Open AccessSlip Hankel matrix series‐based singular value decomposition and its application for fault feature extraction
Author(s) -
Xu Jian,
Tong Shuiguang,
Cong Feiyun,
Chen Jin
Publication year - 2017
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2016.0176
Subject(s) - hankel matrix , singular value decomposition , control theory (sociology) , bearing (navigation) , fault (geology) , feature extraction , impulse (physics) , singular value , engineering , algorithm , computer science , mathematics , artificial intelligence , mathematical analysis , physics , eigenvalues and eigenvectors , quantum mechanics , seismology , geology , control (management)
The failure of rolling bearings is one of the most important factors for rotating machinery breakdown. The detection of initial fault in rolling bearings is crucial for the further prevention of equipment malfunction and failure. In this study, a new rolling bearing fault diagnosis method based on the singular value decomposition, slip Hankel matrix series construction and maximum singular value energy analysis is proposed. It has been validated that the proposed method has an excellent impulse recognition capacity, which can be further applied to design the optimal band‐pass filter for rolling bearing fault diagnosis. Then, the minimum entropy deconvolution (MED) technique is introduced to improve the fault extraction ability of the proposed method. Simulated signals and artificial fault tests are used to prove the capacity of the new method for rolling bearing fault detection. Furthermore, the result of accelerated life test indicates the initial bearing fault can be recognised by the proposed method, while the envelope spectrum cannot directly distinguish the failure type because of the redundant frequency interference. It can be concluded that the proposed method has the effectiveness of initial fault identification and redundant frequency elimination for rolling bearing fault diagnosis.