JPS6225234A - Vehicle failure diagnosis device - Google Patents

Abstract

(57) [Summary] This bulletin contains application data before electronic filing, so abstract data is not recorded.

Description

DETAILED DESCRIPTION OF THE INVENTION [Technical Field of the Invention] The present invention relates to a vehicle fault diagnosis device that applies a so-called expert system.

[Description of prior art]

As an example of a conventional vehicle failure diagnosis apparatus to which a so-called expert system is applied, the ones shown in FIGS. 12 to 15 can be considered. FIG. 12 is an IR diagram of the device, FIG. 13 is an explanatory diagram of knowledge data, FIG. 14 is an explanatory diagram of a fault tree, and FIG. 15 is a flowchart of inference processing.

As shown in FIG. 12, the vehicle fault diagnosis device 1 has a knowledge data section 3 and an inference section 5, and a user terminal 9 is connected to the inference section 5 via an interface 7. It is composed of The user terminal 9 is equipped with, for example, a keyboard, a CRT, a suitable audio input/output device, etc., and the user terminal 9 and the interface 7 are connected, for example, online.

The knowledge data section 3 is constructed as a database, and as shown in FIG. 13, therein is stored a large amount of data related to vehicles in general or required for fault diagnosis for each vehicle type. That is, the knowledge data section 3 contains data ca in a form that gives meaning that Y is considered to be the cause of symptom X.
use (X, Y), and in the presence of cause Y, the phenomenon P
data rul in a form that gives meaning to what will occur
e(P, Y) are stored. Here, for example,
cause (△.

(B+, B2, B3)) is the cause B+, 82 for symptom A, as shown in the fault tree in FIG.

This means that B3 is possible, and rule (
P+, B+) means that in the presence of cause B1, phenomenon P1 will occur.

Note that the fault tree shown in Figure 14 has been simplified for ease of explanation, and in an actual fault tree, a larger number of causes are related to one symptom, and a larger number of causes are related to a single symptom. It is composed of symptoms that are interrelated in a more complex manner.

As shown in FIG. 15, the inference section 5 performs the following inference processing on the above-mentioned severe fault tree.

Assume that "vehicle malfunction" A is set as the symptom in step S1. In the initial stage, this symptom setting is performed by inputting symptom information from the user terminal 9, and in the process of inference, self-setting is performed. Ill be set.

In step S2, cause B+, 82B3, is inferred as the cause of A. This inference is the knowledge data part's cause (
A, (B+, B2, B3)).

Next, in step S3, cause list [B1.82] is created.

83], one of them Bi (St) is assumed to be the cause. Then, in step S4, rule(pi
.

Bi) is searched to infer the phenomenon Pi that would occur in the presence of the cause Bi, and in step S5, the diagnostician is asked questions such as "Is there Pi in the charcoal?"

This interview is performed by being displayed on a display device of a user terminal, for example.

Therefore, the diagnostician answers whether or not the phenomenon Pi occurs.

Step S6 is for determining the content of the answer by the diagnostician, and if the phenomenon P1 has occurred, the process proceeds to step S7; if the current IPi has not occurred, the process proceeds to step S8.

Step S8 is a process performed when it is determined in step S6 that the phenomenon P1 has not occurred, and here, although the phenomenon Pi should definitely occur if the cause Bi exists, the phenomenon P1 actually does not occur. Bi from the cause list (B+, B2, B:l) based on the fact that it has not occurred.
(B+) and returns to step S3 to consider the next cause Bi.

On the other hand, step S7 is a process performed when it is determined in step S6 that the phenomenon Pi has occurred, and here
i is considered to be a strong candidate for symptom A, and this cause Bi is considered to be the next cause of symptom Bi as a new symptom, such as C+, C
2. C3 is searched.

However, in conventional vehicle failure diagnosis devices designed in this way, the vehicle failure information stored in the knowledge data section 3 is general information or information for each specific vehicle type. There are problems as follows.

■ When constructing a general database of vehicle failure information, it is impossible to include failure information of specific parts for each vehicle model as failure information, and it is necessary to conduct failure diagnosis for specific parts for each vehicle model. become unable to do so.

■ When constructing a database of vehicle failure information for each vehicle model, a great deal of knowledge about a large number of vehicle models is required, and the database has no choice but to be expanded, which is economically disadvantageous and has practical problems. .

[Purpose of the invention]

It is an object of the present invention to solve the above-mentioned problems and provide a vehicle failure diagnosis device that can be adapted to any type of vehicle without expanding the database blindly.

[Summary of the invention]

In order to achieve the above object, the present invention provides a general-purpose failure information storage means 11 that stores general-purpose failure information that can be applied to any vehicle type, as shown in FIG.
, a vehicle type-specific failure information storage means 13 for storing failure information specific to each vehicle type, a vehicle type information input means 15 for inputting vehicle type information, and a vehicle type-specific failure information based on the vehicle type information inputted from the means 15. A unique failure information selection means 17 that selects vehicle type-specific failure information of a predetermined vehicle type from among the general-purpose failure information of the general-purpose failure information storage means 11 and the unique failure information selected by the unique failure information selection means 17 are combined. failure information forming means 19 for forming vehicle failure information according to the vehicle type; and symptom information input means 21 for inputting symptom information.
and inference means for searching the vehicle failure information formed in the vehicle failure information forming means 19 based on the symptom information input from the means 21 and inferring the cause of the failure of the symptom input from the symptom information input means 21. 23 and a notification means 25 for notifying the inference content of the means 23, a vehicle failure diagnosis device 27 is configured, and is capable of performing failure diagnosis of many types of vehicles with a small database.

[Explanation of Examples]

The present invention will be described in detail below.

FIG. 2 is a schematic diagram of an apparatus in which the invention can be practiced.

As illustrated, the vehicle failure diagnosis device 27A includes an interface 7, a user terminal 9, an inference section 29, a general knowledge data section 31, and a vehicle specific knowledge data section 33. The interface 7 and user terminal 9 are the same as those shown in the conventional example.

In addition to the inference unit 5 shown in the conventional example, the inference unit 29 includes a first
It has a vehicle failure information forming means 19 shown in the figure, and has a function of synthesizing data stored in a general-purpose knowledge data section 31 and a vehicle-specific knowledge data section 33 according to the vehicle type.

An example of data contents stored in the general knowledge data section 31 is shown in FIG.

The general-purpose knowledge data section 31 stores general-purpose failure information that can be applied to any vehicle within a predetermined target range. The predetermined target range can be set arbitrarily, and for example, a series series can be set as the predetermined target range.

As shown in FIG. 3, the general-purpose data is data in which Y is thought to be the cause of symptom X cause (X.

Although Y) is shown as an example, it goes without saying that it is also possible to use the interview data rule (P, Y) as shown in the conventional example. However, here,
The data is limited to a general-purpose component level, for example, up to 70 meters of air. FIG. 4 shows a fault tree formed from the data shown in FIG.

The vehicle specific knowledge data section 33 is the general knowledge data section 3.
This is data in the form that should be combined with the general knowledge stored in 1, classified and stored for each vehicle type. Therefore, the vehicle specific knowledge data section 33 has internal information for each vehicle type a, b, .
・It will have a knowledge data section for each i... Specific examples of this are shown in FIGS. 5 to 8.

FIG. 5 shows an example of specific knowledge regarding a car model (for example, car model a) that employs the hot wire method for its air flow meter. In other words, the data indicates that "air flow meter hot wire disconnection" is considered to be the cause of the failure of the "air flow meter." FIG. 6 shows a fault tree formed from this data.

Figure 7 shows, for example, a flap on an air flow meter.

A car that uses this method! ! ! (For example, vehicle type b) is shown as an example of unique knowledge. In other words, the data indicates that "air 70 meter flap sticking" is considered to be the cause of the failure of the "air flow meter". FIG. 8 shows a fault tree formed from this data.

The operation of the vehicle failure diagnosis device 27A having the above configuration will be explained using FIGS. 9 to 11.

FIG. 9 is a flowchart of the diagnostic processing performed by the vehicle failure diagnostic device 27A.

Step 901 shows the process of inputting the vehicle type, and this input is performed by operating the keyboard of the user terminal 9.

Of course, a touch panel method using a CRT, a light pen method, etc. may also be used. Specifically, car model a,
or b is selected, or neither is selected.

Step 903 is for determining the state of vehicle type input. If any vehicle type, for example a or b, has been selected, the process moves to step 905, and if none has been selected, the process moves to step 907.

Step 907 shows a process of displaying the message "Do you want to diagnose using only general knowledge?" on the CRT, and the user (diagnosis person) answers rYesJ or "NO" in step 909. If the answer is rYesJ, the process moves to step 913; if the answer is rNoJr, the process moves to step 915.

Therefore, in step 905, the vehicle failure information forming means 19 of the inference unit 29 reads the general knowledge and specific knowledge according to the vehicle type selected in step 901 from the database, reads only the general knowledge in step 913, and in step 915 Now, the process of reading general knowledge and all specific knowledge is performed.

FIG. 10 shows an example of a fault tree formed by inputting the vehicle type in step 901 and reading general knowledge data and specific knowledge data of the vehicle type in step 905.

FIG. 11 shows an example of a fault tree formed by reading the general knowledge data and all the unique knowledge data in step 905 after "NO" is selected in step 909.

In this way, the user can arbitrarily select a database according to the type of vehicle to be diagnosed, and the knowledge data can be optimized according to the type of vehicle.

After the knowledge data has been optimized and predetermined vehicle failure information has been formed in step 905, step 913, or step 915, the terminal 2 is entered, but from then on, the conventional example (
Processing similar to that shown in FIG. 15) is performed to infer the cause of the failure of the input symptom. Using the example in Figure 10, for car models, ``flap sticking'' will be considered for the air flow meter as a cause of poor starting.
In the example of FIG. 11, in addition to this, "hot wire disconnection" will also be considered.

In the vehicle failure diagnosis device 27A described above, general knowledge data and vehicle-specific knowledge data according to the vehicle type can be synthesized according to the vehicle type to be diagnosed, so that necessary and sufficient knowledge data according to the vehicle type can be combined. This allows the user to perform efficient vehicle failure diagnosis using the necessary and sufficient knowledge data.

Furthermore, since general-purpose knowledge data can be created for basic failure parts and parts, the structure of the knowledge data is simple and clear, and once created, general-purpose knowledge data can be used by adding specific data to it. It is also economically advantageous because it can be used for a long period of time without requiring major changes.

〔Effect of the invention〕

Since this invention is a vehicle fault diagnosis device that combines general-purpose fault information with vehicle-specific fault information to form vehicle fault information, it is possible to form sufficient knowledge data for various types of vehicles using a relatively small database. becomes possible.

[Brief explanation of the drawing]

Fig. 1 is a diagram corresponding to the claims of this invention, Fig. 2 is a block diagram of a vehicle failure diagnosis device showing an embodiment of the invention, and Fig. 3 is a diagram corresponding to claims of the present invention.
The figure is an explanatory diagram of general-purpose knowledge data, Fig. 4 is an explanatory diagram of a fault tree formed from the data shown in Fig. 3, and Fig. 5 is an explanatory diagram of a fault tree formed from the data shown in Fig. 3.
Figure 6 is an explanatory diagram of the knowledge data, and Figure 6 is an explanatory diagram of the fault tree.
Fig. 7 is an explanatory diagram of the knowledge data of vehicle type A, Fig. 8 is an explanatory diagram of its fault tree, Fig. 9 is a flowchart of the diagnosis process, and Fig. 10 is an explanatory diagram of the fault tree when vehicle type a is selected. , 1st
FIG. 1 is an explanatory diagram of a fault tree when general-purpose data and all unique knowledge data are combined. Fig. 12 is a schematic diagram of a conventional vehicle fault diagnosis device, Fig. 13 is an explanatory diagram of conventional knowledge data, Fig. 14 is an explanatory diagram of a fault tree formed by the knowledge data, and Fig. 15 is a conventional 2 is a processing flowchart of the vehicle failure diagnosis device of FIG. 11...General purpose failure information storage means 13...Vehicle type specific failure information storage means 15...Vehicle type information input means 17...Unique failure information selection means 19...Vehicle failure information formation means 21... Symptom information input means 23... Reasoning means 25... Notification means 27.17A... Vehicle failure diagnosis device patent applicant
Nissan Motor Co., Ltd.

Claims (1)

[Claims] General-purpose failure information storage means for storing general-purpose failure information that can be applied to any vehicle type; vehicle type-specific failure information storage means for storing failure information specific to each car type; vehicle type information input means for inputting car type information; specific failure information selection means for selecting specific failure information of a predetermined vehicle type from among the specific failure information for each vehicle type based on the vehicle type information input from the general-purpose failure information storage means and the specific failure information selection means; a failure information forming means for synthesizing the unique failure information selected by the means to form vehicle failure information according to the vehicle type; a symptom information input means for inputting symptom information; and a symptom information input means for inputting symptom information. An inference means for searching the vehicle failure information formed in the vehicle failure information forming means and inferring the cause of the failure of the symptom input from the symptom information input means, and a notification means for notifying the contents of the inference by the means. A vehicle fault diagnosis device consisting of: