AC induction machines (IM) are the most popular motors in many manufacturing processes and industrial applications. They are widely applied in petrochemical, military, aerospace and nuclear plants and mining industry. Broken rotor bar (BRB) is one of the most frequent AC IM faults. Accurate and timely diagnosis of BRB can help to lower maintenance costs and prevent unscheduled downtimes. BRB faults will cause rotor resistance change and then affect stator supply current directly. Therefore, it is an effective way to detect BRB fault by analysing instantaneous electrical signature (IES). However, most of the analysis results achieved by the common methods are time consuming and cannot process geometric information for BRB fault diagnosis with high accuracy. Mathematical morphology (MM) has attracted considerable attention due to its advantages of extracting the geometric structure of the modulation feature with less computation. In this paper, a novel fault detector is developed for BRB fault detection using MM based motor IES analysis. A novel morphological gradient is improved to analyse the motor current signal for fault related modulation components enhancement. And then the max value in the low frequency range of the MM filtered signal is extracted as a fault detector for BRB diagnosis. The performance of the proposed method is evaluated by experimental study. The test motors are setup with different broken bar level and operating under different loads. The analysis results prove the efficiency and performance of the proposed method on the BRB faults diagnosis for IMs.
|25th IEEE International Conference on Automation and Computing
|5/09/19 → 7/09/19