OpensetCapsNet: A Collaborative Fault Diagnosis Framework for Emerging and Compound Faults of Machinery

Abstract

Abstract Developing intelligent fault diagnosis (IFD) methods based on deep neural networks has attracted considerable attention and brought successful breakthroughs for industrial applications in recent years. However, the historical IFD methods often concentrate on single diagnosis tasks, such as emerging fault detection or compound fault decoupling (CFD), leading to potential misdiagnosis and missed diagnosis, and also necessitate sufficient labeled data for model training in advance. Aiming to address such problems, a collaborative fault diagnosis framework, named the Open-Set Capsule Network (OpensetCapsNet), is proposed to mitigate the misdiagnosis of both emerging and compound faults. First, the OpensetCapsNet is constructed with four parts: a feature extractor for feature learning, an emerging fault detector for unknown fault detection, a compound fault classifier for intelligent CFD, and a domain discriminator for domain adaptation. Second, a collaborative training strategy is proposed based on adversarial learning and multitask learning. This strategy enables training the OpensetCapsNet with datasets that include both health and known fault samples collected from one working condition, alongside unlabeled samples from varying conditions. Finally, the cross-validation experiments were conducted on an automobile transmission, demonstrating the OpensetCapsNet’s capacity for collaborative execution of diagnosis tasks, encompassing known fault classification, emerging fault detection, and CFD. The proposed framework significantly reduces the risk of misdiagnosis and missed diagnosis, offering a substantial advancement for IFD in industrial applications.