Bearing fault diagnosis method based on parameter adaptive VMD and optimized SVM

Abstract

The invention discloses a bearing fault diagnosis method based on parameter adaptive VMD and an optimized SVM. The method comprises the following steps: S1, collecting original vibration signals of a bearing; S2, performing the adaptive VMD on the original vibration signals to obtain K component signals; S3, screening an optimal component signal IMF from the K component signals by using a time-frequency weighted kurtosis index, and dividing the screened IMF into a training set and a test set; S4, inputting the training set into the optimized SVM for model training, and obtaining a machine learning model capable of judging bearing faults after the training is completed; and S5, inputting the test set into the machine learning model so as to output a bearing fault diagnosis result. According to the method, the problem that it is difficult for VMD parameters to be self-adaptive is solved, so that accurate diagnosis of the bearing faults is realized, the diagnosis precision is high, and a reliable basis is provided for safe and stable operation of equipment.

Description

technical field [0001] The invention relates to the field of bearing vibration signal processing and fault diagnosis in mechanical equipment, in particular to a bearing fault diagnosis method based on parameter adaptive VMD and optimized SVM. Background technique [0002] Rolling bearings are important components of rotating machinery, and their operating status affects the health of the entire rotating machinery. At the same time, rolling bearings are subjected to various dynamic loads and harsh operating conditions, which makes rolling bearings face huge risks of failure and aggravated deterioration. Relevant studies have shown that the proportion of rotating machinery failures caused by bearings is as high as 30%. Therefore, accurate and effective fault diagnosis for rolling bearings is very important. [0003] For rolling bearing fault diagnosis, the following problems still exist: [0004] (1) In the face of a large number of complex unsteady vibration signals, how to...

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Problems solved by technology

[0007] The purpose of the present invention is to provide a bearing fault diagnosis method based on parameter adaptive VMD and optimized SVM, which solves the problem that VMD decomposition parameters are difficult to self-adapt, and uses more accurate indicators to screen the best mode with fault characteristic frequency components, and adopt the optimized SVM algorithm model that can independently optimize the best parameters, so as to realize the accurate diagnosis of gear faults, with high diagnostic accuracy, and provide a reliable basis for the safe and stable operation of the equipment

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