Transfer learning using deep representation regularization in remaining useful life prediction across operating conditions

Intelligent data-driven system prognostic methods have been popularly developed in the recent years. Despite the promising results, most approaches assume the training and testing data are from the same operating condition. In the real industries, it is quite common that different machine entities work under different scenarios, that results in performance deteriorations of the data-driven prognostic methods. This paper proposes a transfer learning method for remaining useful life predictions using deep representation regularization. The practical and challenging scenario is investigated, where the training and testing data are from different machinery operating conditions, and no target-domain run-to-failure data is available for training. In the deep learning framework, data alignment schemes are proposed in the representation sub-space, including healthy state alignment, degradation direction alignment, degradation level regularization and degradation fusion. In this way, the life-cycle data of different machine entities across domains can follow the same degradation trace, thus achieving prognostic knowledge transfer. Extensive experiments on the aero-engine dataset validate the effectiveness of the proposed method, which offers a promising solution for industrial prognostics.

Automated summary

This paper proposes a transfer learning method for remaining useful life predictions using deep representation regularization. By aligning the life-cycle data of different machine entities across different operating conditions, prognostic knowledge transfer is achieved.