Deep Learning–Based Fault Identification Testing Experiment for Bellows Valves - Jianwen Guo; YongHeng Cai; Zihan Chen; Zonghao Tan; Honghua Qiu; Ning He; Yu Liu; Shuai Wang (Science and Technology of Nuclear Installations, 2024)
This study focuses on the fault identification of the pneumatic bellows valve. This valve plays an essential role in regulating system pressure to ensure the smooth progress of the tritium removal process. To conduct research on the health status of the pneumatic bellows valve and to timely identify potential faults and anomalies, we developed a dedicated experimental platform and assessed the performance of various deep learning models, including recurrent neural networks (RNNs), single-layer long short–term memory networks (LSTMs), double-layer LSTM, multilayer LSTM, single-layer gated recurrent unit (GRU), and bidirectional GRU, in valve fault identification. The experimental outcomes reveal that the RNN and bidirectional GRU models exhibit superior performance in terms of accuracy and model fit, particularly in scenarios involving normal valve operations, valve leakage faults, and valve head contact faults. These findings offer new perspectives and methodologies for the detection and prevention of valve faults, contributing to the operational stability and safety of bellows valves.