Simulation Research on Rolling Element Bearing Feature Extraction Based on Recursive Least-Squares Lattice-Ladder Algorithms

In order to extract the weak fault information from complicated vibration signal of rolling element bearing, the Recursive Least-Squares (RLS) Lattice-Ladder Algorithms is introduced into the field of rolling bearing feature extraction. An adaptive feature extraction method is proposed. The RLS Latt...

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Vydáno v:Applied Mechanics and Materials Ročník 548-549; číslo Achievements in Engineering Sciences; s. 481 - 486
Hlavní autoři: Zhang, Yong Xiang, Zhang, Shuai, Zhu, Jie Ping, Wang, Xiao Lin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Zurich Trans Tech Publications Ltd 28.04.2014
Témata:
Algorithms
Bearing
Faults
Feature extraction
Least squares method
Recursive
Roller bearings
Vibration
RLS Lattice-Ladder Algorithms
Envelope Analysis
Fault Diagnosis
Rolling Bearing
LM Algorithm
ISBN:3038350842, 9783038350842
ISSN:1660-9336, 1662-7482, 1662-7482
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Shrnutí:In order to extract the weak fault information from complicated vibration signal of rolling element bearing, the Recursive Least-Squares (RLS) Lattice-Ladder Algorithms is introduced into the field of rolling bearing feature extraction. An adaptive feature extraction method is proposed. The RLS Lattice-Ladder algorithms and its adaptive filter property in the process of feature extraction were discussed. The rolling bearing vibration signal was refined by the RLS Lattice-Ladder filter method, and the refined vibration signal was demodulated by square envelope, then the rolling bearing’s characteristic fault frequency was identified by enveloped normalized amplitude-frequency spectrum. Simulation results show that compared with the LMS filter method, this method can identify fault frequency more quickly and more effectively.
Bibliografie:Selected, peer reviewed papers from the 2014 3rd International Conference on Manufacturing Engineering and Process (ICMEP 2014), April 10-11, 2014, Seoul, Korea
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ISBN:3038350842
9783038350842
ISSN:1660-9336
1662-7482
1662-7482
DOI:10.4028/www.scientific.net/AMM.548-549.481