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 domain-invariant 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.
Highlights
Rotating machinery is critical to the efficiency and safety of mechanical systems [1]
The data of all source and target domains are mapped into a feature space to learn the common domain-invariant representations
We propose an approach based on a multi-feature spatial adaptation that aligns domain-specific distributions of each pair of source and target domains by learning multiple domain-invariant representations and the classifiers’ output from multiple domains
Summary
Rotating machinery is critical to the efficiency and safety of mechanical systems [1]. The data about each pair of source and target domains are mapped into multiple different feature spaces, and the domainspecific distribution is aligned with in order to learn multiple domain-invariant representations These representations are used to train multiple domain-specific classifiers. We propose an approach based on a multi-feature spatial adaptation that aligns domain-specific distributions of each pair of source and target domains by learning multiple domain-invariant representations and the classifiers’ output from multiple domains. 2) Traditional methods use only a set of source domain data for training, while our approach collected many sets of source domain data that has been monitored from different working conditions to identify the target data, which extracts various fault features from multiple source domains to achieve a more effective result.
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