An imbalanced domain-adversarial hypergraph convolutional network for robust fault diagnosis of rotating machinery
点击次数：
发表刊物：Expert Systems with Applications
关键字：Fault diagnosis; Imbalanced domain adaptation; Hypergraph neural network; Domain adversarial learning; Rotating machinery
摘要：Reliable fault diagnosis for rotating machinery becomes challenging when operating conditions change across domains and fault data exhibit long-tailed class distributions. Under such settings, conventional deep learning models often suffer from degraded transferability and biased recognition of rare but critical faults. This paper proposes IDAHGCN, an Imbalanced DomainAdversarial Hypergraph Convolutional Network that jointly addresses domain shift and class imbalance. IDAHGCN constructs a hypergraph to encode group-wise relations among samples, which enables structural representation learning beyond pairwise connections. On this basis, a domain-adversarial hypergraph convolutional network is employed to learn features that remain discriminative while reducing cross-domain discrepancy. To further improve minority-fault recognition, IDAHGCN integrates class-balanced reweighting, class-wise alignment, and marginbased regularization. Experiments on two widely used rotating machinery datasets under multiple imbalanced domain adaptation protocols demonstrate consistent improvements over representative unsupervised and imbalanced domain adaptation baselines, measured by accuracy as well as imbalance-sensitive metrics such as Macro-F1 and G-mean. Additional analyses on noise robustness and computational efficiency further support the practicality of the proposed framework for safetycritical industrial diagnosis.
合写作者：Yuansheng Luo,Miao Chen,Yaguang Guo
第一作者：Zhangjun Wu
论文类型：期刊论文
通讯作者：Haidong Shao
学科门类：工学
文献类型：J
是否译文：否
发表时间：2026-02-26
收录刊物：SCI
附件：