognize various variations or modifications within the scope of the claims, and these also fall within the technical scope of the present invention.

Claims

1. A fault diagnosis system, characterized in that, have: The data acquisition unit acquires data by associating driving environment data with actual driving result data. The driving environment data includes driving operation data of the target vehicle that is the subject of fault diagnosis, which at least represents driving operations. The actual driving result data represents the actual driving result based on the driving environment data. The appropriate range setting unit derives an appropriate range of actual driving results based on the driving environment data for vehicles of the same type as the target vehicle. The fault diagnosis unit determines whether the target vehicle has a fault by judging whether the actual driving result data is included in the appropriate range; Vehicle simulation model, which simulates the operation of a vehicle on a computer; as well as The fault determination unit, when the fault judgment unit determines that the target vehicle has a fault, repeatedly changes the parameters of the vehicle simulation model and performs a simulation by setting the driving environment data as the input data of the vehicle simulation model to derive virtual driving result data representing virtual driving results, and determines the part of the virtual driving result data that is consistent with the actual driving result data and is related to the changed parameters of the vehicle simulation model as the fault part.

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