TY - GEN
T1 - Reconstruction independent component analysis-based methods for intelligent fault diagnosis
AU - Lei, Yaguo
AU - Shan, Hongkai
AU - Jia, Feng
AU - Lin, Jing
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/9/13
Y1 - 2016/9/13
N2 - Based on machine learning techniques, this paper presents a novel intelligent fault diagnosis method, which is an integrated framework concerning reconstruction independent component analysis (RICA) and multiclass relevance vector machine (MRVM). In this method, the RICA is first used to automatically extract features from raw vibration signals. Then, the learned features are used as the input data of MRVM for the classification of different health conditions of machines. The proposed method is applied to the fault diagnosis of locomotive rolling bearings. According to the diagnosis results, it is verified that the proposed method is able to reliably classify different health conditions. By comparing with diagnosis method based on time-domain statistical analysis and wavelet transformation, the proposed method shows its superiority in automatic features extraction from raw signals.
AB - Based on machine learning techniques, this paper presents a novel intelligent fault diagnosis method, which is an integrated framework concerning reconstruction independent component analysis (RICA) and multiclass relevance vector machine (MRVM). In this method, the RICA is first used to automatically extract features from raw vibration signals. Then, the learned features are used as the input data of MRVM for the classification of different health conditions of machines. The proposed method is applied to the fault diagnosis of locomotive rolling bearings. According to the diagnosis results, it is verified that the proposed method is able to reliably classify different health conditions. By comparing with diagnosis method based on time-domain statistical analysis and wavelet transformation, the proposed method shows its superiority in automatic features extraction from raw signals.
KW - automatic features extraction
KW - intelligent fault diagnosis
KW - multiclass relevance vector machine
KW - reconstruction independent component analysis
UR - https://www.scopus.com/pages/publications/84991716562
U2 - 10.1109/CSCWD.2016.7565996
DO - 10.1109/CSCWD.2016.7565996
M3 - 会议稿件
AN - SCOPUS:84991716562
T3 - Proceedings of the 2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2016
SP - 245
EP - 250
BT - Proceedings of the 2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2016
A2 - Liu, Xiaoping P.
A2 - Yong, Jianming
A2 - Barthes, Jean-Paul
A2 - Shen, Weiming
A2 - Yang, Chunsheng
A2 - Luo, Junzhou
A2 - Chen, Limin
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2016
Y2 - 4 May 2016 through 6 May 2016
ER -