Fault diagnosis of hydrogen sensor based on wavelet singular entropy and relevance vector machine

Aiming at the fault problem of hydrogen sensor, an intelligent fault diagnosis method which can diagnose and distinguish the fault state of the sensor was proposed. The fault diagnosis method based on wavelet singular entropy and relevance vector machine was researched, the feature of fault signal was extracted completely by combining the theory of the wavelet transform and singular entropy. The niche particle swarm optimization algorithm was used to optimize kernel parameter of RVM, and the accuracy of the fault diagnosis was improved. The proposed method was compared with other mature algorithms. Results indicates that the fault diagnosis recognizable rate reaches 98%. It resolves the problem of sensor fault diagnosis under the condition of nonlinear and small sample, and promote the reliability of sensor.