TY - JOUR

T1 - Cross-Domain Augmentation Diagnosis

T2 - An Adversarial Domain-Augmented Generalization Method for Fault Diagnosis under Unseen Working Conditions

AU - Li, Qi

AU - Chen, Liang

AU - Kong, Lin

AU - Wang, Dong

AU - Xia, Min

AU - Shen, Changqing

N1 - This is the author’s version of a work that was accepted for publication in Reliability Engineering and System Safety. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Reliability Engineering and System Safety, 234, 2023 DOI: 10.1016/j.ress.2023.109171

PY - 2023/6/30

Y1 - 2023/6/30

N2 - Intelligent fault diagnosis based on domain adaptation has recently been extensively researched to promote reliability of safety-critical assets under different working conditions. However, target data may be inaccessible in the model training phase, resulting in the degradation or failure of the diagnosis model. Therefore, this paper introduces a new idea called cross-domain augmentation (CDA) to achieve diagnosis under unseen working conditions, which are frequently occurred in industrial scenarios. To realize this idea, an adversarial domain-augmented generalization (ADAG) method is proposed with domain augmentation via convex combination of data and feature-label pairs. Through adversarial training on multi-source domains and the augmented domain, ADAG enables learning generalized and augmented features, which are proximal representation in the unseen domain, facilitating the generalization ability of the model. Moreover, feature extractor and domain classifier are optimized as adversaries in model training to obtain domain-invariant features, while the fault classifier is trained to identify the features. Extensive experiment studies indicate that ADAG can successfully solve the cross-domain diagnosis problem under unseen working conditions. For SDUST case study, ADAG promotes the model accuracy by 1.44%; while for a more challenging Ottawa case study, it promotes the model accuracy by 5.34%. Moreover, the domain discrepancy is reduced by 4.6%.

AB - Intelligent fault diagnosis based on domain adaptation has recently been extensively researched to promote reliability of safety-critical assets under different working conditions. However, target data may be inaccessible in the model training phase, resulting in the degradation or failure of the diagnosis model. Therefore, this paper introduces a new idea called cross-domain augmentation (CDA) to achieve diagnosis under unseen working conditions, which are frequently occurred in industrial scenarios. To realize this idea, an adversarial domain-augmented generalization (ADAG) method is proposed with domain augmentation via convex combination of data and feature-label pairs. Through adversarial training on multi-source domains and the augmented domain, ADAG enables learning generalized and augmented features, which are proximal representation in the unseen domain, facilitating the generalization ability of the model. Moreover, feature extractor and domain classifier are optimized as adversaries in model training to obtain domain-invariant features, while the fault classifier is trained to identify the features. Extensive experiment studies indicate that ADAG can successfully solve the cross-domain diagnosis problem under unseen working conditions. For SDUST case study, ADAG promotes the model accuracy by 1.44%; while for a more challenging Ottawa case study, it promotes the model accuracy by 5.34%. Moreover, the domain discrepancy is reduced by 4.6%.

KW - Domain augmentation

KW - Fault diagnosis

KW - Unseen working condition

KW - Rotating machinery

KW - Domain generalization

U2 - 10.1016/j.ress.2023.109171

DO - 10.1016/j.ress.2023.109171

M3 - Journal article

VL - 234

JO - Reliability Engineering and System Safety

JF - Reliability Engineering and System Safety

SN - 0951-8320

M1 - 109171

ER -