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Method for mechanical fault diagnosis based on information entropies and evidence theory

A technology of mechanical failure and evidence theory, applied in the direction of informatics, electrical digital data processing, special data processing applications, etc., can solve problems such as wrong diagnosis results

Active Publication Date: 2016-03-02
HARBIN ENG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The above three methods directly use evidence theory for information fusion, which may produce wrong diagnostic results in noisy environments and limited sensor accuracy

Method used

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  • Method for mechanical fault diagnosis based on information entropies and evidence theory
  • Method for mechanical fault diagnosis based on information entropies and evidence theory
  • Method for mechanical fault diagnosis based on information entropies and evidence theory

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Experimental program
Comparison scheme
Effect test

preparation example Construction

[0083] Step:6: Evidence synthesis. Using the improved evidence synthesis method based on the conflict between corrected evidences, the obtained basic probability assignment function is combined to obtain the synthesis result. Specifically include the following steps:

[0084] Step 6.1: Calculate the distance function between pairs of evidence according to the Mingshi distance;

[0085] m i ,m j (i,j=1,2,...,M) is the recognition frame Θ={R 1 , R 2 , R 3 , R 4}'s two BPAs, the construction method is shown in formula (11).

[0086] m i ( R l ) = p 1 ...

Embodiment 1

[0114] Embodiment 1: It is assumed that the measurement environment is good and the accuracy of the sensor is good. Under MATLAB simulation conditions, simulate a misalignment fault, assuming that the number of sensors M = 4, the information entropy weight w 1 =w 2 =0.1,w 3 =w 4 =0.4, preset threshold ε=0.03, ε 1 =0.15,ε 2 =0.4, the beneficial effects of the present invention are simulated:

[0115] Fault characteristic reference values ​​of typical mechanical fault types The specific value of is shown in Table 1, the fault characteristic F of the vibration signal received by the sensor j =[H j1 ,H j2 ,H j3 ,H j4 ] are shown in Table 2.

[0116] Table 1 Reference values ​​of fault characteristics of four typical mechanical fault types

[0117]

[0118] Table 2 Fault characteristics of vibration signals of four sensors

[0119]

[0120] Basic probability assignment function m for sensor assignment to typical mechanical failure types i (R j ) is shown in Ta...

Embodiment 2

[0131] Embodiment 2: It is assumed that the measurement environment is harsh and the accuracy of the sensor is poor. Under the MATLAB simulation condition, simulate a misalignment fault, the setting of concrete parameter is identical with embodiment 1, and the beneficial effect of the present invention is carried out simulation experiment:

[0132] Obtain the fault feature F of the sensor vibration signal j =[H j1 ,H j2 ,H j3 ,H j4 ], the specific values ​​are shown in Table 6.

[0133] Table 6 Fault characteristics of vibration signals of four sensors

[0134]

[0135] Basic probability assignment function m for sensor assignment to typical mechanical failure types i (R j ) is shown in Table 7.

[0136] Table 7 The basic probability assignment function assigned by the sensor to each typical mechanical fault type

[0137]

[0138] It can be seen from Table 7 that now the sensor S 1 , S 2 , S 3 Both use the maximum degree of confidence to judge that the type o...

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Abstract

The invention relates to a method for mechanical fault diagnosis based on information entropies and an evidence theory. The method comprises the following steps of step 1 adopting four typical mechanical fault types to construct a recognition framework; step 2 taking four information entropies of a vibration signal as fault characteristics; step 3 computing to obtain fault characteristic reference values of the four typical mechanical fault types through analogue simulation; step 4 obtaining a fault vibration signal received by a sensor, and computing to obtain fault characteristic values thereof through the information entropies; step 5 utilizing a fault characteristic extraction method based on weighted information entropies to obtain sensor vibration signals to be distributed to basic probability assignment functions of the four typical mechanical fault types; step 6 utilizing an improved evidence synthesizing method based on a conflict between revised evidences to carry out evidence synthesizing on the obtained basic probability assignment functions in order to obtain a synthesized result; and step 7 obtaining a final result for fault diagnosis according to a decision rule.

Description

technical field [0001] The invention relates to the technical field of mechanical fault diagnosis signal processing, in particular to a mechanical fault diagnosis method based on information entropy and evidence theory. Background technique [0002] With the rapid development of science and technology and modern industry, machinery and equipment in the machinery, energy, petrochemical, transportation and national defense industries of the national economy are increasingly large-scale, high-speed, integrated and automated. How to ensure the safe operation of machinery and equipment has become the current problem research focus. Accurate diagnosis of mechanical faults can deal with the faults in a targeted and timely manner, which is of great significance to ensure the safe operation of mechanical equipment. Fault diagnosis is a new discipline gradually formed based on reliability theory, information theory, cybernetics and system theory, using modern testing instruments and ...

Claims

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Application Information

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IPC IPC(8): G06F19/00
CPCG16Z99/00
Inventor 李一兵陈杰王秋滢林云叶方聂伟王彦欢罗仁欢杨子健
Owner HARBIN ENG UNIV
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