Incipient Fault Diagnosis for the Fastening Bolt Loosening of Limit Switch Based on Wavelet Transform and Support Vector Machine

The aim of incipient fault diagnosis is to detect the fault at its early stage. By the incipient fault diagnosis, the system fault can be detected before the malfunction of the system, and the loss of the system breakdown could be avoided. The limit switch is widely used in industry or traffic systems. However, there are few studies on the fault diagnosis of limit switches. In this paper, we propose a fault diagnosis method of limit switch for the loosening of fastening bolts based on wavelet transform and support vector machine (SVM) methods. In this fault diagnosis method, the voltage signal of limit switches is monitored and sampled. After the de-noising by the wavelet threshold method, the wavelet transform is used for feature extraction, and the SVM theory is applied for feature classification, which is optimized by a combined simulated annealing and particle swarm optimization procedure. Finally, an experiment is implemented, and the fault diagnosis method is verified.