NDLI: Early classification for bearing faults of rotating machinery based on MFES and Bayesian network

Content Provider	IEEE Xplore Digital Library
Author	Wenqiang Guo Qiang Zhou Yongyan Hou Zhu, Z. Jingjing Yang Baorong Zhang
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. Inf. Sci., Univ. of Guelph, Guelph, ON, Canada (Zhu, Z.) \|\| Coll. of Electr. & Inf., Shaanxi Univ. of Sci. & Tech., Xi'an, China (Wenqiang Guo; Qiang Zhou; Yongyan Hou; Jingjing Yang; Baorong Zhang)
Abstract	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.
Sponsorship	IEEE Control Syst. Soc.
Starting Page	1066
Ending Page	1071
File Size	268346
Page Count	6
File Format	PDF
ISBN	9781467355339
e-ISBN	9781467355346
DOI	10.1109/CCDC.2013.6561082
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault diagnosis Vibrations Rotating Machinery MFES Frequency-domain analysis Signal processing algorithms Bayesian Network Fault Classification Educational institutions Bayes methods Signal Processing Algorithms Machinery
Content Type	Text
Resource Type	Article

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