Bearing defect diagnosis by stochastic resonance with parameter tuning

The interference from background noise makes it difficult to identify the incipient defect of a bearing via vibration analysis. By the aid of stochastic resonance (SR), the unavoidable noise can, however, be applied to enhance the system output. The classical SR phenomenon requires small parameters, which is not suited for bearing defect diagnosis since the defect-induced frequency of a bearing is usually much higher than 1 Hz. This paper investigates the SR approach with parameter tuning for identifying the bearing defect. A new method of multiscale noise tuning is developed to realize weak signal detection via SR at a fixed noise level. The proposed SR model with multiscale noise tuning overcomes the limitation of small parameter requirement of the classical SR, and can thus detect a high driving frequency. The proposed model is well-suited for enhancement of bearing defect identification when the noise is present at different scales. It has shown more effective results than the traditional methods, which was verified by means of a practical bearing vibration signal carrying defect information.