基于多元统计的电机故障诊断新方法

A Novel Method for the Fault Diagnosis of Asynchronous Motor Based on Multivariate Statistical Analysis

提出了用多元统计过程控制方法(MSPC)对异步电动机进行故障诊断的新方法.利用多个传感器测量的异步电机多维信号参量,构建电机在正常工作和发生故障时的Q统计和T2统计,以实现电机的状态检测;利用Q统计和T2统计值构建电机的状态特征向量,通过比较度量当前电机的特征向量H与电机发生故障时的特征向量HF的几何距离来实现电机故障的定位与分离.实验证明,该方法可以有效地实现故障的诊断与分离.

A new multivariate statistical process control （MSPC） method is presented for the fault diagnosis of asynchronous machine. The multi-dimensional signals measured by sensors with a machine working under normal condition or fault condition, can be used to build Q statistic and T^2 statistic respectively to detect the state of the machine. Meanwhile, the feature vector is constructed by the Q value and the T^2 value, the geometrical distance between the normal feature vector H and the feature vector H^F when the machine goes wrong is used to realize the fault location. The results show that the method is feasible.