A Fault Diagnosis Method under Data Imbalance Based on Generative Adversarial Network and Long Short-Term Memory Algorithms for Aircraft Hydraulic System

Abstract

Safe and stable operation of the aircraft hydraulic system is of great significance to the flight safety of an aircraft. Any fault may be a threat to flight safety and may lead to enormous economic losses and even human casualties. Hence, the normal status of the aircraft hydraulic system is large, but very few data samples relate to the fault status. This causes a data imbalance in the fault diagnosis of the aircraft hydraulic system, which directly affects the accuracy of aircraft fault diagnosis. To solve the data imbalance problem in the fault diagnosis of the aircraft hydraulic system, this paper proposes an improved GAN-LSTM algorithm by using the improved GAN method, which can stably and accurately generate high-quality simulated fault samples using a small number of fault data. First, the model of the aircraft hydraulic system was built using AMESim software, and the imbalanced fault data and normal status data were acquired. Then, the imbalanced data were used to train the GAN model until the system reached a Nash equilibrium. By comparing the time domain and frequency signal, it was found that the quality of the generated sample was highly similar to the real sample. Moreover, LSTM (long short-term memory) and some other data-driven intelligent fault diagnosis methods were used as classifiers. The accuracy of these fault diagnosis methods increased steadily when the number of fault samples was gradually increased until it reached a balance with the normal sample. Meanwhile, three different sample generation methods were compared and analyzed to find the method with the best data generation ability. Finally, the anti-noise performance of the LSTM-GAN method was analyzed; this model has superior noise immunity.

Inverter Fault Diagnosis for a Three-Phase Permanent-Magnet Synchronous Motor Drive System Based on SDAE-GAN-LSTM

“…Notably, this approach exhibited excellent performance in diagnosing faults in three-phase PMSM drive system inverters, leading to improved classification and recognition accuracy. Comparative tests conducted in references [34,35], involving various algorithms, revealed that the proposed SDAE-GAN-LSTM method consistently produced experimental results either comparable to or superior to existing methods. This substantiates the superiority and effectiveness of the SDAE-GAN-LSTM approach in fault diagnosis.…”

“…This approach significantly improved fault diagnosis accuracy, serving as a foundation for evaluating the effectiveness of the SDAE-GAN-LSTM method across diverse applications. In the context of time series data, reference [35] introduced an enhanced model structure tailored toward tackling sample imbalance problems. This method, employing LSTM as the classifier, achieved progressively higher accuracy as the number of fault samples increased.…”

“…SAE in [33] 93.46% Gear pitting GAN-SAE in [34] 98.89% Electrically driven feed pump GAN-LSTM in [35] 69.67% Aircraft SDAE-GAN-LSTM 98.92% PMSM…”