Fault diagnosis based on dependent feature vector and probability neural network for rolling element bearings

Rolling element bearings (REB) are crucial mechanical parts of most rotary machineries, and REB failures often cause terrible accidents and serious economic losses. Therefore, REB fault diagnosis is very important for ensuring the safe operation of rotary machineries. In previous researches on REB fault diagnosis, achieving the accurate description of faults has always been a difficult problem, which seriously restricts the reliability and accuracy of the diagnosis results. In order to improve the precision of fault description and provide strong basis for fault diagnosis, dependent feature vector (DFV) is proposed to denote the fault symptom attributes of the six REB faults in this paper, and this is a self-adaptive fault representation method which describes each fault sample according to its own characteristics. Because of its unique feature selection technique and particular structural property, DFV is excellent in fault description, and could lay a good foundation for fault diagnosis. The advantages of DFV are theoretically proved via the Euclidean distance evaluation technique. Finally, a fault diagnosis method combining DFV and probability neural network (PNN) is proposed and applied to 708 REB fault samples. The experimental results indicate that the proposed method can achieve an efficient accuracy in REB fault diagnosis.