Detection and Separation of Faults in Permanent Magnet Synchronous Machines using Hybrid Fault-Signatures

As digitalization in electric motors accelerates, online condition monitoring systems are becoming more popular, allowing unplanned downtime to be detected at its initial stage. Individual faults in motors are effectively diagnosed. However, due to identical signatures, fault separation and/or identification remain a challenge. This study presents the detection and separation of inter-turn short, demagnetization, static eccentricity, bearing, and the combination of these faults in permanent magnet synchronous machines. Hybrid fault signatures are used by monitoring the frequency spectrum of stator current, vibration, and induced voltage in the airgap. A planer-shaped airgap search coil (PASC) is employed to extract the induced voltage of each stator tooth. Faults-related anomalies in the induced-voltage, vibration, and the stator current caused are monitored. Any deviation in either signal is considered as generic fault indicator. Furthermore, specific fault features in all signals are used to classify these faults with improved accuracy. The PASC used in this study can also identify the location of the inter-turn short fault and the severity of demagnetization fault. The proposed method is verified using the finite element method simulation and experiments.