JPH0332967A - Troubleshooting device for vehicle - Google Patents

Abstract

PURPOSE:To accurately detect checking portions corresponding to trouble status by inferring general trouble information common to each type of vehicles, specific trouble information applicable to specific types of vehicles and the probability of respective information on the basis of the trouble symptoms of the vehicles. CONSTITUTION:Among casual relations of parts and trouble symptoms due to the failure of the parts, a general casual relation common to each type of vehicles is saved in the first memory part 5, and a specific casual relation applicable to specific types of vehicles is saved in the second memory part 6. On the other hand, the trouble symptom of the vehicle is inputted via an input part A. Also, parts troubled in the general casual relation and the probability thereof, and parts troubled in the specific casual relation and the probability thereof are respectively inferred at the inference part 9 on the basis of inputted trouble symptoms. Furthermore, one of function chain systems among a plurality of parts saved in the third memory part 7 is selected with a select part 10. Finally, the inferred result and selected function chain system are respectively announciated at an announciation part 11.

Description

DETAILED DESCRIPTION OF THE INVENTION (Field of Industrial Application) The present invention relates to a vehicle failure diagnosis device that allows the cause of a failure, that is, the location of the failure, to be known based on the symptoms of the failure of the vehicle.

(Prior Art) Modern vehicles tend to have increasingly complex structures. For this reason, when a vehicle is malfunctioning, that is, when a vehicle malfunction occurs, it is becoming increasingly difficult for even experienced mechanics to identify the part that is causing the malfunction. . This places a large burden on prompt repair of failures.

For this reason, recently, as shown in Japanese Unexamined Patent Publication No. 62-6856, a system has been proposed that uses what is called an expert system to identify the cause of a failure based on its symptoms. . Based on past data, the cause-and-effect relationship between failure symptoms and component failures that cause them is stored as a failure tree, and by manually inputting the failure symptoms one by one, it is possible to finally identify the failed parts. It searches for and displays this. By also storing the probability of a causal relationship between a failure symptom and the component failure that causes it, it is possible to locate a malfunctioning component with higher accuracy.

(Problems to be Solved by the Invention) However, with the conventional vehicle failure diagnosis device described above, not only does it take a large amount of time to finally locate the malfunctioning part, but also the quality of the failure symptoms input is If the number and quantity are not sufficient, an extremely large number of parts with the same failure probability will be displayed. This means that
This will significantly reduce the utility value of this type of device.

SUMMARY OF THE INVENTION Accordingly, an object of the present invention is to provide a vehicle failure diagnosis apparatus that can more accurately provide information to a person who maintains a part that is the cause of a failure in accordance with an input failure symptom.

(Means and operations for solving the problems) In order to achieve the above-mentioned object, the following aspects are taken into consideration in the present invention.

First, as data for failure symptoms, in addition to data that is common to each car model in consideration of versatility, there is data for specific car models, such as those that have been sold for a limited time or those that have just been released as new cars. Be sure to prepare the following items. In other words, conventional fault diagnosis data has to be statistically generalized, so while it is excellent in terms of generality, it is difficult to properly diagnose faults for specific vehicle models. It will not be done.

More specifically, when a certain failure symptom occurs, the failure part that is the cause of the failure symptom may be impossible to diagnose based on data common to each car model, or it may be impossible to diagnose the failure part based on data common to each vehicle model, or it may be difficult to diagnose the failure part even though the reliability of the failure is low. Even if it is a part,
If it is based on data for a specific vehicle type, a completely different B part can be diagnosed as having an extremely high probability of failure. A specific configuration of the present invention having such data common to each vehicle type and data for a specific vehicle type is shown in claim 1.

Second, in the present invention, in addition to the data showing the causal relationship between failure symptoms and the failure parts that cause them, a functional chain system is prepared as data, and by notifying the maintenance person of this data, the failure part Even if it cannot be clearly diagnosed, the maintenance personnel can easily understand which parts should be inspected. This functional chain system shows the connections between subsystem-level systems such as the ignition system and fuel system, that is, the assembly of multiple parts related to realizing a certain function of the vehicle. FIG. 15 shows a block diagram of the ignition system as an example. By looking at such a functional chain system, a maintenance person can more appropriately know which parts and items should be inspected. The selection of this functional linkage system can be made in various ways.

For example, a mechanic can manually select the
Furthermore, it is also possible to automatically select and notify those with a high degree of relevance based on the causal relationship with the failure symptoms. Preferably, if the data on this functional chain system includes the degree (probability) of failure of the parts that make up this system, the number of parts to be inspected for one functional chain system can be small. It turns out.

The most preferred embodiment of the present invention is illustrated in a block diagram in a first embodiment.
This is shown in Figure 6. That is, Fig. 16 shows the causal relationship between failure symptoms common to each vehicle model and the parts that cause them, the causal relationship between failure symptoms and parts that cause them for specific vehicle models, and the relationship between failure symptoms and their causes. This system utilizes the causal relationship with a specific collection of parts, that is, the functional chain system, and the data of the functional chain system itself.

(Effects of the Invention) As described above, according to the present invention, it is possible to more accurately know which parts to inspect depending on the failure symptoms.

In particular, by adopting the configuration as described in claim 1, it is possible to more accurately know the site to be inspected regardless of isolation.

By adopting the configuration as described in claim 2, even when the faulty part to be inspected is not initially clear, the part to be inspected can be more accurately identified by using the functional chain system. Maintenance personnel can be made aware of this.

By adopting the configuration as described in claim 3, it is possible to obtain an apparatus having the advantages of both claims 1 and 2.

By configuring the scope of claim 4,
While securing the advantages described in claim 2, the functional chain system can be used more conveniently.

By configuring the scope of claim 5,
While obtaining the advantages of both claims 1 and 2, it is possible to make the use of a single function chain system more convenient.

By configuring the scope of claim 6,
It is possible to reduce the number of parts to be inspected using the functional chain system.

(The following is a blank space) (Example) An example of the present invention will be described below based on the attached drawings.

FIG. 1 is a block diagram showing the entire system of the present invention, and this system is constructed using a combination.

In this FIG. 1, the fault symptom input means A is represented by four blocks I, 2.3.4. That is, the failure symptom manual means A includes a failure symptom input section 1 , a fifth storage section 2 , a question display section 3 , and a manual information storage section 4 . The failure symptom input section 1 is used by a person using this system to input primary failure symptoms.

The fifth storage unit 2 stores components that are the cause of failure symptoms along with the probabilities of their causal relationships, and is stored in the form of a failure tree as described in the above publication. There is. The question display section 3 displays to the operator questions limited to the range of the failure tree stored in the fifth storage section. The input information storage unit 4 stores input information, and this stored information is
This information is used as input information of failure symptoms for parts constituting the present invention, which will be described later. That is, when using the present invention, in order to limit the failure symptoms to a certain extent,
- Each element l, 2.3.4 as described in L is constructed.

In FIG. 1, 5 is a first storage section, 6 is a second storage section, 7 is a third storage section, 8 is a fourth storage section, 9 is an inference section, IO is a selection section, I
I is the notification section.

The first storage unit 5 stores, for each component, the causal relationship between this component and a failure symptom caused by the failure of this component, together with its probability, as first failure information, and is data common to each vehicle model. It is created based on. The second storage unit 6 also stores one component (ranked 11th), and stores the causal relationship between this component and the failure symptom caused by the failure of this component, along with its probability, as second failure information. Created based on data dedicated to specific vehicle models.
The storage unit 7 stores a large number of functional chain systems. This functional chain system indicates the connection relationship (input/output relationship) of multiple parts related to realizing each of the many functions that a vehicle has, such as the ignition system and fuel system. A block diagram of an ignition system is shown in FIG. 15 as an example. The third storage unit 7 stores the probability of failure for each component constituting one functional chain system. The fourth storage unit 8 stores the causal relationship between the component assemblies constituting each of the functional chain systems and the cause and effect relationship when the component assemblies fail, together with the probability thereof.
This is stored as failure information. The storage units 5, 6.8 are all stored in the form of an inference unit which will be described later.

The reasoning unit 9 searches each of the first, second, and fourth storage units 5.6.8 according to the information stored in the input information storage unit 4, and determines whether there is a failure. In addition to inferring parts and their probabilities, a collection of parts that are likely to be faulty is inferred together with their probabilities. The result of this inference is then received by the notification section 8.

The selection unit 10 selects from the third storage unit 7 a function chain system that corresponds to the component assembly that is likely to be defective as inferred by the inference unit 9, and each of the selected function chain systems This information is notified to the notification unit 11 together with the failure probability of the component. However, in the embodiment, the first. Only when the maintenance person is not satisfied with the search results in the second and fourth storage units 5.6.8, is the function chain system notified. For this reason, the input unit 1 outputs a command signal instructing the selection means 10 to select a functional chain system.

The above-mentioned failure symptom input section 1 is constituted by, for example, a keyboard. For each storage section 2.5, 6.7.8, an external storage device such as a floppy disk or a hard disk is used since a large storage capacity is required. Input↑11 Information storage section 4 and each storage section 2.5.6
．． 7.8 may be used, but an internal storage device with a small storage capacity may also be used. For example, a CRT is used for the question display section 3 and the notification section II, and a printer can also be used. Of course, the inference unit 9
Constructed by jJ.

, f'' Car A The stored contents of the fifth storage unit 2 are in the form of a fault tree as shown in Fig. 2, if shown schematically. The causes are linked in such a way that they become more specific as they move downwards, and eventually arrive at the part that is considered to be malfunctioning.Then, the cause and effect relationship between the top and bottom of the tree becomes more specific. The degree of failure is shown by its probability. Figure 3 is a schematic representation of something like that shown in Figure 2. Specifically, such a fault tree is It is stored as a large number of rules as shown in Figure 4. In other words,
, Y is the cause one level below X (the lowest part of the tree becomes a component), and N is the probability (certainty of the causal relationship). The certainty of this failure is as shown in Figure 5.
For example, LOW, MIDDLE, IIIGII
For example, three-level rough classification may be used together, or only this rough classification may be used.

FIG. 12 is a flowchart showing the procedure until information is stored in the failure information storage section 4 of FIG. 1, and FIG. 12 will be described below. First, in P (step) l, the first rule (the rule with A in FIG. 3 as the top rule) is fetched. Next, in P2, the rule X is looked at and a search is made to see if there are any other rules with this X. Then, in this rule with X, confidence N
Select the rule with the largest value.

After P2, it is determined in P3 whether Y in the selected rule matches the failure symptom. And-
=～・If it is, X, Y, N of current 7E in P4
The relationship is stored in the input storage section 4.

Then, in P5, after Y is set to X (Y is updated as X in the rule), it is determined in P6 whether there is a rule that causes X. If the determination at P6 is No, the process is immediately terminated. Further, if the determination at P6 is YES, the processes from P2 onwards are repeated.

If the determination in P3 is NO, then in P7 a rule with a confidence level N lower by -.degrees is selected, and then in P8 it is determined whether a similar rule still exists. Then, if there are other rules, the process returns to P3, and if not, a rule at a lower level than the current one (a rule at the bottom of the fault tree) is stored in the input storage section 4 in P9. Note that when reaching step ))9, the part that ultimately causes the failure symptom cannot be reached to a sufficiently lower level in the failure tree.

When storing information in the storage unit 4 using the above-mentioned failure tree, it is possible to save time and effort by creating an interview sheet that compiles questions based on the failure tree to some extent and having it done manually. Become. However, in this case, the questions should be limited to those that can be easily determined, such as 1's perspective and basic inspection.

1st, 2nd, -4th -5, 6, 8 First, if we classify the configuration of a vehicle from major concepts to minor concepts, we get the result as shown in Figure 6. That is, a vehicle is roughly divided into major concepts (system level) such as an engine and a transmission. In addition, each system level is a medium concept (subsystem level), such as the ignition system and fuel system in the case of an engine.
are categorized. Things at each subsystem level are classified into individual component levels, such as fuel pumps, injectors, etc. in the fuel system, for example.

Based on the above-mentioned relationship, inference water can be divided into two types: systems corresponding to subsystems such as the ignition system and fuel system, that is, systems related to a collection of specific parts, and systems at the parts level.
The type is set.

FIG. 7 shows an example of reasoning about a parts assembly, and FIG. 7 shows an ignition system as an example.

In other words, if the ignition system malfunctions.

First, the fault symptom of the broad concept of poor starting is associated, and to this fault symptom of poor starting, the next lower level concept (failure symptom or cause of failure) is that the spark plug is wet and the igniter is sending an incorrect signal. It is associated that this is occurring. In this way, the inference water is
Failure symptoms that occur due to a failure in a system as a collection of parts are associated from top to bottom in order from large concepts to more specific concepts.

Of course, the upper and lower causal relationships in this reasoning are weighted as failure probabilities.

Figure 8 schematically shows the inference process as described above. Specifically, this type of inference process is based on rules as shown in Figure 9, just as in the case of fault trees. is remembered as. In other words, if we focus on rule 1,
The lower level Bt, Dr for the AI of FIG. 8 is stored along with its confidence level and confidence.

Figure 1O shows inference water on a part-by-part basis, and the 9th point is that individual parts come first instead of a collection of parts.
It is different from the one shown in the figure (Fig. 7). As shown in FIG. 11, such a part-by-part inference book is also stored as a rule along with its confidence level and degree of confidence. In addition, in the case of a reasoning book for each part, parts of the same level above and below the reasoning book often correlate together and exhibit one failure symptom, and in this case, a separate rule is formed as AND information ( (See Rule 3 in Figure 11). Of course, this inference book for each part is created separately for each vehicle type (first storage section 5) and for a specific vehicle type (second storage section 6).

It stores a functional chain system as shown in the block diagram in Figure 15, and is encoded in the following format.

(Distributor (Function "Distribution of ignition energy") (Connected parts (before IG coil) (aft, er lIT code)) In other words, for example, focusing on one component that makes up the ignition system, it is called "distribution of ignition energy". The functions performed by the component, the names of the components before and after the connection relationship, that is, the input/output relationship (rlG coil and HT code), and the probability of failure of this component (distributor) are stored as one set.

Of course, regarding the IG coil, the components before and after it are connected are stored as ``battery and ECUJ'' on the input side, and ``distributor'' on the output side. In addition,"
1 Failure probability is set relative to other parts within the range of one functional chain system, and if it is related to multiple functional chain systems, the failure probability is determined for each functional chain system for one component. It is good to remember.

”+ 9, part 10, knowledge 11 reasoning part 9
Depending on the failure symptoms manually determined, use the above-mentioned reasoning book for parts assemblies and the reasoning book for parts + ij (there are two practice books, one for each car model and one for specific car models).
Infer parts assemblies and parts that are likely to be faulty, along with their failure probabilities. In other words, if there is a rule that stores content that matches the input failure symptom,
It is inferred that the component assembly or component has failed based on the confidence level and confidence failure probability stored in this existing rule. This inferred result ultimately determines the correlation between the parts assembly and parts and the 1gIg
Depending on your confidence level.

An appropriate selection is made to obtain the final inference result. Of course, this final inference result will be notified by the notification unit IN.

An example of the final inference result by the inference unit 9 is as follows.

■About the parts assembly; Fighting system (tl I GN O, 6) Fuel system () I I GI O, 3) ■About the parts Water temperature sensor (HIGII O, 5) Control unit (HI GlI O, 3) Injector ( MJDDLE 0.8) EGR valve (1-O
W 0.7). Looking at these results, the maintenance person suspects that the water temperature sensor, control unit, and injector are malfunctioning based on the correlation between the parts assembly and the parts, and the confidence level and confidence level. On the other hand, the EGR valve has been ruled out as a possible malfunction.

Even if the maintenance person searches for the failure information stored in the first, second, and fourth storage units 5, 6, and 8, the maintenance person may not be fully satisfied. At this time, the selection means 10 selects the function corresponding to the parts assembly with the highest failure probability among the parts collections estimated to be faulty from among the function chain systems stored in the third storage means. A chain system is selected.

Then, this selected functional chain system is notified to the notification section 11, and the parts with a high failure probability in this functional chain system are also notified. A maintenance person who sees such a chain of functions determines whether or not there is an abnormality in the chain of functions by checking the output state of the chain of functions.

If the result of this output inspection is abnormal, the inspection is performed in the order of the failure probability among the parts constituting the functional chain system. Even if the output is found to be normal in the inspection, the function chain system corresponding to the parts assembly inferred to have the highest failure probability is selected, and the above-described procedure is repeated.

FIGS. 13 and 14 are flowcharts showing the procedure for obtaining an inference result using the above-described inference tree. As mentioned above, the input information storage unit 4 stores failure symptoms that have been input so far and parts that can be classified as failures according to these, within a somewhat limited range. In this storage section 4
This means that it is only necessary to use the information stored in -.

First, at Ql in FIG. 13, the process shown in FIG. 12 is performed.

Next, in Q2 and Q3, it is inferred whether the parts assembly is at fault or not, along with its failure probability, according to the failure symptoms (search of the fourth storage unit 8). It is inferred whether or not the remaining parts have failed, along with their failure probabilities (first
retrieval of storage means 5). Furthermore, in Q6 and Q7, it is estimated whether or not the middle parts of the specific vehicle model are malfunctioning, along with their failure probabilities (search of the second memory 6).

Note that the inference in Q6 may be performed only when there is an inference command from the input unit 1 regarding a specific vehicle type.

In Q8, parts assemblies and parts (common to each car model and specific i1j
Based on the results of each inference for the two scales (1) and (1), those that are less likely to be malfunctioning are removed according to the comprehensive inference, that is, the correlation between each other, the level of certainty, and the degree of certainty. The final inference result obtained after this processing is sent to the notification section 11 in Q9.

The fraud of Q2 (Q, 4, Q 6 ) described above is as shown in FIG. First, in R1, a parts collection or a reasoning book corresponding to the parts is selected (first storage unit 5,
selection of the second storage unit 6 or the fourth storage unit 8). Then,
In R2, the rule located above is selected.

In R3, it is determined whether or not the information stored in the selected rule matches the input failure symptom. YE with this R3's 'I'11 cut
In the case of S, in R4, the part assembly (part) that is currently the inference target is stored together with the certainty level and confidence in the rule that is YES when 1<3. After this,
In R5, the rule is updated to the next lower level rule in the inference book, and the processes from R3 onwards are repeated.

If the judgment in R3 is No, then in R6 another rule with the same level of -E sub-relationships in the inference book is selected, and then it is judged in R7 whether such a rule exists. When the judgment in R7 is YES, the process returns to R3, and on the other hand, when the judgment is NO, the process ends (search ends).

After 09 in FIG. 13, it is determined in QIO whether the inference result of Q9 is sufficiently satisfactory. If this determination is YES, the process ends immediately. Also, QI
When the determination of O is NO, in Qll, the function chain system corresponding to the parts assembly that is estimated to be faulty in Q3 and has the highest failure probability among the parts assemblies is selected. (Then, the selected function chain system and the failure probability of each component that constitutes the function chain system are reported in the notification section!
To be taken. , in Ql2, 1 selected Pi('f
The maintenance person checks whether the output state of the @link button system is normal. If there is no abnormality as a result of this check, then the functional chain system corresponding to the parts assembly with the next highest failure probability is selected and displayed.

When the tII classification of Q, +3 is YES, inspection will be performed on parts with a high failure probability among the parts constituting this functional chain system.

In this divine failure symptom system, how accurately the user name recognizes the failure symptoms is important in inferring the failure location.

From this point of view, the question display section 3 in FIG. It is a good idea to compile it in advance so that it is accurate and accurate.

First, a question story (primary question group) and a secondary question group are prepared in advance, as shown below, according to failure phenomena that are broadly classified as primary.

Failure phenomenon-11. Poor starting---11 question story 1
122nd question group-2, Idle problem-2-1 Question story 2-22nd question group-3, Poor running ---3-1 Question story l
3-22nd question group-4, engine stall---4-1
Question story 4-2 Written in the 2nd question group In the question story and the 2nd question group, the key questions include the user's human ability to answer them (Which question should I ask next depending on Ii'i? Knowledge (=next action knowledge) that determines the next action is prepared.

When broadly categorized symptoms such as "poor starting" and "poor running" are input, it is determined which question story to use. Once the question list is determined, the patient will be interviewed according to this list. At this time, since the next action knowledge has been determined in advance as described above, processing is performed in accordance with this next action knowledge. The following is an explanation based on the premise that a major failure phenomenon is "starting failure."

Format of medical interview processingQuestion story (Code 1---2 where questions are set)
．． ,,---n) Secondary question group (Code 1---2 where questions are set)
．． ,, ---n) Code 1 (Question item ["Answer code that can be answered, Action code) Looking at a larger example, the question story "Starting failure" (Q, I Q2 Q3 Q4 0.7Q, 19
Q 20 Q, 21) Secondary question group (Q5 Q6 Ql5 Q, I6 Q17) Question code (Ql “The starting state is” QUI ACTI) (Q
2 1i Do you appear?”Q, tJ 2 to CT2)4
Answer code (QLJ I (Q, Q I QQ2 QQ3)
) ) (Q t) 2 (QQ2+ QQ
22 [JNDEF)) Actual value of oral answer code (QQI “Do not crank”) (Q02 “
It cranks but doesn't explode for the first time") (QQ3 "It explodes for the first time but doesn't explode completely") (QQ21 "It always happens") (QQ22 "It only happens sometimes") (IINDEF
“I don’t know”) Action code (ACTI (if QQI L
hen Q5) (el s e C0NT
I NIJ IE) ) (ACT2 (if QQ
, 21 Shihen Q4) (if QQ22
しhen Q6) (else C0NTINL
JEI) In the above example, if the input result of the medical interview Ql (=starting state), that is, the user's answer result is QQI (=do not crank), then the action is Q5, so you can leave the question chain and move to the secondary question group. to move to.

If the user's answer is QQ2 (=cranking but no first explosion) or QQ3 (=first explosion but not complete explosion),
Since the action is C0NT INUI-, the question string is
The interview will continue according to the schedule. Similarly, in the case of interview Q2, if the user's manpower value is QQ21, Q4
For QQ2, go to Q6; for other manpower values, the interview will continue according to the question story.

As described above, even if the user only has a very general understanding of the failure phenomenon, the failure part 4>
When inferring 'i, more specific and accurate failure symptoms can be entered to make failure diagnosis more accurate.

Disclaimer 1 Only one of the above-mentioned failure symptoms based on failure information common to each vehicle type and failure symptoms based on failure information of a specific vehicle type can be executed according to a user's request. In this case, as described below, the failure information storage and failure diagnosis may be performed using the rule ``if rif=if...'', rLhen=.

In this case, the rifJ part often has multiple conditions (= failure symptoms), and if all conditions are not satisfied, rL
, hcnJ is not executed" rule, and "O
It is divided into two parts: ``R'' rule. Basically, such rules are configured so that evaluation is performed in descending order of priority.

Also, the ri LJ part of each rule (= multiple articles f[>
Next, "next action knowledge" is defined that determines the next evaluation action based on the comparison result of the input value of the priority and Article 5.

Regarding the example description format and evaluation example of the rules mentioned above,
This will be explained below.

Lulu's writing format % expression % () )) (t, kr e n e Q axisanon) (
L h e n n e action)) (t, h
en (Failure part name Possible failure type Confidence)
) (AND) --- Hail evaluation method. In the case of AND with other ORs, all of the conditions must be met; in the case of OR, basically any one of the conditions needs to be met. However, if an axicon is set, follow the instructions (description) there.

1) 1, P 2 ------ I can't prioritize. the smaller number High priority.

△△△, Rororo Conditions - comparison with human results.

eq, ne---- eq = eq II a Abbreviation for l, ne=not
Abbreviation for equal.

Action I'm waiting for my next move. value is.

NEXT - Compare the human power information with the following conditions, J U M P - This rule is rated medium! l2→Evaluation of next rule END-1 End evaluation process.

(Evaluation results with very high confidence = For example, only in the case of 0.9 process) It has three action orders.

For example, (RLJ L E # 10 (AND) f
(PI QQI (t, h e n e q N E X T )
(else, JUNP)) (P2 QQ21 (t, hen eq END) comb hen nc NEXT)) (L, h e rl (injector airtight failure 0.9)
5))) When there is a rule like 1 input value QQI and QQ2+
In that case, 95J of the rLhenJ part [Injector airtight failure - ○] will be added to the previous 1 juice value result list as an estimated result. On the other hand, when there is no QQI as a 5-person power value, rclse, JUMPJ
Interrupt the evaluation of the rule as instructed and start the next rule r#
Now we will move on to the evaluation of IIJ. When the evaluation of the rules is completed, the rules are sorted in descending order of confidence.

After completing the evaluation of the general rules (common rules for 3 cars) in this way, next, if necessary, as an extra-large case, you can evaluate the specific car type, i.e. whether it is not included in the 11 type rules or the rules for Just do the evaluation. The format of such a specific rule is as follows, where the vehicle type name and failure symptom are defined as the rifJ section, and the estimated failure location, estimated failure type, and failure certainty are defined as the rthenJ section.

In the case of singular cases (= small type rules, new car rules), the rule form ((to vehicle type (state Q, QI (estimated failure location (estimated failure type 0.8)) (state QQ I O) (estimated failure location (estimated Failure type 0.6) ) 1G coil) Leak) QQIO) Injector) Poor airtightness) (Medium type [3

[Brief explanation of drawings]

FIG. 1 is an overall system diagram showing an embodiment of the present invention. FIG. 2 is a diagram showing an example of a fault tree. FIG. 3 is a diagram schematically showing FIG. 2. FIG. 4 is a diagram showing an example of storing the contents of FIGS. 2 and 3. FIG. 5 is a diagram showing an example of setting confidence levels and confidence degrees. FIG. 6 is a diagram showing how the configuration of a vehicle is classified from higher-level concepts to lower-level concepts. FIG. 7 is a diagram showing an example of an inference book about a parts assembly. FIG. 8 is a diagram schematically showing FIG. 7. FIG. 9 is a diagram showing an example of storing the contents of FIGS. 7 and 8. FIG. 10 is a diagram schematically showing an example of an inference book about parts. FIG. 11 is a diagram showing an example of storing the contents of FIG. 10. 12 to 14 are flowcharts showing control examples of the present invention. FIG. 15 is a block diagram showing an example of a functional chain system. FIG. 6 is a block diagram showing a preferred configuration of the present invention.

Claims (6)

[Claims] (1) A first storage means that stores, as first failure information, a general causal relationship common to each vehicle model among the causal relationships between a component and a failure symptom caused by a failure of the component, together with its probability; and a component and the component. a second storage means for storing a specific causal relationship exclusive to a specific vehicle model along with its probability as second failure information among the causal relationships with the failure symptoms caused by the failure of the vehicle; and an input means for inputting the failure symptoms of the vehicle. , Based on the failure symptoms input by the input means, the first failure information is searched to infer a failure component and its probability, and the second failure information is searched to determine the failure component. A fault diagnosis device for a vehicle, comprising: an inference means for inferring a probability thereof; and a notification means for notifying a result inferred by the inference means. (2) A first storage means that stores the causal relationship between a component and a failure symptom caused by the failure of the component, together with its probability, as failure information; a second storage means for storing connection relationships among a plurality of parts related to each other as a functional chain system; an input means for inputting failure symptoms of the vehicle; an inference means for retrieving failure information and inferring failure parts and their probabilities; and inference means for inferring failure parts and their probabilities; and inference means for inferring failure parts and their probabilities; A fault diagnosis device for a vehicle, comprising: a selection means for selecting a chain system; and a notification means for notifying the inference result inferred by the inference means and the functional chain system selected by the selection means. . (3) a first storage means that stores, as first failure information, a general causal relationship common to each vehicle model among the causal relationships between a component and a failure symptom caused by a failure of the component, together with its probability; and a component and the component; a second storage means that stores, as second failure information, a specific causal relationship exclusive to a specific vehicle model along with its probability among the causal relationships with failure symptoms caused by the failure of the vehicle; a third storage means that stores the connection relationship between a plurality of parts that are mutually related to realize the function as a functional chain system; an input means for inputting a failure symptom of the vehicle; and a failure input by the input means. an inference means for searching the first failure information to infer a malfunctioning component and its probability, and searching the second failure information to infer a malfunctioning component and its probability, based on the symptoms; , selection means for selecting one function chain system from among the plurality of function chain systems stored in the third storage means according to predetermined conditions; and an inference result inferred by the inference means and the above. A fault diagnosis device for a vehicle, comprising: notification means for notifying the functional chain system selected by the selection means. (4) A first storage means that stores the causal relationship between a component and a failure symptom caused by the failure of the component together with its probability as first failure information, and realizes each of the numerous functions of the vehicle. a second storage means that stores the connection relationships between a plurality of parts related to each other as a functional chain system; a specific parts assembly constituting the functional chain system; and a part assembly that occurs due to a failure of the parts assembly. a third storage means for storing a causal relationship with a failure symptom together with its probability as second failure information; an input means for inputting a failure symptom of the vehicle; an inference means for searching failure information to infer a failure component and its probability, and searching the second failure information to infer a failure specific component assembly and its probability; 2. A selection means for selecting a function chain system corresponding to the part assembly inferred by the inference means from among the two storage means; and a function chain system selected by the inference result inferred by the inference means and the function chain system selected by the selection means. A vehicle failure diagnosis device comprising: a notification means for notifying; and a vehicle failure diagnosis device. (5) a first storage means that stores a general causal relationship common to each vehicle model, along with its probability, as first failure information among the causal relationships between a component and a failure symptom caused by a failure of the component; and a component and the component; a second storage means that stores, as second failure information, a specific causal relationship exclusive to a specific vehicle model along with its probability among the causal relationships with failure symptoms caused by the failure of the vehicle; a third storage means that stores the connection relationships between a plurality of parts that are mutually related to realize the function as a functional chain system; a specific parts assembly that constitutes the functional chain system; and a failure of the parts assembly. a fourth storage means that stores a causal relationship with a failure symptom caused by the failure as third failure information together with its probability; an input means for inputting the failure symptom of the vehicle; and an input means for inputting the failure symptom input by the input means. , searching the first failure information to find the parts that are failing and their probabilities; searching the second failure information to find the parts that are failing and their probabilities; and searching the third failure information to find the parts that are failing. an inference means for inferring a specific parts assembly and its probability; a selection means for selecting a functional chain system corresponding to the parts assembly inferred by the inference means from the third storage means; A fault diagnosis device for a vehicle, comprising: notifying means for notifying the inference result inferred by the means and the functional chain system selected by the selecting means. (6) Any one of claims 2 to 5
In paragraph 1, the storage means storing the functional chain system also stores the probability of failure for each specific component constituting the functional chain system, and the notifying means stores the functional chain system together with the failure probability , in which the probability of failure for each component constituting the functional chain system is reported.