Health level verification for electric control valve based on BiLSTM with multihead attention mechanism

The failure of electric valves represents a significant safety hazard in industrial systems. Traditional manual detection and regular replacement strategies are insufficient to address this issue. This study proposes an online verification method combining convolutional neural networks (CNN), two-way long short-term memory networks (BiLSTM) and multi-head attention mechanism (MHA), which is capable of identifying weak fault characteristics such as plugging and twitching caused by regulator wear. Transfer learning addresses the issue of data scarcity and enhances the model's adaptability in the target domain. The CNN-BiLSTM-MHA-TL model exhibited high prediction accuracy, with MAE, RMSE, and MAPE values of 0.4451, 0.5722, and 0.0132, respectively. This online verification method offers notable advantages and promising prospects for application in the field of valve health monitoring.