An Intelligent Fault Diagnosis Method Enhanced by Noise Injection for Machinery

Machinery generally operates under severe and complex conditions, and therefore, the monitoring signals acquired from machinery would inevitably be accompanied by various types of noise in the process of data acquisition. Noise would result in the instability of intelligent fault diagnosis and prediction models and decline their recognition and prediction accuracy. In stochastic resonance, however, noise is beneficial to weak signal detection and intelligent image classification, while the research on the benefits of noise in mechanical intelligent fault diagnosis is still rare. For this purpose, the benefits of noise to the intelligent fault diagnosis are studied in this article by injecting different levels of Gaussian and uniform noise to intelligent fault diagnosis models and even their input datasets. Then, an intelligent fault diagnosis method enhanced by injecting moderate noise is proposed to improve the classification accuracy of those ones without noise injection. Finally, three experiments including hydraulic motors and two different motor bearings were performed to verify the proposed method. The experimental results show that the diagnosis accuracy of hydraulic motors and two different motor bearings after noise injection is 95%, 95.6%, and 97.5%, respectively, which is increased by 1.4%, 1.6%, and 1.1% than those without noise injection. Comparing the experimental results by injecting two different types of noise, all of them have the same optimal noise level to achieve fairly high classification accuracy. In addition, it is found that the diagnosis accuracy by injecting Gaussian noise is higher than that by injecting uniform noise.