Bearing fault diagnosis method based on quantum genetic algorithm optimized support vector machine

Abstract

A bearing fault diagnosis method based on a quantum genetic algorithm optimized support vector machine of the invention comprises the following steps: (1) acquiring a vibration signal of a bearing; (2) calculating dimensionless indexes; (3) optimizing model parameters C and Sigma of a support vector machine based on a cloud model quantum genetic algorithm; (4) training a support vector machine model; (5) performing fault diagnosis using the support vector machine model; and (6) outputting a bearing fault diagnosis result. The bearing fault diagnosis method of the invention has the advantage of high diagnosis accuracy. A new method is provided for solving the problem of bearing fault diagnosis.

Description

technical field [0001] The invention relates to a bearing fault diagnosis method, in particular to a bearing fault diagnosis method based on quantum genetic algorithm optimization support vector machine. Background technique [0002] With the advancement of science and technology, the internal structure of mechanical equipment is becoming more and more complex, the functions are becoming more and more powerful, and the level of intelligence is getting higher and higher. Bearings are the core components of machinery. If the bearings of mechanical equipment fail during operation, the entire production process may be interrupted, which not only affects the economic benefits of the enterprise, but also may endanger the personal safety of the staff, and even bring catastrophic consequences. In order to avoid losses caused by the failure of mechanical bearings, it is necessary to monitor the operating conditions of mechanical bearings, to predict failures before failures or to qu...

AI Technical Summary

Problems solved by technology

The existing bearing fault diagnosis methods have the problems of long training time of the algorithm and the accuracy of the diagnosis needs to be improved

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