采用EWT和OCSVM的高压断路器机械故障诊断

根据断路器故障诊断对可靠性要求较高的实际工程需求,提出了一种采用经验小波变换(EWT)和单类支持向量机(OCSVM)的高压断路器机械故障诊断新方法。首先,通过EWT准确分离断路器振动信号中具有不同物理意义的固有模态函数(IMF);之后,通过Hilbert谱分析,获得时-频矩阵并计算其时-频熵,构成用于分类的特征向量;然后,仅使用易于获取的正常状态振动信号训练经粒子群算法(PSO)常数参数寻优的OCSVM,并通过OCSVM来准确判断断路器是否发生机械故障,提高故障诊断可靠性;如OCSVM判断发生机械故障,则进一步通过支持向量机(SVM)判断具体故障类型。在SF6高压断路器上进行实验证明,新方法能够更加准确地区分故障与正常样本,满足高压断路器故障诊断的高可靠性要求。

Mechanical fault diagnosis of high voltage circuit breakers utilizing empirical wavelet transform and one-class support vector machine

According to the actual engineering requirement of high reliability in the fault diagnosis of circuit breakers, this paper proposes a new mechanical fault diagnosis method of high voltage circuit breakers utilizing empirical wavelet transform (EWT) and one class support vector machine (OCSVM). First, the method uses EWT to separate the intrinsic mode functions (IMF) containing different physical significances in the vibration signals of circuit breakers accurately. Then, Hilbert spectrum analysis is used to obtain the time frequency matrix and compute the time frequency entropy, which constitute the feature vector used for classification. Afterwards, only the easily obtained normal vibration signals are used to train the OCSVM with the constant parameters optimized by particle swarm optimization (PSO); and the OCSVM is used to accurately judge if the circuit breaker mechanical fault occurs and improve the fault diagnosis reliability,. If OCSVM judges that a mechanical fault occurs, the support vector machine (SVM) is used to further recognize the specific fault type. Experiment on a SF6 high voltage circuit breaker was conducted, the result proves that the proposed new method can differentiate the fault samples from normal samples more accurately. Thus, the new method can satisfy the requirement of high reliability for the mechanical fault diagnosis of high voltage circuit breakers.