Fault diagnosis method of rotating machinery based on adaptive multi-classified Markov Taguchi method

A technology for rotating machinery and fault diagnosis, applied in the testing of mechanical components, testing of machine/structural components, measuring devices, etc., can solve problems such as property loss, adverse social impact, damaged machinery, etc., to reduce professional requirements and increase engineering Applied Effects

Active Publication Date: 2019-02-01
BEIJING JIAOTONG UNIV
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Problems solved by technology

Bearing failure can have catastrophic results
Slight failures will damage machinery, and serious failures will cause serious casualties, property losses and adverse social impacts

Method used

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  • Fault diagnosis method of rotating machinery based on adaptive multi-classified Markov Taguchi method
  • Fault diagnosis method of rotating machinery based on adaptive multi-classified Markov Taguchi method
  • Fault diagnosis method of rotating machinery based on adaptive multi-classified Markov Taguchi method

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example

[0083] In this example, the rolling bearing fault signal provided by the Bearing Data Center of Western Reserve University is used for verification. Using sample signals under four states of normal, inner ring fault, outer ring fault and rolling element fault respectively, the present invention is based on the self-adaptive multi-classification Markov Taguchi method for detecting and verifying the rotating machinery fault diagnosis method, in order to test the present invention in practical application The role of the real situation is simulated by using the variable working condition data of the three states of inner ring fault, outer ring fault and rolling element fault of Western Reserve University. The specific steps are as follows:

[0084] Step 1: Perform EMD decomposition on the vibration signal of the bearing.

[0085] The number of signal samples in the four states is shown in Table 1.

[0086] Table 1 Number of samples in four states

[0087]

[0088] To perform...

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Abstract

The invention relates to a fault diagnosis method of rotating machinery based on an adaptive multi-classified Markov Taguchi method. The fault diagnosis method comprises: first, performing wavelet denoising and EMD to decompose the rotating mechanical vibration signal into multiple intrinsic mode functions (IMF); then, subjecting the initial feature matrix of each IMF component to singular value division using SVD, and taking the obtained singular value as a feature vector of a signal; finally, adaptively improving the multi-classified Markov Taguchi method, and using the new method as a classifier for fault diagnosis. The method of the invention can accurately identify and classify faults, and has high recognition accuracy, and the method is reliable.

Description

technical field [0001] The invention relates to a rotating machinery fault diagnosis method based on the self-adaptive multi-classification Marsh Taguchi method, which belongs to the technical field of fault diagnosis of mechanical components. Background technique [0002] Rotating machinery has a wide range of uses and has had a huge impact on social life. It is very important to ensure the normal operation of rotating machinery. Rolling bearing failure is the most common cause of failure in rotating machinery, and if a bearing fails, the machinery can be severely damaged. Failure of a bearing can have catastrophic results. Slight failures will damage machinery, and serious failures will cause serious casualties, property losses and adverse social impacts. Therefore, the monitoring of the running state of the rolling bearing and the analysis of the vibration signal of the bearing have been the research hotspots all the time. Identifying and diagnosing the fault state is ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01M13/045
CPCG01M13/045
Inventor 王志鹏王宁贾利民秦勇陈欣安耿毅轩
Owner BEIJING JIAOTONG UNIV
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