JPH05116601A - Trouble diagnosis method - Google Patents

Abstract

PURPOSE:To accurately diagnoze trouble covering a wide range in a vehicle of which a plurality of functional parts are mutually related. CONSTITUTION:In a vehicle of which a plurality of functional parts comprising sensors 2-9 are provided and they are mutually related, weight setting against mutually related two functional parts are respectively set according to the degree of relation, the detected value of each sensor is converted to the quantity of state to indicate the degree of abnormality, these quantities of state are weightedly computed from respective sensors as starting points, at every system of related functional parts, according to the degree of relation between functional parts, and hence judgement for trouble of the functional parts is performed based on the computed result of every system.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a failure diagnosis method, and more particularly to a vehicle failure diagnosis method including a plurality of functional parts including a sensor, and the functional parts being associated with each other.

[0002]

2. Description of the Related Art In vehicles and the like, various devices are being electronically controlled along with the rapid development of electronics. Adopting electronic control has the advantage of improving the reliability of the system, but on the contrary, when the system is abnormal, the computerized part becomes a black box,
There is another aspect that it is difficult to identify the location of the failure from the outside.

In order to solve such a problem, it is considered to incorporate a failure diagnosis function into the system as disclosed in, for example, Japanese Patent Laid-Open No. 61-107436.
In this system, a control unit that controls the system outputs a predetermined failure diagnosis signal, and when a predetermined response signal cannot be obtained, it is determined as a failure.

[0004]

By the way, in a complicated system in which a plurality of functional parts such as a sensor and an actuator are related to each other such as an engine, the diagnostic accuracy is insufficient by the conventional failure diagnosis method. There is a problem of doing. For example, taking an engine as an example, the engine has a system configuration in which an intake system, a fuel system, an ignition system, an exhaust system, and the like are interconnected with each other with respect to the engine main body. Even if the engine is out of order, an abnormality in the intake system affects the engine body and causes knock, or the effect of the abnormality affects the exhaust system and raises the temperature of the exhaust gas. Therefore, even if an abnormality occurs in the exhaust temperature, it cannot be concluded that the exhaust system has failed.

To solve such a problem, failure diagnosis data in which sensor information and the like are patterned based on the knowledge obtained by experience of a service person or the like is stored in advance in a control unit or the like, and pattern matching is performed. Although it is considered that the failure diagnosis is performed by using the above method, there is a problem that the failure range that cannot be experienced cannot be dealt with and the diagnosis range is limited.

[0006] The present invention addresses the above problems in vehicle failure diagnosis in which a plurality of functional parts are associated with each other, and it is an object of the present invention to enable the failure diagnosis to be performed accurately over a wide range.

[0007]

That is, a failure diagnosis method according to claim 1 of the present application is provided with a plurality of functional parts including a sensor, and these functional parts are associated with each other in a vehicle. Each functional component is weighted according to the degree of association, and the detected value for each sensor is converted into a state quantity that indicates the degree of abnormality, and these state quantities are used as starting points for the related functional components. A feature is that weighting calculation is performed according to the degree of association between functional components for each system, and failure determination of the functional components is performed based on the calculation result for each system. Here, the functional component means a component or an assembly of a plurality of components that achieve a certain function regardless of electrical or mechanical.

The failure diagnosis method according to claim 2 of the present application is the failure diagnosis method according to claim 1, wherein the state quantity indicating the degree of abnormality based on the detection value of the sensor is determined according to the degree of association between the functional parts. When performing weighting calculation, use the same system. When an overlapping operation is performed in the process of the operation processing in the above, the operation processing in the system is terminated at that point.

The failure diagnosis method according to claim 3 of the present application is the failure diagnosis method according to claim 1, wherein when the state quantity indicating the degree of abnormality based on the detection value of the sensor is smaller than a predetermined set value, The feature is that weighting calculation is not performed for the state quantity.

Further, in the failure diagnosing method according to claim 4 of the present application, in the failure diagnosing method according to claim 1, the magnitudes of the numerical values indicating the degree of abnormality based on the detection value of the sensor are compared, and the numerical value is large. The feature is that the weighting operation is performed in order from the one.

Further, a failure diagnosis method according to claim 5 of the present application is provided with a plurality of functional components including a sensor, and in a vehicle in which these functional components are mutually associated, two functional components associated with each other are provided. In addition to setting the weighting according to the degree of association, convert the detected value for each sensor into the state quantity that indicates the degree of abnormality, and use these state quantities as the starting point of each sensor for the functional component system The weighting calculation is performed according to the degree of relevance, the functional component failure determination is performed based on the calculation result for each system, and the determination result is displayed.

[0012]

According to the failure diagnosis method of the first aspect, the state quantity indicating the degree of abnormality obtained from the detection value of the sensor is determined for each system of the functional parts that are related to each other from the sensor as a starting point, and the degree of association between the functional parts is calculated. Since the weighting calculation is performed in accordance with the above, and the failure determination of the functional component is performed based on the calculation result for each system, the failure diagnosis can be performed accurately over a wide range.

Further, according to the failure diagnosis method of the second aspect, even when the functional parts are related in a loop, the overlapping operation is not performed, so that the diagnostic accuracy is improved.

Further, according to the failure diagnosis method of the third aspect, when the state quantity indicating the degree of abnormality based on the detected value of the sensor is small, the weighting operation is not performed on the state quantity, so that the calculation time is shortened. Will be.

Further, according to the failure diagnosis method of the fourth aspect, since the weighting operation is performed in order from the one having the largest state quantity indicating the degree of abnormality, it is possible to quickly narrow down the failed functional parts. Therefore, even if a situation arises in which the calculation must be aborted midway, it is possible to estimate the functional component that has failed with high accuracy.

According to the failure diagnosis method of the fifth aspect, the failure diagnosis can be performed accurately over a wide range, and the result of the failure diagnosis is displayed, so that the maintainability is improved. Also becomes.

[0017]

EXAMPLES Examples of the present invention will be described below.

As shown in FIG. 1, the engine control unit 1 mounted on the vehicle includes a throttle sensor 2
The throttle opening signal detected by the air flow sensor 3, the intake air amount signal detected by the air flow sensor 3, the intake air temperature signal detected by the intake air temperature sensor 4, the crank angle signal detected by the crank angle sensor 5, and the water temperature. The engine water temperature signal detected by the sensor 6, the exhaust temperature signal detected by the exhaust temperature sensor 7, the air-fuel ratio signal detected by the O ₂ sensor 8, and the knock signal detected by the knock sensor 9 are input. At the same time, the engine control unit 1 transfers each of these signals to the failure diagnosis unit 10.

On the other hand, the failure diagnosis unit 10 has a communication interface 11 and a membership function indicating the degree of abnormality set corresponding to the detection values of the sensors 2 to 9 as shown in FIGS. A function storage unit 12 stored therein, a knowledge data storage unit 13 storing knowledge data for failure diagnosis, an arithmetic processing unit 14 for inferring a failure based on sensor information fetched via the interface 11, The inference result storage unit 15 stores the inference result. A display device 16 for displaying a failure is connected to the failure diagnosis unit 10.

Now, the membership function stored in the function storage unit 12 will be described. For the throttle sensor 2, for example, as shown in FIG. 2, the sensor output voltage and the membership value M indicating the degree of abnormality are compared. The relationship is set. That is, the sensor output voltage is the predetermined value V ₁
The membership value M tends to increase sharply around this point.

Similarly, the air flow sensor 3, the intake air temperature sensor 4, the crank angle sensor 5, the water temperature sensor 6, the exhaust gas temperature sensor 7, the O ₂ sensor 8 and the knock sensor 9 are also as shown in FIGS. Each membership function is set.

Further, in the knowledge data storage section 15, for example, as shown in FIG. 10, a throttle system A, a canister B, a bypass air system C, an alternator D, an intake system E, a fuel, which are functionally engineered blocks, are provided. System F, ignition system G, engine body H, exhaust system I, cooling system J, EGR system K
Also, a rule that represents a correlation path that represents the correlation between the sensors 2 to 9 and a correlation coefficient that is set according to the degree of association for each rule are stored. For example, the throttle sensor 2 affected by the throttle system A and the throttle system A, which is the influence source, are connected by a correlation route of the rule R1 starting from the throttle system A, and a correlation coefficient indicating the degree of association between the two. The value (0.88) is stored in the knowledge data storage unit 13 with the rule R1 as a calling code.

The relationship between each of these rules and the correlation coefficient is summarized as shown in Table 1 below.

[0024]

[Table 1] That is, for example, the value of the correlation coefficient corresponding to the rule R2 indicating the correlation between the throttle system A and the air flow sensor 3 is 0.76.

Next, the failure diagnosis processing performed by the failure diagnosis unit 10 will be described. This failure diagnosis processing is performed as follows according to the flowchart of FIG.

That is, the arithmetic processing unit 14 in the failure diagnosis unit 10 makes the interface 1 in step S1.
After reading the sensor values via 1, the sensor values are converted into membership values M ... M in step S2. That is, the actual sensor value captured via the interface 11 is compared with the membership function stored in the function storage unit 12, and the value of the membership function corresponding to the corresponding sensor value is selected as the membership value M. To do. For example, if the output voltage indicated by the signal from the throttle sensor 2 is equal to 2V, the membership value M indicating the degree of abnormality is shown from the relationship of FIG.
Will be selected as 1.

Next, the arithmetic processing section 14 executes step S3 to sort the membership value M. That is, the membership values M are compared, and the membership values M are rearranged in order from the largest.

Then, the arithmetic processing unit 14 proceeds to step S4 to determine whether or not the inference processing for all the sensors has been completed. When the determination result is NO, the arithmetic processing portion 14 proceeds to step S5 and rearranges in step S3. It is determined whether the maximum membership value M is larger than a predetermined minimum determination value M ₀ from the group of membership values M ... M, and Y
When it is determined to be ES, a predetermined inference process is executed in step S6. That is, for example, if the membership value M by the throttle sensor 2 is the largest, it is the throttle system A that is associated with the throttle sensor 2 according to the correlation model of FIG.
The inference value R of is the value obtained by multiplying the membership value M by the correlation coefficient according to the rule R1.

Next, the arithmetic processing unit 14 determines in step S7 whether the inferred value R is larger than the reference value R ₀ ,
When it is determined to be YES, the inference value R is stored in the result table provided for each block in the inference result storage unit 15 in step S8, and then the process returns to step S6 to continue the inference process and infer in step S7. When it is determined that the value R is smaller than the reference value R ₀ , step S
In step 9, it is determined whether or not there is another correlation path, and when YES is determined, the process returns to step S6 and the inference process is continued. That is, in the correlation model of FIG. 11, for example, the above process is executed until one round of a series of independent correlation paths ending at the throttle sensor 2 is completed. In that case, when the inference process that started from a particular sensor begins to use the same rules again, at that point the set of inference operations for that sensor is terminated. For example, focusing on the throttle system A and the intake system E, the rule R is used in the process of obtaining the inferred value of the intake system A.
When 9 is used, this rule R9 is never used again. As a result, the infinite loop is avoided, the calculation time is shortened, and the accumulation of the error due to the overlapping calculation is also avoided.

When it is determined in step S9 that there is no other correlation path, the arithmetic processing unit 14 returns to step S4 and determines again whether or not the inference processing for all the sensors has been completed, and YES is determined in step S4. Inference processing is executed in order from the largest membership value M until it is determined or the membership value M is smaller than the minimum determination value M ₀ in the subsequent step S5.
As a result, as shown in FIGS. 12 and 13, a result table accumulating the inference values R ... R for each block is obtained for each sensor.

Then, the arithmetic processing section 14 carries out step S10.
Then, a predetermined tabulation process is executed. That is, the inference values R ... R stored in the result table for each sensor are added for each block, and those values are added for each block to the final result table.

The arithmetic processing unit 14 determines whether or not the totalized value S stored in the final result table in step S11 is larger than a predetermined reference value S _{0. If} YES, the process proceeds to step S12 and the display device 16 is operated. Let the fault be displayed. That is, as shown in FIG. 14, when the total value S of the intake system E is the largest and is larger than the reference value S ₀ , it is determined that the intake system E is out of order, and a display to that effect is displayed on the display device 16. Will be output to.

Further, when it is determined that there is a failure, the fail safe mode may be entered.

As shown in FIG. 15, in a service factory or the like, a personal computer 17 is provided, an interface 19 attached to a main body 18 of the computer 17 and an engine control unit 1 mounted on a vehicle 20. Failure diagnosis unit 10 connected to
The diagnosis result may be displayed on the display 23 by communicating with and via the diagnostic connector 21 and the cable 22. Then, for example, when the exhaust temperature sensor 7, the intake temperature sensor 4, and the knock sensor 9 have a large degree of abnormality and the failure diagnosis unit 10 determines that the intake system A has failed, as shown in FIG. An output screen showing the route to the affected sensor will be displayed on the display 23. As a result, the diagnosis result can be understood at a glance.

Further, as shown in FIG. 17, the engine control unit 1 is provided with a communication interface 24.
Is provided to connect the interface 24 to the multiplex transmission line 30 to which the EAT node 25, the ABS node 26, the meter node 27, the air conditioner node 28, the power steering node 29, etc. are connected, and the failure diagnosis unit 10
The interface 11 provided in the above may be connected to the multiplex transmission line 30 so that various data necessary for failure diagnosis may be received via this multiplex transmission line 30. By doing so, it becomes possible to perform accurate failure diagnosis for the entire vehicle over a wide range.

[0036]

As described above, according to the failure diagnosing method of the first aspect of the present invention, the state showing the degree of abnormality obtained from the detection value of the sensor for each system of the functional parts related to each other from the sensor as a starting point. Since the quantity is weighted according to the degree of association between the functional parts, and the malfunction of the functional parts is determined based on the calculation result for each system,
It is possible to perform fault diagnosis accurately over a wide range.

Further, according to the failure diagnosis method of the second aspect, even if the functional parts are related in a loop, the overlapping operation is not performed, so that the diagnostic accuracy is improved.

According to the failure diagnosis method of the third aspect, when the state quantity indicating the degree of abnormality based on the detection value of the sensor is small, the weighting operation is not performed for the state quantity, so that the calculation time is shortened. Will be.

Further, according to the failure diagnosis method of the fourth aspect, since the weighting calculation is performed in order from the one having the largest state quantity indicating the degree of abnormality, it is possible to narrow down the functional parts that fail early. Therefore, even if a situation arises in which the calculation must be aborted midway, it is possible to estimate the functional component that has failed with high accuracy.

According to the failure diagnosis method of the fifth aspect, the failure diagnosis can be performed accurately over a wide range, and the result of the failure diagnosis is displayed, so that the maintainability is improved. Also becomes.

[Brief description of drawings]

FIG. 1 is a block diagram showing a failure determination system in a first embodiment.

FIG. 2 is a characteristic diagram showing an example of a membership function indicating the degree of failure with respect to the output state of the throttle sensor.

FIG. 3 is a characteristic diagram showing an example of a membership function indicating the degree of failure with respect to the output state of the air flow sensor.

FIG. 4 is a characteristic diagram showing an example of a membership function indicating the degree of failure with respect to the output state of the intake air temperature sensor.

FIG. 5 is a characteristic diagram showing an example of a membership function indicating the degree of failure with respect to the output state of the crank angle sensor.

FIG. 6 is a characteristic diagram showing an example of a membership function indicating the degree of failure with respect to the output state of the water temperature sensor.

FIG. 7 is a characteristic diagram showing an example of a membership function indicating the degree of failure with respect to the output state of the exhaust temperature sensor.

FIG. 8 is a characteristic diagram showing an example of a membership function indicating the degree of failure with respect to the output state of the O ₂ sensor.

FIG. 9 is a characteristic diagram showing an example of a membership function indicating the degree of failure with respect to the output state of the knock sensor.

FIG. 10 is a correlation diagram showing a structural model of the engine.

FIG. 11 is a flowchart showing a failure determination process.

FIG. 12 is a conceptual diagram showing an example of a result table accommodating inference results starting from one sensor.

FIG. 13 is a conceptual diagram showing an example of a result table that stores inference results from another sensor as a starting point.

FIG. 14 is an artistic diagram showing an example of a final result table in which final inference results are stored.

FIG. 15 is a schematic diagram showing an example of a case where a failure diagnosis result is externally monitored.

FIG. 16 is a schematic diagram showing a display example of a failure diagnosis result.

FIG. 17 is a block diagram of a failure diagnosis system showing a second embodiment of the present invention.

[Explanation of symbols]

2 Throttle sensor 3 Air flow sensor 4 Intake temperature sensor 5 Crank angle sensor 6 Water temperature sensor 7 Exhaust temperature sensor 8 O ₂ sensor 9 Knock sensor 10 Failure diagnosis unit 14 Arithmetic processing unit A Throttle system B Canister C Bypass air system D Alternator E Intake system F Fuel system G Ignition system H Engine body I Exhaust system J Cooling system K EGR system

Claims (5)

[Claims] 1. A method of diagnosing a vehicle failure, comprising a plurality of functional components including a sensor, wherein the functional components are associated with each other, wherein two functional components associated with each other are weighted according to a degree of association. While setting each, convert the detection value for each sensor into a state quantity indicating the degree of abnormality,
From these state quantities, each sensor is used as a starting point to perform a weighting calculation according to the degree of association between the functional parts for each system of the related functional parts, and to determine the malfunction of the functional part based on the calculation result for each system. Characteristic failure diagnosis method. 2. The fault diagnosing method according to claim 1, wherein when a state quantity indicating an abnormality degree based on a detection value of a sensor is weighted according to a degree of association between functional parts, arithmetic processing in the same system is performed. The method for diagnosing a failure is characterized in that, when an overlapping operation is performed in the process of, the operation processing in the system is terminated at that point. 3. The fault diagnosing method according to claim 1, wherein when the state quantity indicating the degree of abnormality based on the detection value of the sensor is smaller than a predetermined set value, the weighting calculation for the state quantity is not performed. Failure diagnosis method. 4. The failure diagnosis method according to claim 1, wherein the magnitudes of the numerical values indicating the degree of abnormality based on the detected values of the sensors are compared, and the weighting operation is performed in order from the largest numerical value. Failure diagnosis method. 5. A vehicle failure diagnosis method comprising a plurality of functional components including a sensor, wherein the functional components are associated with each other, wherein two functional components associated with each other are weighted according to a degree of association. While setting each, convert the detection value for each sensor into a state quantity indicating the degree of abnormality,
These state quantities are weighted according to the degree of association between the functional parts for each system of the related functional parts with each sensor as a starting point, and the failure determination of the functional parts is performed based on the calculation result for each system, A failure diagnosis method characterized by displaying the determination result.