Variable working condition bearing fault diagnosis method based on multi-scale dispersion entropy partial mean value and nonlinear mode decomposition

A pattern decomposition and fault diagnosis technology, applied in mechanical bearing testing, character and pattern recognition, testing of mechanical components, etc., can solve problems such as inability to accurately select effective components, achieve high fault recognition and overcome ineffectiveness

Active Publication Date: 2020-04-10
ANHUI UNIVERSITY OF TECHNOLOGY
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Problems solved by technology

The present invention can overcome the problem that effective components cannot be accurately selected when nonlinear mode decomposition is used to process complex signals; secondly, it can accurately diagnose time-varying signals generated by bearings under variable working conditions, and overcome the traditional order ratio analysis method when processing complex time-varying signals. invalidity of

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  • Variable working condition bearing fault diagnosis method based on multi-scale dispersion entropy partial mean value and nonlinear mode decomposition
  • Variable working condition bearing fault diagnosis method based on multi-scale dispersion entropy partial mean value and nonlinear mode decomposition
  • Variable working condition bearing fault diagnosis method based on multi-scale dispersion entropy partial mean value and nonlinear mode decomposition

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[0081] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention.

[0082] In this embodiment, the method for diagnosing bearing faults under variable operating conditions based on multi-scale dispersed entropy partial mean value and nonlinear mode decomposition includes the following steps:

[0083] Step 1-1: Collect the original fault signal of the bearing to be diagnosed under variable working conditions;

[0084] Step 1-2: Decompose the collected original fault signal by using nonlinear mode decomposition;

[0085] Step 1-3: Use the multi-scale dispersion entropy index to calculate the obtained components, and obtain the partial mean value of the multi-scale dispersion entropy;

[0086] Step 1-4: Select the component corr...

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Abstract

The invention discloses a variable working condition bearing fault diagnosis method based on a multi-scale dispersion entropy partial mean value and nonlinear mode decomposition, which belongs to thetechnical field of fault diagnosis. The method comprises the following steps of: acquiring a fault original signal of a to-be-diagnosed bearing under a variable working condition, decomposing the acquired original fault signal by adopting nonlinear mode decomposition, calculating the obtained component by adopting a multi-scale dispersion entropy index to obtain a multi-scale dispersion entropy partial mean value, selecting a component corresponding to the maximum partial mean value, and identifying the fault type of the bearing by adopting order analysis. According to the method, the variableworking condition bearing fault signals are decomposed through nonlinear mode decomposition, the multi-scale walk entropy index-partial mean value is adopted to select the component containing the most fault information, and the fault type can be accurately judged.

Description

Technical field: [0001] The invention relates to the technical field of fault diagnosis, in particular to a fault diagnosis method for variable working condition bearings based on multi-scale dispersed entropy partial mean value and nonlinear mode decomposition (NMD). Background technique: [0002] Rolling bearings are widely used in rotating machinery, and they are also one of the most easily damaged parts, especially when the speed changes drastically. The time-varying signal generated under this working condition often exhibits non-stationarity, and its fault characteristic frequency changes with the change of the speed, and some weak fault characteristics that are not easy to reflect may also appear, so that the conventional analysis of the stationary signal method fails. Therefore, it is of great significance to carry out bearing fault diagnosis under variable speed conditions. In recent years, Empirical Mode Decomposition, Ensemble Empirical Mode Decomposition, Varia...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G01M13/045G01M13/04
CPCG01M13/04G01M13/045G06F18/213
Inventor 郑近德刘庆运丁克勤王兴龙潘海洋童靳于
Owner ANHUI UNIVERSITY OF TECHNOLOGY
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