A Weighted Deep Domain Adaptation Method for Industrial Fault Prognostics According to Prior Distribution of Complex Working Conditions

In modern industrial engineered systems, variant working conditions disturb the distributions of machines' operational data, which results in different feature distributions (DFD) problems for fault prognostics. Domain adaptation (DA) have been proved good at dealing DFD problems, and several deep DA-based methods have been also proposed in fault prognostics filed. However, existing methods refer to the DA tasks from one working condition to another, without considerations of transferring between datasets under complex working conditions. The prior distribution of working conditions will influence the distributions of machines' operational data, and few studies take prior distribution of working conditions into consideration of DA for fault prognostics. Thus, in this paper, a working-condition-based deep domain adaptation network (Deep wcDAN) is proposed to overcome the DFD problems caused by variant complex working conditions. In the proposed method, CNNs combines LSTMs with domain adaptive transfer technique to minimize the distribution discrepancy between training and testing datasets. Furthermore, a working-condition-based MMD (wcMMD) is proposed to optimize the DA process based on the prior distribution of each working condition. The performance of proposed model is evaluated and the negative transfer effects have been analyzed based on C-MAPSS datasets. The results show that the proposed method performs better than baseline methods on predicting remaining useful life (RUL) with DFD problems.