Fault Detection in Permanent Magnet Synchronous Motors using Machine Learning

Abstract: In the aviation industry, safety and robustness are the number one priorities, which is why they use well-tested systems such as hydraulic actuators. However, drawbacks such as high weight and maintenance have pushed the industry toward newer, electrical, actuators that are more efficient and lighter. Electrical actuators, on the other hand, have some reliability issues. In particular, short circuits in the stator windings of Permanent-Magnet SynchronousMotors (PMSMs), referred to as Inter-Turn Short Faults (ITSFs), are the dominating faults, and is the focus of this thesis. ITSFs are usually challenging to detect and often do not become noticeable until the fault has propagated, and the motor is on the verge of being destroyed. This thesis investigates the possibility of detecting ITSFs in a PMSM, at an early stage when only one turn is shorted. The method is limited to finding the faults using ML algorithms. Both an experiential PMSM and a simulated model of the experimental PMSM, with the ability to induce an ITSF, were used to collect the data. Several Machine Learning (ML) models were developed, and then trained and tested with the collected data. The results show that four of the tested ML models, being: Random Forest, Gaussian SVM, KNN, and the CNN, all achieve an accuracy exceeding 95%, and that the fault can be found at an early stage in a PMSM with three coils connected in parallel in each phase. The results also show that the ML models are able to identify the ITSF when the simulated data is downsampled to the same frequency as the experimental data. We conclude that the ML models, provided in this study, can be used to detect an ITSF in a simulated PMSM, at an early stage when only one turn is shorted, and that there is great potential for them to detect ITSFs in a physical motor as well.