Multi-view rotating machinery fault diagnosis with adaptive co-attention fusion network

Intelligent fault diagnosis is an intriguing topic, attracting increasing interest in safe and reliable industrial production. Tremendous progress has been made in recent years in developing better fault diagnosis methods. Nevertheless, most methods rely on an individual vibration signal while ignoring the consensus and complementary between different views of the signal. Towards this end, we propose a novel method named COFU, i.e., a multi-view learning model with CO-attention FUsion network for rotating machinery fault diagnosis, which primarily exploits consensus and complementary across multiple views. Specifically, we first utilize three different encoders to construct high-level feature spaces of multiple views. Then the adaptive co-attention fusion network is designed to learn an integrated representation where rich associations among these feature spaces are fully considered. Finally, the fault detector fed by the fused representation is devised to diagnose the fault category. To affirm the efficacy of the proposed approach, a comprehensive evaluation has been conducted on the CWRU, SEU_bearing, and SEU_gear datasets. The results indicate that the accuracy of the COFU method is 100%, 99.95%, and 100%, respectively. Encouraging findings demonstrate that our method outperforms all the baseline methods. Furthermore, it is observed that the COFU method demonstrates improved performance when applied in noisy environments. This study offers a promising solution that ensures the great potential of multi-view fusion in rotating machinery fault diagnosis.