基于超程时间测量的电磁继电器故障检测

针对现有电磁继电器隐性故障检测率较低的问题，提出一种基于超程时间测量的电磁继电器故障检测方法。首先，通过离线优化获取最优模糊阈值，然后应用线性回归方法，对超程时间监测数据进行在线预测，并利用拟合误差将预测值扩展为预测区间；其次，根据最优模糊阈值，将监测数据和预测区间分别转换为当前和预测故障证据，利用区间证据推理迭代融合积累、当前、预测3种故障证据，获取更新后的积累故障证据；最后，按照故障判据，由积累故障证据得到继电器故障检测结果。所提方法充分考虑了继电器隐性故障的渐变性特征以及超程时间测量过程所包含的不确定性，使故障证据更贴近于继电器实际工况。实验结果表明，与现有方法相比，所提方法在证据准确性和收敛速度上具有显著优势，能够有效提升电磁继电器隐性故障的检测精度。

Fault detection of electromagnetic relay based on super-path time measurement

In order to improve the hidden fault detection rate of electromagnetic relay, a fault detection method based on super-path time measurement for electromagnetic relay is proposed in this paper. Firstly, the fuzzy threshold is optimized offline. Secondly, the super-path time data is predicted online using sliding window linear regression, and extended to predicted interval based on fitting error. Then, the relay super-path time measured data and predicted interval are converted into current fault evidence and predictive fault evidence using fuzzy threshold, and cumulative fault evidence is updated by iterative fusing cumulative, current and predictive fault evidence using interval evidence reasoning. Finally, the result of fault detection can be obtained from cumulative fault evidence according to fault criteria. The proposed method considers the slow-changing features of hidden relay fault and uncertainty in super-path time measurement process, so that the fault evidence is closer to the actual working condition of relay. The experimental results show that the proposed method has significant advantages in evidence accuracy and convergence speed, and can effectively improve the detection accuracy of electromagnetic relay hidden fault.