Operating performance assessment and fault diagnosis of electro-hydraulic servo valves using Trans-SN-CGAN with Gaussian and non-Gaussian information fusion

The electro-hydraulic servo valve (EHSV) is a critical component in hydraulic systems, particularly in high-precision applications such as aircraft braking systems. Traditional operational performance assessment (OPA) and fault diagnosis (FD) methods often struggle to capture the complex spatiotemporal relationships and non-Gaussian characteristics inherent in EHSV data, especially when dealing with imbalanced datasets. To address these challenges, this paper presents a Transformer-based spectral normalization conditional generative adversarial network (Trans-SN-CGAN) for monitoring the EHSV. Trans-SN-CGAN integrates both Gaussian and non-Gaussian information through kernel principal component analysis (KPCA) and kernel independent component analysis (KICA). It further transforms time-series data into 2D images using Gramian angular fields (GAF), preserving essential spatiotemporal features. Additionally, spectral normalization (SN) is incorporated into the conditional generative adversarial network (CGAN) to enhance training stability and generate high-quality synthetic data samples. The Transformer architecture is utilized to enhance the model’s capacity to extract discriminative feature representations by capturing long-range dependencies in sequential data. Experimental results demonstrate exceptional performance, with OPA accuracy of 97.6 % and FD accuracy of 97.2 %, showcasing the framework’s robustness and reliability for improving EHSV efficiency and dependability.