JPH03142191A - Failure diagnostic device - Google Patents

Abstract

PURPOSE:To shorten a working time by providing a knowledge compiler which automatically forms a signal flow chart or causal matrix and further a fault tree for diagnosis from a transfer function. CONSTITUTION:A knowledge compiler 201 utilizes a computor 211 so as to form a diagnostic knowledge automatically on inputting of the transfer function of a diagnostic object, using a keyboard 208 as an input means and a CRT 209 or plotter 210 as an output means. Namely, the knowledge compiler 201 is divided into a 1st step knowledge compiler 202 and 2nd step knowledge compiler 203 according to the function thereof. The 1st step knowledge compiler 202 is to output a signal flow chart 205 and causal matrix 206 by means of the input of a transfer function 204 and also the 2nd step knowledge compiler 203 outputs a fault tree 207 for diagnosis, i.e., a diagnostic knowledge by means of the input of the causal matrix 206.

Description

DETAILED DESCRIPTION OF THE INVENTION <Industrial Application Field> The present invention relates to a failure diagnosis device suitable for drive units of extreme work robots, etc., and can also be applied to general mechanical devices.

<Prior Art> FIG. 10 shows a conventional failure diagnosis device 100. As shown in the figure, the failure diagnosis device 100 has a diagnostic knowledge file 10.
1. It consists of observation information input means 1022, diagnosis processing means 103, and diagnosis result display 104. Here, the diagnostic knowledge file 101 stores diagnostic knowledge. Observation information is input to observation information input means 102 . Diagnostic processing means 10
3 derives a diagnostic result based on diagnostic knowledge from the diagnostic knowledge file 101 and observation information from the observation information input means 102. The diagnosis result display 104 displays the location of the failure based on the diagnosis result from the diagnosis processing means 103.

<Problems to be Solved by the Invention> However, in the conventional fault diagnosis device 100, it was necessary to artificially convert diagnostic knowledge into a format that can be stored in a diagnostic knowledge file.

This has resulted in a complicated and time-consuming task.

The present invention has been made in view of such prior art, and an object of the present invention is to provide an efficient and accurate failure diagnosis device that can shorten the above-mentioned time and effort.

Means for Solving the Problems〉 The configuration of the present invention to achieve the above object is to provide a system for diagnosing robot failures based on diagnostic knowledge and observation information, which further performs diagnosis from transfer functions to signal flow diagrams or causal matrices. The system is characterized by having a knowledge compiler that automatically creates a fault tree for the system.

<Operation> When a transfer function to be diagnosed is input to the knowledge compiler, the transfer function is automatically converted by the knowledge compiler into a signal flow diagram, causal matrix, and further into a diagnostic fault tree so that it can be stored in a diagnostic knowledge file. It turns out.

Embodiments Hereinafter, the failure diagnosis apparatus of the present invention will be described in detail based on embodiments shown in the drawings.

FIG. 1 shows an embodiment of the present invention. The fault diagnosis device 200 shown in the same figure has a knowledge compiler 201 added thereto, a diagnostic knowledge file 101, and observation information input means 102.
2. The diagnostic processing means 103 and the diagnostic result display 104 are the same as those in the prior art shown in FIG.

Here, when the knowledge compiler 201 inputs the transfer function to be diagnosed, the computer 201 automatically generates diagnostic knowledge.
11 (see Figure 3), and a keyboard 208.11 as an input means. A CRT 209 or a plotter 210 is used as an output means. That is, as shown in FIG. 2, the knowledge compiler 201 is divided into a first stage knowledge compiler 202 and a second stage knowledge compiler 203 according to their functions.

The first stage knowledge compiler 202 outputs a signal flow diagram 205 and a causal matrix 206 based on the input of the transfer function 204, and the second stage knowledge compiler 203 outputs a diagnostic fault tree based on the input of the causal matrix 206. 207, that is, the diagnostic knowledge is output.

Input of the transfer function 204 is performed by, for example, a keyboard shown in FIG. 3, and output of the signal flow diagram 205 or diagnostic fault tree 207 is performed by the CRT 209 or plotter 210.

Here, the transfer function 204 is a fault vector.

It consists of an observation vector, a node and a branch, e.g.
Its contents are as follows.

Fault vector (Xl, N2.N3.N4) Observation vector (o,, o2.o3.o4) Node (N
,, N2. N,, N4. N5. N
6. N7) Branch G, = (N,.

G2-(01゜ G3-(X,.

G4 = (0□.

G5-(N2゜ G, = (03° G7=(N2゜ G8-(N3゜ G9-(N4゜ 0,. 1) 02, KA) 02, 1) N2,1) 03.11 N3・　K,) N3,1) N4. hundred ) N5. hundred ) G. -(N5,04.K.

G,,-(N4,0.,-1 G, 2-(N5.N6. I G, 3-(N6. N2.-1 G, 4 = (04, N7. K.

G,5-(N7.O,,-1 G,6=(X,,N6.l G17-(N4.N,, 1) When the above transfer function is input to the first-stage knowledge compiler 202, Processing is performed according to the procedure shown in Figure 6.Here, "input of transfer function" means inputting characters typed from the keyboard into the computer READ (for example, FO
RTRAN) command. The input transfer function is checked for errors by the manual data checking means 301 as shown in FIG. 4, and then divided into the words failure vector, M measurement vector, node, and branch by the input data word analysis storage means 302 and stored. be done. Next, "creating and outputting a signal flow diagram" is performed by drawing the signal flow diagram shown in FIG. 7 by the signal flow diagram drawing means 304. In FIG. 7, circles indicate nodes containing fault vectors and observation vectors, arrows indicate branches, and symbols above the circles indicate transmittance between nodes. For example, for branch GI, the human input signal N is connected to the output signal 0° with a transmittance of 1. The signal flow diagram 1 visually displays the contents of the input transfer function, and is useful for checking whether the input was performed correctly. On the other hand, the fault vector
Means 30 for determining the causal relationship from the fault vector to the observation vector R, B, from the failure vector to the observation vector
3 and the formula and formula calculation means 305. For example, the causal relationship between failure X1 and *SO, is calculated by the following formula processing.

Therefore, if there is no other failure, O,=-,,X.

There is this relationship. The above calculation method is based on Mason's formula (Hiroshi Ikeya, "Automatic Control Handbook Basic Edition", Instrument and Automatic Control Association, Ohmsha, P694, 1)
983).

"Create and output a causal matrix from the causal relationships" means that the causal matrix R is created as follows by means 306 for rearranging the causal relationships R to create a causal matrix.

The causal matrix R shows the relationship between the failure vector and the observed vector.

Next, when the causal matrix is input to the second stage knowledge compiler 203, processing is performed according to the means shown in FIG. That is, "inputting a causal matrix" means that the causal matrix simplification means 4 is input as shown in FIG.
The causal relationship R of the causal matrix input in 02 is
The causal matrix simplification information input to the causal matrix simplification information input means 401 includes, for example, simplification to zero. The "decomposition into causal knowledge" is performed by converting the causal matrix into causal knowledge as shown in the table below using the causal knowledge converting means 403 as shown in FIG.

For example, from the relationship 0 = -K
X, > Oma # is x3 < 0) and I F (0, > 0
), THEN(X, < 0 or X3>0).

Next, the observation O of the IF section in which there are many failures X in the rTHEN section is extracted'' is carried out by the higher-order observation vector extraction means 404. For example, in Table 1, there is a failure x,
x3. Observation O4 in the x4 OT HE N section is taken out and placed at the beginning of the diagnostic fault tree shown in FIG. Then, “X of the THEN part of the observation O4 taken out
``Extract the observation 0 of the IF section that has more of the same failures.''
For example, as shown in Table 1, X3. X4
The same fault X, take out more 01 and observe 0
Observation O1 comes next after 4. ``In this way, a diagnostic fault tree is created and output from the upper layer to the lower layer'' means that the above steps are repeated to complete and display the fault tree shown in FIG. 9. It should be noted that each time the means 406 for checking the presence or absence of an independent fault tree searches for a diagnostic fault tree, the diagnostic fault tree drawing means 405 creates and displays the diagnostic fault tree.

The fault tree for diagnosis is diagnostic knowledge used by the diagnostic processing means 103 for diagnosis.

1 <Effects of the Invention> As specifically explained above based on the embodiments, in the present invention, the diagnostic knowledge, that is, the diagnostic fault tree, which was conventionally created artificially, can be automatically created by the knowledge compiler. It saves time and reduces work time.

Therefore, it is possible to provide a more efficient and accurate failure diagnosis device according to the present invention.

[Brief explanation of the drawing]

FIG. 1 is an overall diagram of a fault diagnosis device according to an embodiment of the present invention, FIG. 2 is an explanatory diagram of a knowledge compiler of the fault diagnosis device shown in FIG. 1, and FIG. 3 is a diagram according to an embodiment of the present invention. An explanatory diagram showing the functional means of the knowledge compiler, Figure 4 is a structural diagram of the functional means of the first stage knowledge compiler, and Figure 5 is the second stage.
6 is a flow chart of the first stage knowledge compiler, FIG. 7 is a signal flow diagram, FIG. 8 is a flow chart of the second stage knowledge compiler, and FIG. 1 is an explanatory diagram of a diagnostic fault tree, and FIG. 10 is an explanatory diagram of a conventional failure diagnosis device. In the figure, 101 is a diagnostic knowledge file, 102 is an observation information input means, 103 is a diagnostic processing means, 104 is a diagnostic result display, 200 is a failure diagnosis device, 201 is a knowledge compiler, 202 is a first stage knowledge compiler, 203 is a second stage knowledge compiler, 204 is a transfer function, 205 is a signal flow diagram, 206 is a causal matrix, and 207 is a diagnostic fault tree. Patent application Ken Sugiura, Director of the Agency for Industrial Science and Technology

Claims (1)

[Claims] A device for diagnosing robot failures based on diagnostic knowledge and observation information, characterized by having a knowledge compiler that automatically creates a signal flow diagram or a causal matrix as well as a diagnostic fault tree from a transfer function. .