TY - JOUR
T1 - Interpretable data-augmented adversarial variational autoencoder with sequential attention for imbalanced fault diagnosis
AU - Liu, Yunpeng
AU - Jiang, Hongkai
AU - Yao, Renhe
AU - Zhu, Hongxuan
N1 - Publisher Copyright:
© 2023
PY - 2023/12
Y1 - 2023/12
N2 - Data augmentation with imbalanced samples is tentative and tricky for rolling bearing fault diagnosis in practice. However, its complex hierarchical knowledge and opaque decision rules prevent users from trusting the outputs. In this paper, an interpretable data-augmented adversarial variational autoencoder with sequential attention (AVAE-SQA) is proposed for assisting imbalanced fault diagnosis. Imbalance fault diagnosis is supported by AVAE-SQA, which synthesizes data to supplement the imbalanced dataset for meeting the needs of intelligent diagnostic models. Firstly, an adversarial variational inference is defined, which is optimized by the reliable control limit (RCL) with dual bounds. The data distributions are estimated better due to dual bounds; that is, inter-class data augmentation is facilitated by RCL. Then, AVAE with an adversarial process is designed to ensure the feasibility and controllability of RCL while improving its accuracy and generalization. Thirdly, SQA is constructed for global dependencies without overfocusing. Furthermore, SQA illustrates the decision rules of AVAE, which coincide with the failure mechanism of rolling bearings. Various cases are adopted to evaluate the effectiveness of the proposed method. Experiments confirm that AVAE-SQA is preferred over other prevailing approaches in fault diagnosis with imbalanced samples and is potentially promising for engineering applications.
AB - Data augmentation with imbalanced samples is tentative and tricky for rolling bearing fault diagnosis in practice. However, its complex hierarchical knowledge and opaque decision rules prevent users from trusting the outputs. In this paper, an interpretable data-augmented adversarial variational autoencoder with sequential attention (AVAE-SQA) is proposed for assisting imbalanced fault diagnosis. Imbalance fault diagnosis is supported by AVAE-SQA, which synthesizes data to supplement the imbalanced dataset for meeting the needs of intelligent diagnostic models. Firstly, an adversarial variational inference is defined, which is optimized by the reliable control limit (RCL) with dual bounds. The data distributions are estimated better due to dual bounds; that is, inter-class data augmentation is facilitated by RCL. Then, AVAE with an adversarial process is designed to ensure the feasibility and controllability of RCL while improving its accuracy and generalization. Thirdly, SQA is constructed for global dependencies without overfocusing. Furthermore, SQA illustrates the decision rules of AVAE, which coincide with the failure mechanism of rolling bearings. Various cases are adopted to evaluate the effectiveness of the proposed method. Experiments confirm that AVAE-SQA is preferred over other prevailing approaches in fault diagnosis with imbalanced samples and is potentially promising for engineering applications.
KW - Adversarial variational autoencoder
KW - Decision rules
KW - Imbalanced fault diagnosis
KW - Interpretable data augmentation
KW - Reliable control limits
KW - Sequential attention
UR - http://www.scopus.com/inward/record.url?scp=85173488158&partnerID=8YFLogxK
U2 - 10.1016/j.jmsy.2023.09.019
DO - 10.1016/j.jmsy.2023.09.019
M3 - 文章
AN - SCOPUS:85173488158
SN - 0278-6125
VL - 71
SP - 342
EP - 359
JO - Journal of Manufacturing Systems
JF - Journal of Manufacturing Systems
ER -