TY - JOUR
T1 - Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine
AU - Haidong, Shao
AU - Hongkai, Jiang
AU - Xingqiu, Li
AU - Shuaipeng, Wu
N1 - Publisher Copyright:
© 2017 Elsevier B.V.
PY - 2018/1/15
Y1 - 2018/1/15
N2 - Unsupervised feature learning from the raw vibration data is a great challenge for rolling bearing intelligent fault diagnosis. In this paper, a novel method called deep wavelet auto-encoder (DWAE) with extreme learning machine (ELM) is proposed for intelligent fault diagnosis of rolling bearing. Firstly, wavelet function is employed as the nonlinear activation function to design wavelet auto-encoder (WAE), which can effectively capture the signal characteristics. Secondly, a DWAE is constructed with multiple WAEs to enhance the unsupervised feature learning ability. Finally, ELM is adopted as the classifier to accurately identify different bearing faults. The proposed method is applied to analyze the experimental bearing vibration signals, and the results confirm that the proposed method is superior to the traditional methods and standard deep learning methods.
AB - Unsupervised feature learning from the raw vibration data is a great challenge for rolling bearing intelligent fault diagnosis. In this paper, a novel method called deep wavelet auto-encoder (DWAE) with extreme learning machine (ELM) is proposed for intelligent fault diagnosis of rolling bearing. Firstly, wavelet function is employed as the nonlinear activation function to design wavelet auto-encoder (WAE), which can effectively capture the signal characteristics. Secondly, a DWAE is constructed with multiple WAEs to enhance the unsupervised feature learning ability. Finally, ELM is adopted as the classifier to accurately identify different bearing faults. The proposed method is applied to analyze the experimental bearing vibration signals, and the results confirm that the proposed method is superior to the traditional methods and standard deep learning methods.
KW - Deep wavelet auto-encoder
KW - Extreme learning machine
KW - Intelligent fault diagnosis
KW - Rolling bearing
KW - Unsupervised feature learning
UR - http://www.scopus.com/inward/record.url?scp=85035078800&partnerID=8YFLogxK
U2 - 10.1016/j.knosys.2017.10.024
DO - 10.1016/j.knosys.2017.10.024
M3 - 文章
AN - SCOPUS:85035078800
SN - 0950-7051
VL - 140
SP - 1
EP - 14
JO - Knowledge-Based Systems
JF - Knowledge-Based Systems
ER -