A Novel Bayesian Deep Dual Network With Unsupervised Domain Adaptation for Transfer Fault Prognosis Across Different Machines

The existing deep learning-based fault prognostic methods require massive labeled condition monitoring (CM) data to train a well-generalized model. However, acquiring massive labeled CM data for real-case machines is infeasible due to time, economic costs, and safety concerns. Fortunately, we can handily obtain labeled CM data from relevant but different machines such as from accelerated degradation experiments in laboratories, which contain partially shared prognosis knowledge correlated to real-case machines. Accordingly, to bridge this practical gap, a novel Bayesian deep dual network with domain adaptation is developed to achieve transfer fault prognosis across different machines with distinct structures, measurement settings, and operating conditions. A deep convolutional neural network (DCNN)-multiple layer perceptron (MLP) dual network is first employed to extract abundant degradation representations from time series-based and time-frequency spectrum-based raw features. Then, domain adaptation regularization is imposed to relieve significant distribution discrepancy issue existing across different machines. Finally, the proposed DCNN-MLP dual network integrated with domain adaptation module is extended into Bayesian dual network through variational-inference (VI)-based method. The experimental verification demonstrates that the proposed method can accurately predict the remaining useful life percentage of testing bearings without any labeled CM data in target domain, and comparisons with other existing methods are also included.

Automated summary

Existing fault prognostic methods based on deep learning require massive labeled data, which is not feasible for real machines. To overcome this limitation, a novel Bayesian deep dual network with domain adaptation is proposed to transfer fault prognosis across different machines with distinct structures and conditions.