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Sensors 2012, 12(10), 13694-13719; doi:10.3390/s121013694

Spectral Regression Based Fault Feature Extraction for Bearing Accelerometer Sensor Signals

School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China
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Received: 21 July 2012 / Revised: 8 October 2012 / Accepted: 10 October 2012 / Published: 12 October 2012
(This article belongs to the Section Physical Sensors)

Abstract

Bearings are not only the most important element but also a common source of failures in rotary machinery. Bearing fault prognosis technology has been receiving more and more attention recently, in particular because it plays an increasingly important role in avoiding the occurrence of accidents. Therein, fault feature extraction (FFE) of bearing accelerometer sensor signals is essential to highlight representative features of bearing conditions for machinery fault diagnosis and prognosis. This paper proposes a spectral regression (SR)-based approach for fault feature extraction from original features including time, frequency and time-frequency domain features of bearing accelerometer sensor signals. SR is a novel regression framework for efficient regularized subspace learning and feature extraction technology, and it uses the least squares method to obtain the best projection direction, rather than computing the density matrix of features, so it also has the advantage in dimensionality reduction. The effectiveness of the SR-based method is validated experimentally by applying the acquired vibration signals data to bearings. The experimental results indicate that SR can reduce the computation cost and preserve more structure information about different bearing faults and severities, and it is demonstrated that the proposed feature extraction scheme has an advantage over other similar approaches. View Full-Text
Keywords: feature extraction; spectral regression; bearing accelerometer sensor; fault diagnosis; fault prognosis feature extraction; spectral regression; bearing accelerometer sensor; fault diagnosis; fault prognosis
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Xia, Z.; Xia, S.; Wan, L.; Cai, S. Spectral Regression Based Fault Feature Extraction for Bearing Accelerometer Sensor Signals. Sensors 2012, 12, 13694-13719.

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