Bearing state monitoring and fault diagnosis method based on TQWT auxiliary SPC

A fault diagnosis and bearing technology, applied in the testing of mechanical components, testing of machine/structural components, measuring devices, etc., can solve problems such as complex wavelet transform, and achieve strong effectiveness, practicability, and good performance.

Active Publication Date: 2020-04-10
JIANGSU UNIV
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Problems solved by technology

This will make the wavelet transform extremely complex

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  • Bearing state monitoring and fault diagnosis method based on TQWT auxiliary SPC
  • Bearing state monitoring and fault diagnosis method based on TQWT auxiliary SPC
  • Bearing state monitoring and fault diagnosis method based on TQWT auxiliary SPC

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Embodiment Construction

[0038] The embodiments of the present invention are described in detail below: this embodiment proceeds under the premise of the technical solution of the present invention

[0039] Carry out implementation, give out detailed implementation mode and specific operation process.

[0040] The invention provides a method for monitoring and fault diagnosis of a bearing based on TQWT-assisted SPC, which mainly includes the following steps:

[0041] 1. A method for bearing condition monitoring and fault diagnosis based on TQWT-assisted SPC, comprising:

[0042] S1: Decompose the vibration data under normal conditions into different wavelet coefficients by using the wavelet transform TQWT with adjustable Q factor, which can be parameterized as Q factor Q and redundancy r. Here we set the Q parameter to 3.5 and the r parameter to 3;

[0043] S2, first monitor the energy of the wavelet coefficients of several scales obtained after decomposition, and divide the wavelet coefficients decom...

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Abstract

The invention provides a bearing state monitoring and fault diagnosis method based on TQWT auxiliary SPC. The core of the method is that TQWT has the capability of extracting bearing fault vibration characteristics. The method comprises the following steps: S1, decomposing vibration data under a normal condition into different wavelet coefficients by utilizing TQWT; S2, determining two safety indexes according to a main wavelet coefficient and a residual coefficient in state monitoring so as to carry out state monitoring; S3, determining a control upper limit through non-parametric statisticsand a unilateral confidence limit of the index; S4, establishing a Schmidt control chart of the multi-scale wavelet coefficients to perform fault diagnosis; and S5, reconstructing the fault signal byusing the inverse TQWT, and improving the detection performance by using the Hilbert envelope spectrum to obtain the fault type. The innovation of the method avoids excessive assumption of data distribution and stability caused by use of Wiener distribution, and meanwhile, the method can also effectively analyze the fault type of the bearing, and is a method which can be applied to industrial application.

Description

technical field [0001] The invention belongs to the technical field of mechanical fault diagnosis, and in particular relates to a signal processing method of variable Q factor wavelet transform and a state monitoring and fault diagnosis method of multi-scale statistical process control. Background technique [0002] In contemporary society, bearings are one of the most important and widely used components in large mechanical systems. However, bearings always work at high speeds, high loads, and harsh environments, which makes them prone to defects. Therefore, the condition monitoring and fault diagnosis of bearings are the key to ensure high reliability, low cost and safe operation of major mechanical systems. Among multiple information sources, vibration signals contain information on the presence of damage and the type of fault, so vibration signals are a powerful signal source for detecting bearing faults. [0003] Let’s start with status monitoring. For status monitori...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01M13/045
CPCG01M13/045
Inventor 樊薇焦之远许桢英韩丽玲刘玉芹
Owner JIANGSU UNIV
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