JPH01278866A - Trouble diagnoser for vehicle - Google Patents

Abstract

PURPOSE:To give a guideline to a user that from what part does it be good to start the checking work by installing an inferring means, which retrieves trouble information on the basis of trouble symptoms inputted, and a means for reading auxiliary information corresponding to this inferred part and informs a user of this auxiliary information together with the inferred result. CONSTITUTION:In this trouble diagnoser, there is provided with a memory means 2 which stores a causal relationship between each trouble symptom and a trouble cause as trouble information and simultaneously has stored at least either of repair cost and trouble frequency in addition to the degree of difficulty for checking work in regard to parts as each auxiliary information. Also, there is provided with an inferring means 3 which retrieves the trouble information of the memory means 2 and infers the trouble cause on the basis of trouble symptoms inputted by an inputting means 1. With regard to parts subject to this inferred trouble cause, the auxiliary information of the memory means 2 is read by a reading means, and this read auxiliary information and the inferred result is informed to a user by an informing means 4. In consequence, a guideline is given to judgment of the user that from what part does it be good to start his inspection and repair.

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. . This method memorizes the causal relationship between failure symptoms and component failures that cause the failures as a failure tree, and by sequentially inputting the failure symptoms, the cause of the failure, that is, the malfunctioning part, is finally found and detected. is displayed. By also recording the probability of a causal relationship between a failure symptom and the component failure that causes it, the malfunctioning component can be found with greater accuracy.

When the above-described system is used, it may be inferred that there are many parts that are the cause of the failure depending on the failure symptoms, and each part has a fairly high failure probability. In such a case, the user has trouble deciding from which part to start the inspection work.

From this viewpoint, as shown in Japanese Unexamined Patent Publication No. 62-4643, it has been proposed to display together with the level of difficulty of the work required for inspection and repair for the part inferred to be the cause of the failure.

(Problem to be solved by the invention) However, even if the difficulty level of the work is displayed, some tasks may be difficult for some users due to differences in the skill level and knowledge of the maintenance personnel. For some people, this may be perceived as being rather easy. therefore,
Simply adding information about the difficulty of the work is not necessarily sufficient.

Therefore, an object of the present invention is to provide a vehicle that can more clearly provide guidelines for determining which part should be inspected and repaired when there are many parts suspected to be malfunctioning. An object of the present invention is to provide a failure diagnosis device.

(Means and operations for solving the problems) In order to achieve the above-mentioned object, the present invention has the following configuration. That is, a storage means that stores the causal relationship between failure symptoms and failure causes as failure information, and stores at least one of the difficulty of inspection work, repair cost, and failure frequency for parts as auxiliary information, and the vehicle. an input means for inputting failure symptoms of the input means; an inference means for searching the failure information and inferring the cause of the failure based on the failure symptoms input by the input means; A reading means for reading out corresponding auxiliary information from among the auxiliary information regarding the part; a notification means for notifying the inference result inferred by the inference means and the auxiliary information read out by the reading means in section 707; In the present invention, even if there are many parts suspected to be malfunctioning, in addition to the difficulty of inspecting and repairing each part, information on the cost or frequency of failure is provided. At least one of the above can be obtained. Therefore, users should take all of these factors into consideration and make decisions more clearly about which parts to start inspection and repair, and be able to respond to each user regardless of their differences in maintenance knowledge. can be given with sufficient rational persuasiveness.

Note that the auxiliary information may be displayed automatically for parts suspected to be malfunctioning, or only when the user makes a selection operation, specifically providing the type of auxiliary information requested by the selection. It is also possible to display only information.

(Example) Examples of the present invention will be described below based on the attached drawings.

Overall Overview FIG. 1 is a block diagram showing the overall system of the present invention, and this system is constructed using a computer. In FIG. 1, each part is roughly divided into five blocks 1, 2.3.4.5. That is, a failure symptom input section 1, a knowledge base 2, and an inference section 3.
, a designation section 4 for providing information, and a notification section 5.

The failure symptom input section 1 is for a person using this system to input failure symptoms and selections of auxiliary information. The knowledge base 2 consists of two storage sections, a first and a second storage section. The first storage unit stores components that are the cause of failure symptoms along with the probability of their causal relationship;
It is stored in the form of a fault tree as described in Publication No. +iii. Further, the second storage section stores, for each component, the difficulty level of inspection and repair work, as well as repair costs and various auxiliary information to be recorded after failure. The inference unit 3 first searches the first storage unit in the h knowledge base and infers the parts that are likely to be faulty and their probabilities. Further, the inference section 3 reads out auxiliary information corresponding to the selection operation from among the auxiliary information in the second storage section of the knowledge base 2. And this inference part 3
The results obtained are reported to the notification section 5. When displaying auxiliary information, the ordering designation unit 4 sorts the information, ie, rearranges the information in order of difficulty, for example, from the lowest to the highest. Therefore, the notification section 5 is notified in this sorted format.

Note that the above-mentioned failure symptom input unit 1 is configured by, for example, a keyboard. For each storage unit in the knowledge base 2, an external storage device 5 such as a floppy disk, hard disk, etc. is used from the viewpoint that a large memory capacity is required. For example, a CRT is used as the notification section 5, and a printer may also be used in conjunction with the CRT. Of course, the inference section 3 and the specification section 4 are constituted by a CPU.

The stored contents of the first storage section in the knowledge base 2 are in the form of a fault tree as shown in FIG. 2, when shown schematically. This means, for example, that failure to crank is a failure symptom.
The cause-and-effect relationships are made more concrete as the cause progresses, and eventually the part that is thought to be malfunctioning is traced back to it. Then, the degree of causality in the hierarchical relationship of the tree is expressed by its probability.

FIG. 3 schematically shows what is shown in FIG. 2. Specifically, such a fault tree is stored as a large number of rules as shown in FIG. In other words, X is the failure symptom (if you go to the F position in the tree, it may be the cause), Y is the cause of one F of X (if you go to the most r position in the tree, it becomes a part), and N is Probability (certainty of causality). As shown in FIG. 5, the reliability of this failure is, for example, LOIII, PIIDDL.
Approximately three levels of rough classification such as E and HIGH may be used together, or only this rough classification may be used.

FIG. 6 is a flowchart showing the procedure for searching the information stored in the E-memory 1 storage unit, that is, failure information, and inferring the cause of failure (component) according to the failure information, along with its probability. , FIG. 6 will now be explained. First, in P (step) l, the first rule (
3) is imported. Then P
In step 2, look at X in the rules and search to see if there are any other rules with this X. Then, among the rules having X, the rule with the highest confidence level N is selected.

After P2, in P3, it is determined whether Y in the selected rule matches the failure symptom. If they match, the current relationship between X, Y, and N is temporarily saved in P4. 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. This P6
If the judgment is NO, the process is terminated. Further, if the determination at P6 is YES, the processes from P2 onwards are repeated.

If the determination in P3 is No, a rule with a lower confidence level N is selected in P7, and then it is determined in P8 whether a similar rule still exists. Then, if there are other rules, the process returns to P3, and if not, in P9, those at a lower level than the current one (rules at the bottom of the fault tree) are temporarily saved. Note that when reaching P9, the part that will ultimately cause the failure symptom cannot be reached to a sufficiently lower level in the failure tree.

The second storage part of the knowledge base contains the following ■~(9)
Each item is stored as auxiliary information.

In other words, ■Difficulty of work, ■Number of work steps, ■Cost required for inspection and repair, [stock] failure frequency, (ΦImage information when performing inspection, and (2) Image information when repairing. Above) The image information of 0 and ■ is a concrete drawing or photograph of the situation when performing inspection or repair so that the contents can be immediately determined by visual inspection.Such image information An example of this is shown in FIG. 9 when inspecting the quality of the water temperature sensor, and the water temperature sensor is indicated by reference numeral 11 in the figure.

Each piece of information mentioned above is stored individually in the form of a rule for each part. In other words, rule 1 = (part name, ■, (2)...(Φ)).The degree of difficulty, cost, failure frequency, etc. of the work is encoded, for example. For example, they are displayed in five stages depending on the ABC order.

Next, the procedure for using the above-mentioned auxiliary information will be explained with reference to the flowcharts of FIGS. 7 and 8.

First, in FIG. 7, as described in the explanation of FIG. 6, when a large number of likely failure parts are inferred according to the failure symptoms (Ql), in Q2, assistance for each inferred part is determined. Information is captured. Then, in Q3,
These parts are sorted in order of work difficulty. As a result of Q3 sorting, the rankings are the same in QIO (
If there are multiple items with the same degree of difficulty, they are sorted in terms of repair costs in Q4.

If it is determined in Q5 that there are a plurality of repair costs having the same rank and same date, then in Q6 the repair costs are sorted according to the failure frequency. And after Q6, Q7
In Q7, it is determined whether or not to display image information such as the one shown in Figure 9 for inspection and repair.
If it is ES, the part whose image information is to be displayed is specified in Q8, and then the image information of the specified part is displayed in Q9. When the judgment of QIO is NO, Q4, Q
6 or move to Q7. Also, if the judgment in Q5 is NO, Q6 or Q7 depending on the selection operation in Q12.
Move to.

FIG. 8 shows an example in which specific pieces of auxiliary information are automatically displayed in a predetermined order. That is, the cause of the failure is inferred in R1 according to the failure symptoms, and parts that are suspected of having failed are displayed in R2. This example of the eighth cause indicates that the injector, the water temperature sensor, the EGR eight, and one spark plug are inferred to be suspicious, and the numerical value in parentheses for each component indicates the probability of failure.

If there is an instruction to sort by difficulty level of inspection/repair, it will be displayed as R3. In this case, the difficulty level is AB
They are expressed in the order of C, and the difficulty level increases from A to B to C.

When instructed to sort by repair cost, it is displayed as R4. The higher the number for each part, the higher the cost.

If there is an instruction to sort by failure frequency, it will be displayed as R5. The higher the numerical value after each component, the higher the failure frequency.

In R6, it is determined whether or not to perform further sorting, and when performing further sorting, sorting is performed in the same format as described above.

Based on the results of the displays R2 to R5 above, it is natural for the user to first inspect and repair the water temperature sensor. In other words, the water temperature sensor is the first candidate for inspection and repair because the probability of failure is quite high, the difficulty of one task and the repair cost are not that high, and the frequency of failure is extremely high. On the other hand, injectors are unlikely to be the top candidates in terms of work difficulty and repair costs, EGR valves are difficult to be the top candidates in terms of failure frequency, and spark plugs are difficult to be the top candidates in terms of failure probability and failure frequency. This makes it difficult for him to become a candidate.

As auxiliary information, in order to make it clearer how to carry out inspection and repair, it is preferable to display a step-by-step guide for each level of work, together with or separately from image information as shown in FIG. 9. This point is illustrated below using a water temperature sensor as an example, and the inspection content becomes more detailed as the level goes from 1 to 2.3.

EVEL 1 1, Inspection of connector connection status IE 2, Water temperature sensor resistance value inspection 3, Wiring (continuity/short circuit) inspection EVEL 2 1, Inspection of connector connection status Visual inspection of connector connection status, pin fitting status 2, Water temperature sensor Check resistance value Check continuity between water temperature connector and ECU connector Is there continuity? Continuity check between water temperature sensor and POD Is there continuity? (Effects of the Invention) As is clear from the above description, even if there are a large number of parts suspected to be malfunctioning, the present invention can be inspected with more rational persuasiveness than in the past. We can give you the parts that must be present and the order in which they should be inspected.

[Brief explanation of the drawing]

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 flowchart showing the procedure for inferring the cause of failure. Figures 7 and 8 are flowcharts showing the procedure for obtaining auxiliary information. Figure 4 Level 6 Figure 8

Claims (1)

[Claims] (1) A storage means that stores causal relationships between failure symptoms and failure causes as failure information, and stores at least one of the difficulty of inspection work, repair cost, and failure frequency for parts as auxiliary information, and a vehicle. an input means for inputting a failure symptom of the input means; an inference means for searching the failure information and inferring the cause of the failure based on the failure symptom input by the input means; With respect to the part, the present invention comprises a reading means for reading out corresponding auxiliary information from among the auxiliary information, and a notifying means for notifying the inference result inferred by the inference means and the auxiliary information read out by the reading means. A vehicle failure diagnosis device characterized by: