矿用栓接电缆接头松动故障识别方法研究

利用自研的实验平台开展了不同松动程度、负载电流和负载类型条件下的电缆接头松动故障实验，研究了不同条件下栓接电缆接头的温度特性、接触电压和回路电流特性。提出1种基于小波能量熵和概率神经网络(PNN)的松动故障识别方法。采用小波变换对电流信号进行多分辨率分析，提取电流能量熵作为松动故障的典型特征，作为PNN松动故障识别模型的输入向量。利用newpnn函数创建PNN模型，采用循环寻优法对该模型的扩展参数S进行优化。分析了训练样本数量以及高频电磁噪声对模型识别准确率的影响。测试结果表明，该方法能够有效识别矿用栓接电缆接头的电连接松动故障。

Research on identification methods of looseness fault in coal-mine bolted cable joint

It’s particularly important to recognize timely the electrical connection looseness fault of coal-mine bolted cable joints.Lots of looseness fault experiments under different loosening states,currents and load conditions were carried out with self-developed experimental platform.The temperature characteristics,contact voltage and current characteristics of the loosening bolted cable joint under different conditions were studied.A new looseness fault identification method based on current energy entropy and Probabilistic Neural Network (PNN) was proposed in this study.The multi-resolution analysis of current signal was conducted by using wavelet transform,and the current energy entropy used as typical feature parameter of electrical connection looseness fault was extracted.Then the current energy entropy was put into the PNN fault identification model.The PNN fault identification model was established by using newpnn function with Matlab software and the spread parameter S was optimized by using Loop optimization method.The relationships between the identification accuracy and both the number of training samples and high frequency electromagnetic noise were also discussed.Lots of testing results show that the suggested method can identify the electrical connection looseness fault of coal-mine bolted cable joint effectively.