TY - GEN
T1 - A novel pattern matching-based fault diagnosis using canonical variate analysis for industrial process
AU - Xu, Yuan
AU - Fan, Cuihuan
AU - Zhu, Qunxiong
AU - He, Yanlin
AU - Hu, Qi
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - Original data with high dimension and noise are usually directly applied to pattern matching, which will affect the accuracy of models to some extent. To solve this issue, a novel pattern matching method integrating canonical variable analysis with adaptive rank-order morphological f lter (CVA-AROMF) is proposed for fault diagnosis in this article. First, canonical variable analysis (CVA) is used to extract the features of training data with sequence correlation and process dynamics, and then the features are used as the template signal of adaptive rank-order morphological f lter (AROMF). Second, the noise-bearing test signal is used to match the template morphology waveform under the supervision of different fault template signals. Third, the fault mode is classifieds by finding the minimal distance between the filter output signal and the raw test signal of each fault mode. Simulations based on Tennessee Eastman(TE) process data is performed and the result verifies the accuracy and superiority of this proposed method.
AB - Original data with high dimension and noise are usually directly applied to pattern matching, which will affect the accuracy of models to some extent. To solve this issue, a novel pattern matching method integrating canonical variable analysis with adaptive rank-order morphological f lter (CVA-AROMF) is proposed for fault diagnosis in this article. First, canonical variable analysis (CVA) is used to extract the features of training data with sequence correlation and process dynamics, and then the features are used as the template signal of adaptive rank-order morphological f lter (AROMF). Second, the noise-bearing test signal is used to match the template morphology waveform under the supervision of different fault template signals. Third, the fault mode is classifieds by finding the minimal distance between the filter output signal and the raw test signal of each fault mode. Simulations based on Tennessee Eastman(TE) process data is performed and the result verifies the accuracy and superiority of this proposed method.
KW - Adaptive rank-order morphological filter
KW - Canonical variate analysis
KW - Fault diagnosis
KW - Pattern matching
KW - Te process
UR - http://www.scopus.com/inward/record.url?scp=85076427433&partnerID=8YFLogxK
U2 - 10.1109/DDCLS.2019.8909051
DO - 10.1109/DDCLS.2019.8909051
M3 - Conference contribution
AN - SCOPUS:85076427433
T3 - Proceedings of 2019 IEEE 8th Data Driven Control and Learning Systems Conference, DDCLS 2019
SP - 1132
EP - 1136
BT - Proceedings of 2019 IEEE 8th Data Driven Control and Learning Systems Conference, DDCLS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2019
Y2 - 24 May 2019 through 27 May 2019
ER -