Multi-sensor open-set cross-domain intelligent diagnostics for rotating machinery under variable operating conditions

Domain adaptation techniques have the proven ability to deal with fault diagnosis issues under variable operating conditions. They can achieve a superb diagnostic performance in single-sensor monitoring scenarios where the training and test data share the same label space. However, in practical engineering, fault modes are usually mixed with each other and new failure modes may appear during operation, which poses a challenge to the effectiveness of existing cross-domain fault diagnosis methods. Furthermore, with the increasing complexity of modern industrial systems, multi-sensor collaborative monitoring has been increasingly deployed for comprehensive measurement and detection of the complicated system. Unfortunately, there is less attention paid to multi-sensor cross-domain diagnosis in the current literature. To bridge this research gap, this paper aims to develop a novel multi-sensor open-set cross-domain fault diagnosis method. First, a convolutional neural network-based single-sensor feature extraction module and a Transformer-based multi-sensor feature fusion module are constructed for discriminative feature extraction and fusion. Second, a weighted adversarial learning scheme is built to conduct domain-invariant learning of the shared fault modes between the source and target domains. Then, a threshold-based supervised contrastive loss is defined to realize instance-level domain alignment, together with an entropy max–min loss to identify unknown class samples. The effectiveness and practicability of the proposed method are validated by a series of experiments conducted on two different types of gearboxes.