Rolling bearing fault diagnosis method based on improved variational model decomposition and extreme learning machine

Abstract

The invention discloses a rolling bearing fault diagnosis method based on improved variational model decomposition and an extreme learning machine. The method comprises: vibration signals of a rollingbearing under different types of faults are collected, the vibration signals are filtered by means of maximum correlation kurtosis deconvolution, parameter optimization is carried out on the maximumcorrelation kurtosis deconvolution method by using a particle swarm algorithm, and an enveloped energy entropy after signal deconvolution is used as a fitness function; the mode number of variationalmodel decomposition is improved by an energy threshold and improved variational model decomposition of the filtered vibration signals is realized to obtain mode matrixes of the corresponding vibrationsignals; singular value decomposition is carried out on the mode matrixes to obtain a singular value vector and a rolling bearing fault feature set is constructed; and the fault feature set is trained by using an extreme learning machine and a rolling bearing fault diagnosis model is established. Therefore, stable feature extraction of the complex vibration signal of the rolling bearing is realized, so that the diagnostic accuracy is improved.

Description

technical field [0001] The invention belongs to the field of mechanical fault diagnosis, in particular to a rolling bearing fault diagnosis method based on improved variational mode decomposition and extreme learning machine. Background technique [0002] Rolling bearings are one of the indispensable general mechanical components in rotating machinery, and are widely used in metallurgy, aerospace, chemical and other fields. Due to the long-term repeated action of the working surface and contact stress of the rolling bearing and the long-term high-speed, high-load, high-temperature environment, the rolling bearing is prone to failures such as fatigue and cracks, which will cause abnormal vibration of the rolling bearing and affect its associated equipment. Even the performance of the whole machine (accuracy, reliability, etc.), in actual production, the possibility of equipment failure caused by rolling bearing failure is the greatest. Therefore, it is of great significance ...

AI Technical Summary

Problems solved by technology

[0004] Wavelet transform has unique advantages in dealing with nonlinear signals. It can perform local refinement analysis on the time domain and frequency domain of the signal. However, the selected wavelet bases are different, and the decomposition results are different. At present, there is still no good theory for the selection of wavelet bases.

EMD can adaptively decompose the signal into multiple intrinsic mode functions, but due to recursive decomposition, it is easy to make the decomposition error pass down layer by layer, causing problems such as mode aliasing, over-envelope, and endpoint effects. Based on the development of EMD EEMD still doesn't solve the problem very well

HHT is through the EMD decomposition of the signal, which gets rid of the shackles of linearity and stability, but lacks rigorous physical meaning, requires complex recursion, and takes a long time to calculate, making HHT more effective only under the condition of relatively simple combination

The variational mode decomposition algorithm overcomes the modal aliasing phenomenon of EMD, but there is no theoretical basis for the setting of the mode number parameter, which has a great influence on the signal decomposition

BP neural network is a more commonly used state recognition method at present. It has the advantages of strong generalization ability and fault tolerance, nonlinear mapping, etc., but there are also problems that the network structure is difficult to determine and the convergence speed is slow.

Compared with BP neural network, support vector machine has faster convergence speed, but it has some problems such as difficult to determine parameters and long training time.

[0005] In the existing technology, the fault diagnosis of rolling bearings has problems such as inaccurate feature extraction, long time for establishing a diagnostic model, and low diagnostic accuracy.

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