摘要
分析断路器的机械振动信号的特性,针对采用单一性质故障特征难以实现整个故障状态空间上准确诊断的局限性,提出了一种基于改进的距离评估技术和多类支持向量机相结合的诊断高压断路器机械故障的方法,该方法由3部分构成:首先从高压断路器机械振动信号中提取时域统计特征、频域统计特征、经验模态分解能量熵及小波包能量特征信息;接着采用改进的距离评估技术从原始特征集合中选取最优特征,实现对原始特征空间的降维处理;最后选取的最优特征量作为"次序二叉树"策略方式的多类支持向量机的输入向量,实现对断路器3种机械故障模式的识别。实验结果表明,该方法诊断高压断路器机械故障能取得良好的效果。
Targeting the characteristics of mechanical vibration signals of high voltage circuit breaker, and there exists limitless for any signal fault feature to achieve the accurate diagnosis needs the whole diagnosis state area, a new method based on improved distance evaluation technique and multi-class support vector machine（MSVM） to diagnosis fault for high voltage circuit breaker is presented, and the method consists of three stages. Firstly, different features, including time-domain statistical characteristics,frequency-domain statistical characteristics, and empirical mode decomposition（EMD） energy entropies and wavelet packet transform（WPT） energy, are extracted to acquire more fault characteristic information.Second, an improved distance evaluation technique is proposed, and with it, the most superior features are selected from the original feature set. Finally, the most superior features are fed into MSVM with strategy of binary tree for estimating fault type. The experimentation shows that the method is effective to diagnose the faults of high voltage circuit breaker.
出处
《高压电器》
CAS
CSCD
北大核心
2015年第12期89-95,共7页
High Voltage Apparatus
关键词
高压断路器
能量熵
支持向量机
故障诊断
high voltage circuit breaker
energy entropy
support vector machine
fault diagnosis
