A Stator Fault Diagnosis Method Based on the Offline Motor Parameter Measurement for PMSM

The permanent magnet synchronous motor (PMSM) is used widely in electric vehicle application due to its high-power density and efficiency. Stator fault is a frequently fault in the motor as it usually works in a harsh environment. Therefore, a stator fault diagnosis method based on the offline motor parameter measurement is proposed to detect and evaluate the stator fault in this paper. Firstly, the line-to-line resistance and inductance of a healthy motor are analyzed when a DC voltage and a high-frequency voltage are excited to the motor respectively, where the DC and AC equivalent circuits at a standstill are introduced. Then, to analyze the resistance and inductance of the stator fault, an extra branch is added to the fault part to obtain the fault equivalent circuits. Accordingly, the stator fault resistance and inductance are derived, and then the resistance and inductance differences between healthy and fault motors are analyzed to provide the basis for the stator fault detection. Furthermore, the fault indicators are defined based on the resistance and inductance differences when a motor has a stator fault. Hence the stator fault severity and location can be evaluated by using these fault indicators. Finally, the experimental results from a 400 W permanent magnet synchronous motor are demonstrated to validate the proposed method.

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Fault Diagnosis Method of Permanent Magnet Synchronous Motor for Electrical Vehicle

Journal of Magnetics ◽

10.4283/jmag.2016.21.3.413 ◽

2016 ◽

Vol 21 (3) ◽

pp. 413-420

Author(s):

Jin-Hyung Yoo ◽

Tae-Uk Jung

Keyword(s):

Fault Diagnosis ◽

Permanent Magnet ◽

Permanent Magnet Synchronous Motor ◽

Synchronous Motor ◽

Electrical Vehicle ◽

Diagnosis Method

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Research on modeling and fault diagnosis of inter-turn short fault of permanent magnet synchronous motor

Journal of Computational Methods in Sciences and Engineering ◽

10.3233/jcm-194127 ◽

2020 ◽

Vol 20 (3) ◽

pp. 959-973 ◽

Cited By ~ 1

Author(s):

Hongqian Hu ◽

Weifeng Shi ◽

Benjamin Venditti

Keyword(s):

Fault Diagnosis ◽

Permanent Magnet ◽

Short Circuit ◽

Permanent Magnet Synchronous Motor ◽

Torque Ripple ◽

Synchronous Motor ◽

Analysis Model ◽

Diagnosis Method ◽

On Line ◽

Circuit Resistance

The stator winding inter-turn short fault of permanent magnet synchronous motor (PMSM) is a common motor fault with high probability. If this fault is not detected and handled in time, small initial fault will rapidly develop into other faults such as grounding short, inter-phase short, winding short et al., and even the motor damage. In order to diagnose the inter-turn short fault of PMSM, this paper firstly builds the model of the inter-turn short fault of PMSM, and then proposes a fast and simple fault diagnosis method for extracting the features of the inter-turn short fault under noise. For the first time, by introducing the full-short-circuit resistance Rf that relates with the fault severity factor δ and by estimating the inductance of inter-turn short fault motor on-line according to the three principles of the inductance calculation, which is used in the inter-turn short fault of transformer winding, the inter-turn short analysis model of PMSM is deduced. The model can be used to simulate the performance not only the health of PMSM but also the fault of inter-turn short fault of PMSM in one phase winding. This paper analyses the transient and steady state performance of 3-phase unbalanced current, speed and electromagnetic torque ripple and the relationship between full-short-circuit resistance and short current under fault condition. Finally, a fast and simple on-line fault diagnosis method of PMSM with inter-turn short fault is proposed based on phase sensitive detection (PSD) algorithm. The method uses the fundamental and third harmonic features of inter-turn short fault current. The simulation results show that the model can be well suited for the simulation of PMSM with inter-turn short fault, and the method can effectively extract the features and diagnose the fault.

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