流体机械2025，Vol.53Issue(3)：110-118,9.DOI:10.3969/j.issn.1005-0329.2025.03.014
可靠性强化试验技术在电动闸阀故障识别中的应用
Application of reliability enhancement test technology in fault identification of electric gate valve
摘要
Abstract
To investigate the reliability of electric gate valves and challenges in minor fault identification,reliability enhancement testing(RET)technology was applied to simulate valve faults under non-destructive conditions.Response data from two typical failure modes were collected and analyzed using three machine learning methods(decision tree,random forest,gradient boosting)combined with two optimization approaches(grid search,actor-critic reinforcement learning).Results indicate that RET effectively simulates valve sticking and jamming faults without physical damage,achieving cost-efficient fault replication.The integration of current and vibration features significantly enhances fault recognition accuracy:normal data identification accuracy reaches 98%with random forest and gradient boosting,while fault data identification peaks at 82%using random forest and decision tree.Multi-feature fusion improves model performance by leveraging data characteristics.Reinforcement learning boosts random forest accuracy by 21%(vibration features),15%(vibration-current features),and 6%(current features),but degrades gradient boosting performance.Decision Tree excels in processing multi-feature data with superior fault recognition,while random forest maintains robust performance across single and combined features.Gradient boosting exhibits instability requiring feature-specific optimization.For industrial applications,random forest and decision tree with multi-feature fusion are recommended to enhance classification performance.This study provides technical references for electric gate valve fault diagnosis.关键词
电动阀门/可靠性强化/故障模拟/故障识别Key words
electric valve/reliability enhancement/fault simulation/fault identification分类
能源科技引用本文复制引用
张林,汤文斌,刘杰,闫晓,湛力,李明刚,周吴..可靠性强化试验技术在电动闸阀故障识别中的应用[J].流体机械,2025,53(3):110-118,9.基金项目
国家自然科学基金项目(52475120) （52475120）
