Fault Diagnosis Method for the Rolling Bearing Based on Information Fusion and BP Neural Network

Abstract

Abstract. In order to diagnose the fault of rolling bearing by the vibration signal, a new method of fault diagnosis based on weighted fusion and BP (Back Propagation) neural network was put forward. At first, the vibration signal from the sensors was wave filtered through the method of correlation function, then the fused signal was obtained by the classical adaptive weighted fusion method, the multi-type characteristics parameters was to be as a neural network input. Finally, the fault diagnosis of rolling bearing was realized by the BP neural network, and the results show that the multi-sensor information fusion fault diagnosis method can be proved effectively to achieve the fault diagnosis of rolling bearing.

A Misfire Fault Diagnosis System Based on Improved Neural Network

“…At the same time , adding to inertia item and changing the learning step size of neural network can enhance the convergence rate of neural network greatly. At last, the improved neural network is used to carry on the diagnosis of misfiring fault [5].…”