Imperfection fault diagnosis rule extraction method based on quantitative characteristic relation

A technology for fault diagnosis and quantification of features, applied in general control systems, control/regulation systems, testing/monitoring control systems, etc., can solve problems such as incorrect diagnosis conclusions, changes in original information, and extraction of fault diagnosis rules, achieving low cost, The effect of improving the accuracy and improving the extraction accuracy

Active Publication Date: 2015-03-25
HARBIN INST OF TECH
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  • Claims
  • Application Information

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Problems solved by technology

[0005] The purpose of the present invention is to solve the problem that the existing incomplete fault diagnosis rule extraction method cannot extract the fault diagnosis rule from the decision table containing multiple unknown attribute values; the existing incomplete fault diagnosis rule extraction method does not consider the There is no problem of measuring the similarity between instances; and the existing artificial data filling process will cause the original information to change, and new noise will be introduced into the data, thereby mining the problem of wrong diagnostic conclusions

Method used

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  • Imperfection fault diagnosis rule extraction method based on quantitative characteristic relation
  • Imperfection fault diagnosis rule extraction method based on quantitative characteristic relation
  • Imperfection fault diagnosis rule extraction method based on quantitative characteristic relation

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specific Embodiment approach 1

[0022] Specific implementation mode one: combine figure 1 Illustrate this embodiment, a method for extracting incomplete fault diagnosis rules based on quantitative feature relationships, characterized in that the method includes the following steps:

[0023] Step 1. Preprocessing of the original incomplete fault diagnosis data: first obtain the original incomplete fault diagnosis data, then perform discretization processing on the original incomplete fault diagnosis data, obtain discrete data, and establish an incomplete fault diagnosis decision table;

[0024] Step 2. According to the definitions of the three unknown attribute values, determine the types of unknown attribute values ​​in the incomplete fault diagnosis decision table, and define corresponding symbolic representations;

[0025] Step 3: Analyze the incomplete fault diagnosis decision table by using quantitative feature relations: calculate the feature similarity between instances in the incomplete fault diagnosi...

specific Embodiment approach 2

[0028] Specific embodiment 2: The difference between this embodiment and specific embodiment 1 is that in said step 2, according to the definition of three unknown attribute values, the type of unknown attribute value in the incomplete fault diagnosis decision table is determined, and the corresponding symbols are defined express:

[0029] The definitions of the three unknown attribute values ​​are as follows:

[0030] Missing type: the attribute value exists but it cannot be obtained for some reason, represented by the symbol "?";

[0031] Missing type: the attribute value can be replaced by any typical value of this attribute, represented by the symbol "*";

[0032] Restricted: The attribute value can be replaced by any typical attribute value of the attribute except the missing attribute value, indicated by the symbol "+".

specific Embodiment approach 3

[0033] Specific embodiment three: the difference between this embodiment and specific embodiment one or two is: the definition of feature similarity in the step three is as follows:

[0034] Feature similarity represents instances xi and x j The feature similarity on the attribute set B is calculated according to formula (1),

[0035] VR B (x i ,x j )=∏ b∈B R b (x i ,x j )·N B (x i ,x j ) (1)

[0036] In formula (1), VR B (x i ,x j ) for instance x i and x j The feature similarity on the attribute set B, B represents the attribute set, b represents an attribute in the attribute set B; R b (x i ,x j ) for instance x i and x j Feature similarity on attribute b; N B (x i ,x j ) for instance x i and x j The proportion of attributes whose value is "?"

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Abstract

The invention relates to a fault diagnosis rule extraction method, in particular to an imperfection fault diagnosis rule extraction method based on the quantitative characteristic relation. The method solves the problem that according to an existing imperfection fault diagnosis rule extraction method, fault diagnosis rules cannot be extracted from a decision table containing various unknown attribute values, solves the problems that according to the existing imperfection diagnosis rule extraction method, similarity degrees among examples are not considered, and the similarity relation among the examples is not measured, and solves the problem that original information will change due to an existing manual data filling process. The imperfection diagnosis rule extraction method based on the quantitative characteristic relation comprises the steps of (1) preprocessing original imperfection fault diagnosis data, (2) determining the type of an unknown attribute value, (3) analyzing an imperfection fault diagnosis decision table, (4) conducting reduction, and (5) extracting fault diagnosis rules. The imperfection fault diagnosis rule extraction method is applied to the field of fault diagnosis rule extraction.

Description

technical field [0001] The invention relates to a method for extracting fault diagnosis rules. Background technique [0002] Fault diagnosis technology has been widely used in aviation, aerospace, shipbuilding and electric power and other fields. However, the harsh operating environment and complex working conditions of modern equipment make the status information of the equipment itself incomplete; and human practice is always limited by the objective environment and conditions, and the diagnostic information obtained to describe the failure mode often has a certain degree of inaccuracy. Incompleteness, such as failure of information collection device, temporary inability to obtain information, data preprocessing and human negligence, etc., so that the fault diagnosis data faced is incomplete. However, most of the existing fault diagnosis methods obtain valuable diagnostic knowledge from complete fault diagnosis information, which leads to the failure to make full use of i...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B23/02
CPCG05B23/0205
Inventor 黄文涛于军赵学增王伟杰
Owner HARBIN INST OF TECH
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