Mixed Fault Classification of Sensorless PMSM Drive in Dynamic Operations Based on External Stray Flux Sensors

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%.

Interturn Short Fault Diagnosis Using Magnitude and Phase of Currents in Permanent Magnet Synchronous Machines

“…The right of Figure 2 shows when the degree of ITSFs is G (µ, R f ). In the right of Figure 2, the phase indicator also means the angle between the vector 1 2 G (µ, R f )v a and vector i aH…”

“…Permanent magnet synchronous machines (PMSMs) are being increasingly used in diverse industries such as electronic appliances, electric vehicles, aviation, and military fields owing to their high power density, high efficiency, and low maintenance costs [1][2][3][4][5][6][7]. Faults in PMSMs can reduce use efficiency or even stop the overall system, leading to serious losses caused by accidents [8,9].…”

“…Figure 2 represents vector plots of the phase indicator for different two ITSFs under the same operating conditions. The left of Figure2means the angle between the vector1 2 G(µ, R f )v a and vector i aH +1 2 G(µ, R f )v a when the degree of ITSFs is G(µ, R f ). The right of Figure2shows when the degree of ITSFs is G (µ, R f ).…”

“…The right of Figure2shows when the degree of ITSFs is G (µ, R f ). In the right of Figure2, the phase indicator also means the angle between the vector1 2 G (µ, R f )v a and vector i aH + 1 2 G (µ, R f )v a when the degree of ITSFs is G (µ, R f ). Figure2represents the degree of ITSFs increases from G to G , the phase indicator decreases.…”

Demagnetization Fault Diagnosis of Permanent Magnet Synchronous Motors Based on Stator Current Signal Processing and Machine Learning Algorithms

“…The largest amount of research related to the development of PMSM fault detectors refer to the application of the NN with shallow structure-feedforward multiLayer perceptron (MLP). It is proposed for the PM demagnetization fault detection, among others, in [ 26 ]. In [ 27 ] the possibility of detecting this type of fault using a self-organizing Kohonen map trained with data obtained from the finite element method (FEM) based on the PMSM model is investigated.…”