Rotating machine fault diagnosis method based on wavelet packet decomposition

Abstract

The invention discloses a rotating machine fault diagnosis method based on wavelet packet decomposition. The method comprises the following steps: 1) collecting vibration signals of a rotating machinein a normal state and a fault state; 2) selecting a wavelet basis function for fault feature extraction; 3) according to the selected wavelet basis function, obtaining sub-signals of different frequency bands of the vibration signal through wavelet packet decomposition; 4) calculating a fuzzy entropy value of the sub-signal to obtain a fault feature vector; 5) performing feature importance sorting according to the correlation, and selecting a set number of fault feature vectors with top sorting results according to the sorting results; 6) using a classifier to construct a fault diagnosis model, dividing the selected fault feature vector and the category label into a training set and a test set, and using the training set as the input of the model to train the model; and 7) inputting the test set into the fault diagnosis model to obtain a fault diagnosis result. According to the method, high-quality fault features can be effectively extracted, and the accuracy of fault diagnosis is improved.

Description

technical field [0001] The invention relates to mechanical fault diagnosis technology, in particular to a method for fault diagnosis of rotating machinery based on wavelet packet decomposition. Background technique [0002] As a key component in the transmission system, rotating machinery is widely used in industrial production such as motors, engines, bearings, and gearboxes. The key components of rotating machinery are prone to failure under harsh or complex working conditions, which directly affects the mechanical performance and even seriously affects production safety. Therefore, constructing a fault diagnosis scheme for rotating machinery under complex working conditions is of great significance to ensure the safe operation of equipment and reduce economic losses. [0003] In the field of rotating machinery fault diagnosis, vibration signal acquisition, feature extraction and fault mode recognition are three important aspects, and feature extraction directly affects t...

AI Technical Summary

Problems solved by technology

However, the above method still has some drawbacks

For example, EMD has disadvantages such as endpoint effects, mode aliasing, under-envelope and over-envelope, while LMD has disadvantages of mode aliasing and computational inefficiency

In addition, these methods are all based on "modes", and their sub-signals may lose some frequency components of the original signal

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