TY - GEN
T1 - Nonnegative matrix factorization and artificial immune based classification for fault diagnosis of diesel valve train
AU - Yang, Yongsheng
AU - Li, Gang
AU - Zhu, Yongsheng
AU - Zhang, Youyun
PY - 2013
Y1 - 2013
N2 - To efficiently mine the classification model for machine fault diagnosis based on images, a hybrid classification algorithm, which inspired by combining nonnegative matrix factorization and artificial immune system, was put forward. In the algorithm, nonnegative matrix factorization was employed for dimensionality reduction of the time-frequency spectral images. An artificial immune based classification model was constructed by means of training of data samples mapped into low-dimensional space to recognize the machine conditions and diagnose faults. Experimental results on the fault classification of diesel valve train demonstrate the effectiveness of the algorithm. Compared with probabilistic neural network classifiers, the hybrid classifier achieves better fault diagnosis performance.
AB - To efficiently mine the classification model for machine fault diagnosis based on images, a hybrid classification algorithm, which inspired by combining nonnegative matrix factorization and artificial immune system, was put forward. In the algorithm, nonnegative matrix factorization was employed for dimensionality reduction of the time-frequency spectral images. An artificial immune based classification model was constructed by means of training of data samples mapped into low-dimensional space to recognize the machine conditions and diagnose faults. Experimental results on the fault classification of diesel valve train demonstrate the effectiveness of the algorithm. Compared with probabilistic neural network classifiers, the hybrid classifier achieves better fault diagnosis performance.
KW - artificial immune system
KW - fault diagnosis
KW - nonnegative matrix factorization
KW - time-frequency image
UR - https://www.scopus.com/pages/publications/84885631860
U2 - 10.1109/CIDM.2013.6597245
DO - 10.1109/CIDM.2013.6597245
M3 - 会议稿件
AN - SCOPUS:84885631860
SN - 9781467358958
T3 - Proceedings of the 2013 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
SP - 262
EP - 266
BT - Proceedings of the 2013 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
T2 - 2013 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013
Y2 - 16 April 2013 through 19 April 2013
ER -