Unilateral alignment transfer neural network for fault diagnosis of aircraft engine

Abstract

Data-driven fault diagnosis for aircraft engine has recently been widely studied, most of which utilized traditional machine learning tools. However, these approaches often assumed that the data used for training and testing come from the identical distribution, which is unpractical in practice, because even for the same type of engine, there may be some discrepancies in the data collected between different engine individuals. This idealized assumption may confine these promising data-driven technologies to well-design experimental environments instead of applying them to practical applications. Meanwhile, the research on data-driven engine fault diagnosis methods based on transfer learning is relatively limited. To overcome this issue, this paper proposes a methodology using unilateral alignment strategy to address the issue of cross domain engine gas path fault diagnosis, where the training data extracted from source domain and the testing data extracted from target domain are assumed to come from different distributions. More importantly, the proposed methodology is capable of maintaining the inter-class relationship of source domain, instead of aligning the discrepancies for source and target domain by mapping both of their feature spaces into an unknown intermediate space, because the label space between two domains may be different, and the label set of target domain might be only a subset of that of source domain. Meanwhile, both the marginal and conditional distribution discrepancies are considered. Finally, the proposed approaches are evaluated by extensive experiments on engine cross domain fault diagnosis, including hybrid transfer experiments, complete transfer experiments and missing class experiments, and the overall results demonstrate the feasibility of the proposed methodology.