仇国庆,顾逸霏.基于区间证据推理的继电器接触系统故障检测[J].电子测量与仪器学报,2022,36(6):126-133
基于区间证据推理的继电器接触系统故障检测
Fault detection of relay contact system based on interval evidence reasoning
  
DOI:
中文关键词:  电磁继电器  接触电阻  故障检测  区间证据推理  区间阈值
英文关键词:electromagnetic relay  contact resistance  fault detection  interval evidential reasoning  interval threshold
基金项目:国家重点研发计划(2018YFB1702200)项目资助
作者单位
仇国庆 1.重庆邮电大学自动化学院 
顾逸霏 1.重庆邮电大学自动化学院 
AuthorInstitution
Qiu Guoqing 1.College of Automation, Chongqing University of Posts and Telecommunications 
Gu Yifei 1.College of Automation, Chongqing University of Posts and Telecommunications 
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中文摘要:
      针对现有接触电阻阈值检测算法过于依赖先验知识导致漏检率较高的缺陷,提出一种基于区间证据推理的继电器接触 系统故障检测算法。 首先,将先验阈值和接触电阻测试数据转换为区间信度结构;然后,根据修正权重对先验阈值和测试数据 进行区间证据推理融合;最后,计算出融合后的检测阈值。 该方法充分考虑了接触电阻测量模型所包含的不确定性以及区间证 据推理融合过程中的权重一致性,避免了现有算法自适应能力不足的缺陷。 在继电器加速寿命试验平台上进行验证,实验结果 表明,与经验阈值算法和常规区间证据推理方法相比,所提算法具有较好的自适应能力,可以有效提升电磁继电器接触系统的 故障检测精度。 考虑数据不平衡问题,对原始数据进行重采样后,所提算法能进一步提高故障检测效果。
英文摘要:
      To overcome the shortcoming of existing contact resistance threshold detection algorithms that rely too much on prior knowledge and result in high false negative rate, a relay contact system fault detection algorithm based on interval evidential reasoning (IER) was presented. First, the prior threshold and contact resistance test data are converted into interval belief structures. Then, the prior threshold and test data are fused by IER based on the modified weight. Finally, the fused detection threshold is calculated. This proposed method considers the uncertainty contained in the contact resistance measurement model and the consistency of weights in the fusion process of IER, avoiding the shortage of adaptive ability of existing algorithms. Experimental verification was carried out and the results show that compared with the empirical threshold algorithm and the conventional IER method, the proposed algorithm has better adaptive ability and can effectively improve the fault detection accuracy of electromagnetic relay contact system. Considering the data imbalance problem, the proposed algorithm can further improve the performance of fault detection after resampling the original data.
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