Gas Path Fault Diagnosis Method Under Small Samples With Inter-Class Imbalance

Abstract

Abstract Current data-driven methods for gas path fault diagnosis in aero-engines often require extensive fault sample sets, which are challenging and expensive to obtain in practice. Additionally, even with limited samples, these methods face issues of inter-class imbalance, including imbalances in normal and fault data quantities, imbalances in the quantities of different fault classes, and imbalances in fault severity. Additionally, current methods for simulating gas path faults solely focus on the degradation of gas path component efficiency, neglecting the non-linear alterations in component characteristics caused by faults and the interconnected effects between components. Therefore, we propose to use a transfer learning-based variational autoencoder (TL-VAE) approach to generate fault samples and optimize the accuracy of gas path fault diagnosis. First, we train the VAE using normal engine operation samples. Then, by incorporating transfer learning, we retrain the VAE using a small number of fault samples by fine-tuning certain weights of VAE. This allows us to combine the operational state features from the source domain with the fault features from the target domain, fitting the distribution of the measured parameters of the faulty engine. This enables the TL-VAE to function as a generator of fault samples. Subsequent fault diagnosis strategies rely on mature classification methods and the generated samples, including Softmax and SVM classifiers. We validate the effectiveness and superiority of the proposed method through simulation. The experimental results demonstrate that the proposed method significantly improves fault diagnosis results with limited samples. Especially within the coverage of the generated samples, fault diagnosis accuracy (FDA) of the Softmax and SVM classifiers significantly improved from 73.3% and 66.5%, respectively, to a perfect 100% after employing the proposed approach. The FDA of Softmax and SVM classifiers with the proposed method is improved from 77.9% and 80.9% to 100% compared to other typical data generation methods.