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A deep learning model trained under a specific operating condition of the gearbox often experiences an overfitting problem, which makes it impossible to diagnose faults under different operating conditions. To solve this problem, this paper proposes an ensemble of deep domain adaptation approaches with a health data map. As a fundamental approach to alleviate the domain shift problem due to inhomogeneous operating conditions, the vibration signal is transformed into an image-like simplified health data map that visualizes a tooth-wise fault of the gearbox. The simplified health data map enables the use of a conventional convolutional neural network (CNN) model. To solve the remaining domain shift problem even with the simplified health data map, this study employs a maximum classifier discrepancy (MCD), which is a typical domain adaptation method. To further enhance its performance, a discrepancy-scale factor-based MCD and its ensemble approach are proposed. The proposed method is demonstrated with a 2 kW planetary gearbox testbed operated under stationary and non-stationary speed conditions. The results present that the proposed method outperforms conventional CNN and MCD even under the inhomogeneous operating condition of the gearbox.

A Health Data Map-Based Ensemble of Deep Domain Adaptation Under Inhomogeneous Operating Conditions for Fault Diagnosis of a Planetary Gearbox