Research on fault diagnosis of gas turbine rotor based on adversarial discriminative domain adaption transfer learning

Abstract

In the process of gas turbine rotor fault diagnosis based on data-driven, transfer learning is an effective method to solve the lack of gas turbines labeled data, which will result in domain shifts due to the data distribution difference between source domain data and target domain data under variable working condition. A gas turbine fault diagnosis method based on Adversarial Discriminative Domain Adaptation Transfer Learning Network (ADDATLN) is put forward to reduce domain offsets and improve the gas turbine fault diagnosis accuracy. In the proposed method, pre-trained deep Convolutional Neural Networks (CNN) models in the source domain is transferred to target domain data, then deep adversarial training between the source domain and target domain is adopted to adaptively optimize the model parameters of the target domain network, with the purpose of reducing domain offsets and improving gas turbine fault classification accuracy. Field test experiment results on gas turbine rotor fault diagnosis under different working conditions show that the average accuracy of the proposed method reaches 96.45%, and the average accuracy of fault diagnosis on different gas turbines with the same type achieved 95.13%. The field test results confirm that the method effectively reduces the domain differences caused by varying working conditions and different gas turbines, and improves the accuracy of gas turbine rotor fault diagnosis under variable working condition and for different gas turbines with small samples.