Health assessment of marine gas turbine propulsion system under cross-working conditions based on transfer learning

Abstract

The marine gas turbine propulsion system generally works in a healthy state, and the samples collected by the monitoring system are characterized by more normal samples and fewer fault samples. Aiming at the problem of lack of fault samples faced by data-driven fault diagnosis methods, a cross-working condition fault diagnosis model is proposed by using transfer learning to reduce the dependence of data-driven methods on fault samples. The proposed method was experimentally validated by using a real-ship-validated dataset. Compared with traditional methods, the proposed method can achieve cross-working condition fault diagnosis with fewer fault samples.