TY - GEN
T1 - Intelligent Fault Diagnosis Using Limited Data Under Different Working Conditions Based on SEflow Model and Data Augmentation
AU - Li, Sijue
AU - Peng, Gaoliang
AU - Mao, Daoyong
AU - Zhu, Zhiyu
AU - Ji, Mengyu
AU - Chen, Yuanhang
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2021
Y1 - 2021
N2 - Accurate fault diagnosis of machine components is quite important for normal operation of equipment. Nowadays, artificial intelligent methods have been widely researched in fault diagnosis of rolling element bearings (REB). However, due to the variation of machine working conditions, the diagnosis accuracy always degrade seriously. Besides, as it is really hard to achieve large amounts of labeled health condition signals from real equipment, data deficiency is another trouble. Both issues impede the practical application of data-driven fault diagnosis. So as to solve the problems, a data augmentation method SEflow based on squeeze-and-excitation networks (SEnet) and flow-generative model is proposed. Proposed SEflow can learn the data distributions from limited data, then generate augmented signals among different machine working conditions. The experiments applied on bearing datasets and ball screw signals verify the effectiveness of proposed method on solving domain adaption and data deficiency.
AB - Accurate fault diagnosis of machine components is quite important for normal operation of equipment. Nowadays, artificial intelligent methods have been widely researched in fault diagnosis of rolling element bearings (REB). However, due to the variation of machine working conditions, the diagnosis accuracy always degrade seriously. Besides, as it is really hard to achieve large amounts of labeled health condition signals from real equipment, data deficiency is another trouble. Both issues impede the practical application of data-driven fault diagnosis. So as to solve the problems, a data augmentation method SEflow based on squeeze-and-excitation networks (SEnet) and flow-generative model is proposed. Proposed SEflow can learn the data distributions from limited data, then generate augmented signals among different machine working conditions. The experiments applied on bearing datasets and ball screw signals verify the effectiveness of proposed method on solving domain adaption and data deficiency.
KW - Data augmentation
KW - Deep learning
KW - Domain adaption
KW - Flow-based generative model
KW - Intelligent fault diagnosis
UR - https://www.scopus.com/pages/publications/85105880952
U2 - 10.1007/978-981-33-6420-2_58
DO - 10.1007/978-981-33-6420-2_58
M3 - 会议稿件
AN - SCOPUS:85105880952
SN - 9789813364196
T3 - Smart Innovation, Systems and Technologies
SP - 475
EP - 484
BT - Advances in Intelligent Information Hiding and Multimedia Signal Processing - Proceeding of the 16th International Conference on IIHMSP in Conjunction with the 13th International Conference on FITAT 2020
A2 - Pan, Jeng-Shyang
A2 - Li, Jianpo
A2 - Namsrai, Oyun-Erdene
A2 - Meng, Zhenyu
A2 - Savić, Miloš
PB - Springer Science and Business Media Deutschland GmbH
T2 - 16th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information Technology, Applications and Tools, FITAT 2020
Y2 - 5 November 2020 through 7 November 2020
ER -