Bearing fault diagnosis method

Abstract

The invention discloses a bearing fault diagnosis method. The method comprises the following steps: carrying out fault feature extraction on a rolling bearing vibration signal; constructing an SVM classifier model; updating penalty parameters and kernel function parameters of a classifier of the SVM according to a training set and an IWOA algorithm; constructing a test model of the SVM according to the obtained optimal penalty parameter and the optimal kernel function parameter; and according to a test set, optimal penalty and the optimal kernel function parameters, adopting the longest optimization time, the shortest optimization time, the average optimization time, the average accuracy and the standard deviation as evaluation criteria to determine a fault result of a bearing after ten-fold cross validation. The method is high in bearing fault diagnosis capability and high in recognition accuracy.

Description

technical field [0001] The invention belongs to the technical field of machinery, and in particular relates to a bearing fault diagnosis method. Background technique [0002] Rolling bearings are one of the most common transmission parts among many parts of mechanical equipment, and they are vulnerable and consumable parts. Especially in machines running at high speed, the fault diagnosis of bearings plays an important role in ensuring its safe and reliable operation. Therefore, it is of great significance to quickly, accurately and conveniently diagnose bearing faults and identify the types of faults. [0003] At present, scholars at home and abroad have conducted a lot of research on the theory and technology of fault diagnosis of rolling bearings. The rolling bearing fault diagnosis method based on Ensemble Empirical Mode Decomposition (EEMD) singular value entropy criterion can clearly divide the category characteristic intervals of different working states of rolling ...

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Problems solved by technology

[0004] Whale Optimization Algorithm (Whale Optimization Algorithm, WOA) is a novel group meta-heuristic optimization algorithm for simulating the hunting behavior of humpback whales proposed by Mirjalili S et al. in 2016. It has the advantages of less parameters and strong robustness, but the basic WOA algorithm may also have the defects of slow convergence speed and stagnation in the later stage of convergence, which still needs further improvement

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