Transfer Learning for Remaining Useful Life Prediction Across Operating Conditions Based on Multisource Domain Adaptation

Nowadays, transfer learning and domain adaptation are widely used in prognostics and health management of rotating machinery, greatly broadening its applications in scenarios with multiple operating conditions. However, almost all existing prognostic methods employ a single-source domain adaptation (SDA), ignoring the domain-shift within the source domain, and fail to make full use of multi-source historical data. To overcome these drawbacks, a multisource domain adaptation network (MDAN) for regression tasks is proposed in this article. MDAN learns domain-invariant features and supervision from multiple sources and provides better generalization in the target domain than traditional SDA methods. Besides, a new MDAN-based framework is presented for remaining useful life prediction of bearings under multiple operating conditions. Specifically, we input the time-frequency representation of the vibration signals into MDAN to achieve end-to-end training and inference across operating conditions. Case studies on the run-to-failure datasets validate the effectiveness of proposed method, and the comparison shows that it outperforms the state-of-the-art methods.