Small-sample Engine Fault Diagnosis Method Based on IACGAN and DNNs

Abstract

The reliability of engines, particularly aero engines, has become increasingly important in recent years. Accurate fault diagnosis can prevent accidents and minimize property damage. Deep neural network methods (DNNs) are commonly used for fault diagnosis, but their performance relies heavily on large amounts of high-quality training data. Unfortunately, obtaining high-quality engine fault data is challenging in practice. To address this problem, this paper proposes an improved auxiliary classifier generative adversarial network (IACGAN) that incorporates Wasserstein distance and a gradient penalty term. Meanwhile, a variable learning rate is also proposed to accelerate the model convergence. This approach effectively mitigates the problem of model gradient disappearance and expands one-dimensional time-series data. The proposed method was verified on a small aero-engine through a failure simulation test. The results show that the accuracy of DNN can be significantly raised by data enhancement of IACGAN, especially in the case of a limited number of samples. Therefore, this method shows promise as an auxiliary tool for DNN-based fault diagnosis.