Int J Performability Eng ›› 2017, Vol. 13 ›› Issue (7): 1165-1170.doi: 10.23940/ijpe.17.07.p20.11651170

• Original articles • Previous Articles    

Fault Diagnosis for Machinery based on Feature Selection and Probabilistic Neural Network

Haiping Li*, Jianmin Zhao, Xinghui Zhang, and Xianglong Ni   

  1. Mechanical Engineering College, Shijiazhuang, 050003, China

Abstract: Fault diagnosis for the maintenance of machinery is more difficult since it becomes more precise, automatic and efficient. To tackle this problem, a feature selection and probabilistic neural network-based method is presented in this paper. Firstly, feature parameters are extracted and selected after obtaining the raw signal. Then, the selected feature parameters are preprocessed according to the faulted characteristic frequencies of components. Finally, the diagnosis results are outputted with the decision method of PNN. Experimental data is utilized to demonstrate the effectiveness of this methodology.

Submitted on May 31, 2017; Revised on October 10, 2017; Accepted on October 18, 2017
References: 13