Early Classification for Bearing Faults of Rotating Machinery Based on MFES and Bayesian Network

摘要：

　　Bearing faults of rotating machinery are observed as impulses in the vibration signal,but it is mostly immersed in noise.In order to effectively remove this noise and detect the impulses,a novel technique with multiple frequency energy spectrum (MFES) and Bayesian network(BN) inference is proposed in this paper.Original acceleration signals are processed by fast Fourier transformation (FFT) from the time domain to frequency domain.According to the analysis of the frequency information,the MFES is put forward to extract features from vibration under normal and faulty conditions of rotational mechanical systems.These features were given as inputs for training and testing the BN model.By existing BN inference algorithms,and the inference result for fault diagnosis is provided.With BN inference algorithms being coupled to this new technique,it makes the presented approach be able to detect early faults.Experimental results show that the proposed approach is effective and robust in bringing out the early bearing fault classification of rotating machinery.

关键词： MFES Bayesian Network Signal Processing Algorithms Fault Classification Rotating Machinery

作者: Wenqiang Guo Qiang Zhou Yongyan Hou Zoe Zhu Jingjing Yang Baorong Zhang

作者单位: College of Electrical & Information,Shaanxi University of Sci. & Tech., Xian, Shaanxi, China, 71002 Department of Computing Information Science, University of Guelph,Canada,N1G 2W1

会议类型: 国际会议

会议名称: the 25th Chinese Control and Decision Conference(第25届中国控制与决策会议)

会议地点: 贵阳

会议语种:英文

页码: 1066-1071

在线出版日期: 2013-05-01（万方平台首次上网日期，不代表论文的发表时间）