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Sensors 2018, 18(7), 2235; https://doi.org/10.3390/s18072235

An Effective Singular Value Selection and Bearing Fault Signal Filtering Diagnosis Method Based on False Nearest Neighbors and Statistical Information Criteria

1
Graduate School of Bioresources, Mie University, 1577 Kurimamachiya-cho, Tsu, Mie 5148507, Japan
2
College of Mechanical & Electrical Engineering, Beijing University of Chemical Technology, Chao Yang District, Beijing 100029, China
3
Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology, Jiangnan University, 1800 Li Hu Avenue, Wuxi 214122, China
*
Authors to whom correspondence should be addressed.
Received: 26 May 2018 / Revised: 7 July 2018 / Accepted: 10 July 2018 / Published: 11 July 2018
(This article belongs to the Special Issue Sensors for Fault Detection)

Abstract

Singular value decomposition (SVD) is an effective method used in bearing fault diagnosis. Ideally two important problems should be solved in any diagnosis: one is how to decide the dimension embedding of the trajectory matrix (TM); the other is how to select the singular value (SV) representing the intrinsic information of the bearing condition. In order to solve such problems, this study proposed an effective method to find the optimal TM and SV and perform fault signal filtering based on false nearest neighbors (FNN) and statistical information criteria. First of all, the embedded dimension of the trajectory matrix is determined with the FNN according to the chaos theory. Then the trajectory matrix is subjected to SVD, which is helpful to acquire all the combinations of SV and decomposed signals. According to the similarities of the signal changed back and signal in normal state based on statistical information criteria, the SV representing fault signal can be obtained. The spectrum envelope demodulation method can be used to perform effective analysis on the fault. The effectiveness of the proposed method is verified with simulation signals and low-speed bearing fault signals, and compared with the published SVD-based method and Fast Kurtogram diagnosis method. View Full-Text
Keywords: false nearest neighbors; statistical information criteria; selection of effective singular value; low-speed bearing fault diagnosis false nearest neighbors; statistical information criteria; selection of effective singular value; low-speed bearing fault diagnosis
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Liao, Z.; Song, L.; Chen, P.; Guan, Z.; Fang, Z.; Li, K. An Effective Singular Value Selection and Bearing Fault Signal Filtering Diagnosis Method Based on False Nearest Neighbors and Statistical Information Criteria. Sensors 2018, 18, 2235.

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