Intelligent fault diagnosis methods based on domain adaptation (DA) have been extensively employed for tackling domain shift problems, and the basic diagnosis tasks under time-varying working conditions were well achieved. Nevertheless, the existing methods usually focus on learning diagnosis knowledge from single-source domain while ignoring abundant underlying information in multisource domain. In practical scenarios, multiple source domains are available, and there are few studies in fault diagnosis based on multisource DA. To this end, a novel multisource domain feature adaptation network (MDFAN) is proposed for bearing fault diagnosis under time-varying working conditions. The proposed network first uses a feature extractor to learn transferable features from different pairs of source and target domains, and then, a domain-specific distribution alignment module is constructed, which adopts the intra-domain alignment strategy and the inter-domain alignment strategy to reduce the shift between all domain pairs. Meanwhile, considering the classifier prediction disagreement, a classifier alignment module is further designed to relieve the classifier prediction discrepancy and enhance the prediction consistency. Case studies on two real datasets with multiple sources demonstrate the effectiveness of the proposed network, and the comparison results show its robustness and superiority.

Multisource Domain Feature Adaptation Network for Bearing Fault Diagnosis Under Time-Varying Working Conditions

Transferable adaptive channel attention module for unsupervised cross-domain fault diagnosis

The vibration signal of gearboxes contains abundant fault information, which can be used for condition monitoring. However, vibration signal is ineffective for some non-structural failures. In order to resolve this dilemma, infrared thermal images are introduced to combine with vibration signals via fusion domain-adaptation convolutional neural network (FDACNN), which can diagnose both structural and non-structural failures under various working conditions. First, the measured raw signals are converted into frequency and squared envelope spectrum to characterize the health states of the gearbox. Second, the sequences of the frequency and squared envelope spectrum are arranged into two-dimensional format, which are combined with infrared thermal images to form fusion data. Finally, the adversarial network is introduced to realize the state recognition of structural and non-structural faults in the unlabeled target domain. An experiment of gearbox test rigs was used for effectiveness validation by measuring both vibration and infrared thermal images. The results suggest that the proposed FDACNN method performs best in cross-domain fault diagnosis of gearboxes via multi-source heterogeneous data compared with the other four methods.

https://www.mdpi.com/1099-4300/24/1/119/pdf?version=1642066138

Fusion Domain-Adaptation CNN Driven by Images and Vibration Signals for Fault Diagnosis of Gearbox Cross-Working Conditions

A knowledge dynamic matching unit-guided multi-source domain adaptation network with attention mechanism for rolling bearing fault diagnosis

Fault diagnosis approaches based on deep learning (DL) are subject to a prerequisite that training samples follow the same distribution as the test samples. Traditional transfer learning (TL)-based fault diagnosis approaches are mainstreamed on a single-source transfer and consider simple variable working conditions. However, rotating machineries work under variable working conditions, making the prerequisite for DL methods hard to satisfy in practical scenarios. Moreover, more than one factor, such as load and speed, can simultaneously affect the working conditions, resulting in complex transfer tasks. Therefore, we propose a new multi-source TL approach, named Optimal transport-based Multi-Source Student–teacher learning Network (OMSSN), to address bearing fault diagnosis under variable working conditions. The target-domain student classifier gains knowledge from the multi-source fault diagnostic teacher by applying optimal transport and continue optimizing along with iterations. A cluster optimization is also introduced to help the model make accurate fault predictions. A well-designed experiment is performed to prove the effectiveness of OMSSN, wherein the proposed and compared methods are tested under four working conditions composed of four load and speed conditions. Detailed experiments, including comparative evaluation and ablation study, confirm the effectiveness of the proposed method.

Xu Shuo

Papers

Multisource domain factorization network for cross-domain fault diagnosis of rotating machinery: An unsupervised multisource domain adaptation method