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

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...

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This will make the wavelet transform extremely complex

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