Fault Diagnosis of Rolling Bearings of Different Working Conditions Based on Multi-Feature Spatial Domain Adaptation

Abstract

The running state of rolling bearings is complex in operation, and the data are generally collected under different working conditions. However, when single-source domain adaptation is used to model the heterogeneously distributed data obtained under different working conditions, the domaininvariant representations can hardly be used for representation, which directly affects the fault diagnosis rate. To this end, a method for the fault diagnosis of rolling bearings under different working conditions based on multi-feature spatial domain adaptation is proposed. Firstly, all the data from source and target domains are mapped into a feature space to learn the common representations of all domains. Secondly, the data for each pair of source and target domains are mapped into different feature spaces to get the fault feature representations under various working conditions. And the multi-domain adaptation network is used for the domain-specific distribution alignment to learn multiple domain-invariant representations. Thirdly, these representations are used to train multiple domain-specific classifiers, thus obtaining the recognition result for each domain-invariant representation. Finally, the domain-specific decision boundaries predicted by multiple classifiers are employed to align the classifiers' output of target samples and thus to reduce the influence from different classifiers. The effectiveness and feasibility of this proposed method have been verified by diagnostic experiments conducted according to the rolling bearing data from Case Western Reserve University and Laboratory, respectively.

Using Data From Similar Systems for Data-Driven Condition Diagnosis and Prognosis of Engineering Systems: A Review and an Outline of Future Research Challenges

“…As previously mentioned with adversarial training using DANN, there are approaches that use the outputs of multiple classifiers for transductive transfer learning (B2.3). Another approach to this, but without adversarial training, was presented by Wen et al [271]. They utilized multiple source domains for the condition diagnosis of bearings.…”

A Systematic Literature Review on Transfer Learning for Predictive Maintenance in Industry 4.0

“…Instance-based [25], [26], [27] Feature-based [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75],…”

“…Second, the sensor value distribution differs from the sensor monitoring data with a single operating condition. As discussed in [68], domain-invariant representations are difficult to understand when single-source domain adaptation is employed to explain the distribution of obtained data under various working conditions. To tackle this issue, Wen et al [68] applied a technique based on multi-feature spatial domain adaptation.…”

“…As discussed in [68], domain-invariant representations are difficult to understand when single-source domain adaptation is employed to explain the distribution of obtained data under various working conditions. To tackle this issue, Wen et al [68] applied a technique based on multi-feature spatial domain adaptation. To achieve more accurate homogeneous transfer knowledge in the TIM scenario, Tian et al [142] developed a multi-source subdomain adaptation transfer learning approach by employing a multibranch network structure and local MMD method.…”