Bearing fault diagnostic method based on second generation wavelet transform and BP neural network

Abstract

The invention relates to a bearing fault mixing intelligent diagnostic method based on second generation wavelet transform and a BP neural network. The bearing fault diagnostic method based on the second generation wavelet transform and the BP neural network includes steps: firstly, using the second generation wavelet transform to resolve a bearing original vibration signal measured by a sensor; secondly, extracting time domain statistical features and frequency domain statistical features from the resolved signal so as to form a combined feature set, and then performing feature evaluation on the extracted feature set so as to obtain a sensitive feature set; using the sensitive feature set as input of the BP neural network for network training, and building a fault diagnostic model based on the BP neural network so as to achieve classification and diagnosis of faults. The bearing fault diagnostic method based on the second generation wavelet transform and the BP neural network and the fault diagnostic model based on the BP neural network are used in the classification and the diagnosis of the bearing faults, and results indicate that the bearing fault diagnostic method based on the second generation wavelet transform and the BP neural network is high in classification and diagnosis accuracy, high in speed and high in efficiency, effectively improves bearing fault diagnostic effects, and is conveniently used in engineering practice.

Description

technical field [0001] The invention belongs to the field of fault diagnosis of mechanical equipment, and relates to a fault diagnosis method for bearings based on second-generation wavelet transform and BP neural network. Background technique [0002] Rolling bearings are one of the important parts in rotating machinery, but due to processing technology, working environment and other reasons, their damage rate is high and their life is random. According to incomplete statistics, rolling bearing faults account for about 30% of the total number of rotating machinery faults. Therefore, mastering the working status of rolling bearings and the formation and development of faults is very important for ensuring the normal operation of rotating machinery. It is the current field of mechanical fault diagnosis. One of the important research directions. [0003] At present, the commonly used bearing fault diagnosis methods include mechanism analysis and intelligent diagnosis. The me...

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Problems solved by technology

[0004] However, due to its theory itself, the neural network method has some problems and deficiencies in practical application that restrict its generalization and promotion performance, such as initial value selection, hidden layer structure and node number selection, etc.

There have been a lot of research results on the improvement method of neural network. It can be said that it is more difficult to further improve the neural network itself to improve the diagnosis effect.

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