Micro Transfer Learning Mechanism for Cross-Domain Equipment RUL Prediction

Transfer learning generally addresses to reduce the distribution distance between source-domain and target-domain. However, it is unreasonable to use a distribution to represent the life-cycle signals as they are always time-varying, and the improper assumption affects the efficacy of transfer remaining useful life (RUL) prediction. To fill this gap, this research proposes a micro transfer learning mechanism for multiple differentiated distributions, and a transfer RUL prediction model is constructed. First, a multi-cellular long short-term memory (MCLSTM) neural network is applied to obtain multiple differentiated distributions of the monitoring data at some point. Then the domain adversarial mechanism is used to achieve the knowledge transfer of multiple differentiated distributions at the cell level. Furthermore, an active screen mechanism is designed for weighting the domain discrimination losses of multiple differentiated distributions. Through the transfer RUL prediction experiments on aero-engines and actual wind turbine gearboxes, the superiority of this model over the advanced transfer prediction models is verified. Note to Practitioners—The work is motivated by the accuracy reduction problem caused by the time-varying characteristics of life-cycle data in the cross domain equipment RUL prediction scenario, where a fixed single distribution is difficult to cover the full life-cycle data. This article proposes a micro transfer learning mechanism containing multiple differentiated distributions, and a novel transfer RUL prediction model based on the mechanism is constructed for solving the problem caused by the time-varying characteristics of life-cycle data. There are four steps for implementing this method in practice: 1) collecting the full-life cycle signals of historical equipment; 2) modeling the degradation curves of equipment by MCLSTM; 3) solving the cross domain RUL prediction by narrowing the distributions of degradation curves by the micro transfer learning mechanism; and 4) making prognostics for new equipment. The novelty is that the proposed mechanism can self-adaptively align multiple differentiated subspaces of the source domain and the target domain, that is, it can adaptively extract the domain invariant features over time. As a result, the proposed method has two main advantages: 1) capable of characterizing the degradation processes of different equipment; and 2) superior prognostic results on cross domain RUL prediction.