A method of bearing condition monitoring and fault diagnosis based on tqwt assisted spc

Abstract

The invention provides a method for TQWT-assisted SPC bearing state monitoring and fault diagnosis. The core of the method is that TQWT has the ability to extract vibration characteristics of bearing faults. It includes the following steps: S1, decompose the vibration data under normal conditions into different wavelet coefficients by using TQWT; S2, determine two safety indicators in the state monitoring to carry out state monitoring by using the main wavelet coefficient and the residual coefficient; S3, through non- Parameter statistics and unilateral confidence limits of indicators to establish upper control limits; S4, establish the Shewhart control chart of multi-scale wavelet coefficients for fault diagnosis; S5, reconstruct fault signals using inverse TQWT and use Hilbert package The network spectrum improves the detection performance to obtain the fault type. The innovation of this method avoids too many assumptions about the distribution and stability of the data caused by the use of Wiener distribution. At the same time, this method can also effectively analyze the type of bearing failure. It is a method that can be applied to industrial applications. .

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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