TY - GEN
T1 - Compound Fault Separation and Diagnosis Method Based on FSA-CNN and DAN
AU - Dong, Jiechao
AU - Yan, Liping
AU - Xia, Yuanqing
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - With the industry becoming larger and more complicated, the technology of fault diagnosis becomes more and more important. Due to the strong coupling and non-stationarity of the compound fault, the existing fault diagnosis methods cannot accurately identify all single faults' detailed information contained in the compound fault. This paper proposes a compound fault separation and diagnosis method based on FSACNN and DAN. Firstly, in order to highlight certain frequency segments, frequency segment attention module is added to CNN. Secondly, a compound fault feature separation framework based on DAN is proposed, which can separate compound fault to two fault components accurately. Thiredly, a signature matrix is introduced into ELM to improve the performance of the classifier. Finally, ablation experiments are designed to prove the advantage of the proposed method.
AB - With the industry becoming larger and more complicated, the technology of fault diagnosis becomes more and more important. Due to the strong coupling and non-stationarity of the compound fault, the existing fault diagnosis methods cannot accurately identify all single faults' detailed information contained in the compound fault. This paper proposes a compound fault separation and diagnosis method based on FSACNN and DAN. Firstly, in order to highlight certain frequency segments, frequency segment attention module is added to CNN. Secondly, a compound fault feature separation framework based on DAN is proposed, which can separate compound fault to two fault components accurately. Thiredly, a signature matrix is introduced into ELM to improve the performance of the classifier. Finally, ablation experiments are designed to prove the advantage of the proposed method.
KW - Attention module
KW - Compound fault separation
KW - Convolutional neural network
KW - Domain adversarial network
KW - Fault diagnosis
UR - http://www.scopus.com/inward/record.url?scp=85126892107&partnerID=8YFLogxK
U2 - 10.1109/SAFEPROCESS52771.2021.9693588
DO - 10.1109/SAFEPROCESS52771.2021.9693588
M3 - Conference contribution
AN - SCOPUS:85126892107
T3 - 2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2021
BT - 2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2021
Y2 - 17 December 2021 through 18 December 2021
ER -