Design and Implementation of a Multi-Channel Intelligent Fault Diagnosis Software for Steam Valves Based on MBD and Dual-Algorithm Fusion

Abstract

Main steam isolation valves face severe challenges during monitoring under high-temperature, high-pressure, and noisy environments. These challenges include weak fault features, data blocking in multi-channel acquisition, and high false alarm rates. To address these issues, this paper proposes a dedicated intelligent fault diagnosis software based on Model-Based Design (MBD) and the PyQt5 framework. The software innovatively employs a QThread pool architecture to achieve non-blocking synchronous acquisition across 26 channels, effectively resolving resource contention under high-throughput data transmission. For diagnosis, the system integrates dual lightweight algorithms (SVM and LSTM) to balance computing constraints with precise status determination and trend prediction, while introducing a spatial weighted algorithm based on time-domain deviation for accurate fault localization. Crucially, a linear regression dynamic threshold model corrected by temperature and pressure is established to mitigate false alarms under non-stationary operating conditions. Validation using full-power operation data from a nuclear power plant demonstrates that the software limits synchronous acquisition delay to within 50ms and achieves a fault determination accuracy of 94%. Furthermore, the conditioncorrected mechanism reduces the false alarm rate by over 98% (to 0.07%) and shortens the average fault investigation cycle by 90%. This system effectively overcomes the lag in traditional monitoring, marking a critical shift from “passive repair” to “proactive prediction” in steam valve maintenance.