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Shock and Vibration
Volume 2014 (2014), Article ID 957531, 9 pages
http://dx.doi.org/10.1155/2014/957531
Research Article

Quantitative Diagnosis of Rotor Vibration Fault Using Process Power Spectrum Entropy and Support Vector Machine Method

1School of Energy and Power Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
2School of Computer Science and Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
3School of Life Science, Beijing Normal University, Beijing 100875, China

Received 19 November 2013; Revised 8 March 2014; Accepted 10 March 2014; Published 31 March 2014

Academic Editor: Valder Steffen

Copyright © 2014 Cheng-Wei Fei et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

To improve the diagnosis capacity of rotor vibration fault in stochastic process, an effective fault diagnosis method (named Process Power Spectrum Entropy (PPSE) and Support Vector Machine (SVM) (PPSE-SVM, for short) method) was proposed. The fault diagnosis model of PPSE-SVM was established by fusing PPSE method and SVM theory. Based on the simulation experiment of rotor vibration fault, process data for four typical vibration faults (rotor imbalance, shaft misalignment, rotor-stator rubbing, and pedestal looseness) were collected under multipoint (multiple channels) and multispeed. By using PPSE method, the PPSE values of these data were extracted as fault feature vectors to establish the SVM model of rotor vibration fault diagnosis. From rotor vibration fault diagnosis, the results demonstrate that the proposed method possesses high precision, good learning ability, good generalization ability, and strong fault-tolerant ability (robustness) in four aspects of distinguishing fault types, fault severity, fault location, and noise immunity of rotor stochastic vibration. This paper presents a novel method (PPSE-SVM) for rotor vibration fault diagnosis and real-time vibration monitoring. The presented effort is promising to improve the fault diagnosis precision of rotating machinery like gas turbine.