Fault injection and diagnosis of gate hoist based on mechanical-hydraulic joint simulation model

Here, to evaluate and diagnose operating signals of a radial gate system with fault features, a mechanical- hydraulic joint simulation model of radial gate opening and closing mechanism was established based on detection data of a certain radial gate experimental platform to perform model similarity analysis and correction. By using a gate state control strategy based on real-time gate opening feedback signals, gate working state was controlled to improve simulation efficiency and stability, and obtain more realistic state features. Hydraulic system and mechanical system faults were injected into the model to analyze effects of faults on gate operation signals. In order to fully reflect fault feature information, multiple signals were selected for fusion and adjusting weight allocation. Based on the weighted multi-channel data fusion method, the large fluctuation problem of fault recognition accuracy was solved. In order to improve the generalization ability of the network model, residual structures were added for optimization. The convolutional neural network based on transfer learning was used to solve the problem of low fault recognition accuracy. The results showed that the constructed model can demonstrate feature dynamic changes of important signals of pressure, flow rate and vibration, etc. in operation process of radial gate; the correct rate of the neural network based on residual structure optimization using improved weighted multi-channel data fusion reaches 97. 17% . © 2024 Chinese Vibration Engineering Society. All rights reserved.