Fault diagnosis method of fire-proof oil system in thermal power plant based on real-time monitoring

INTRODUCTION: Fire-proof oil systems in thermal power plants are prone to pump wear, leakage, oil degradation, and valve jamming, threatening operational safety. Real-time fault diagnosis is required. OBJECTIVES: To develop a real-time and accurate fault diagnosis method for fire-proof oil systems. METHODS: A multi-sensor monitoring framework integrating pressure, flow, temperature, and oil-quality signals was established. A lightweight deep learning model based on multi-source feature fusion was constructed, with a confidence-driven decision mechanism to reduce the influence of low-quality samples. Experiments were conducted on an open hydraulic-system dataset under multiple operating conditions. RESULTS: The proposed method achieved accuracies of 0.96–0.93 and AUC values of 0.98–0.97. Average diagnostic time was 3.17–3.58 ms, with alarm delays of 49.36–54.82 ms. The model outperformed MBDNN and TDANet in accuracy, efficiency, and robustness under noise. CONCLUSION: The proposed framework enables reliable real-time fault diagnosis of fire-proof oil systems and supports intelligent operation and maintenance in thermal power plants.