Abstract
This paper aims to classify local demagnetisation and inter-turn short-circuit (ITSC) on position sensorless permanent magnet synchronous motors (PMSM) in transient states based on external stray flux and learning classifier. Within the framework, four supervised machine learning tools were tested: ensemble decision tree (EDT), k-nearest neighbours (KNN), support vector machine (SVM), and feedforward neural network (FNN). All algorithms are trained on datasets from one operational profile but tested on other different operation profiles. Their input features or spectrograms are computed from resampled time-series data based on the estimated position of the rotor from one stray flux sensor through an optimisation problem. This eliminates the need for the position sensors, allowing for the fault classification of sensorless PMSM drives using only two external stray flux sensors alone. Both SVM and FNN algorithms could identify a single fault of the magnet defect with an accuracy higher than 95% in transient states. For mixed faults, the FNN-based algorithm could identify ITSC in parallel-strands stator winding and local partial demagnetisation with an accuracy of 87.1%.
Highlights
Fault diagnosis in permanent magnet synchronous motors (PMSM) has always received a lot of attention, aiming at increasing the safety level in dynamic and critical applications or preventing large economical losses due to unexpected downtime [1]
A fault classifier scheme using stray flux alone would be easier to implement in existing sensorless PMSM drives, since an external flux sensor can be placed in proximity of the PMSM
This paper presents a scheme of fault classification for single and mixed faults of a sensorless PMSM drive in dynamic operations using two external stray flux sensors alone
Summary
Fault diagnosis in permanent magnet synchronous motors (PMSM) has always received a lot of attention, aiming at increasing the safety level in dynamic and critical applications or preventing large economical losses due to unexpected downtime [1]. PMSMs in wind turbines and electric vehicles are intensively exposed to dust, mechanical, chemical, and thermal stresses in harsh environments and thermal cycling due to the dynamic operations. This causes an inter-turn short-circuit (ITSC) in the stator windings and demagnetisation on rotor permanent magnets (PM) of PMSMs, resulting in reduced efficiency and increased cogging torque [2]. The complexity of machine structure, variable speeds, and loads render fault diagnosis challenges due to sensor requirements and nonstationary signals, attracting significant research that has tackled ITSC and demagnetisation in industry and academia in recent years, which is briefly discussed hereafter
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
