Fault diagnosis for machinery based on feature selection and probabilistic neural network

被引：0
作者：
Li H. [1 ]
Zhao J. [1 ]
Zhang X. [1 ]
Ni X. [1 ]
机构：
[1] Li, Haiping
[2] Zhao, Jianmin
[3] Zhang, Xinghui
[4] Ni, Xianglong
来源：
Li, Haiping (hp_li@hotmail.com) | 1600年 / Totem Publishers Ltd卷 / 13期
关键词：
Fault characteristic frequency; Fault diagnosis; Feature selection; Probabilistic neural network;
D O I：
10.23940/ijpe.17.07.p20.11651170
中图分类号：
学科分类号：
摘要：
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. © 2017 Totem Publisher, Inc. All rights reserved.
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页码：1165 / 1170
页数：5
相关论文
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