Demagnetization Fault Diagnosis Research Articles

Torque-Ripple-Based Interior Permanent-Magnet Synchronous Machine Rotor Demagnetization Fault Detection and Current Regulation

To develop a reliable permanent-magnet synchronous machine (PMSM) controller for electric vehicle application, detection of permanent-magnet (PM) demagnetization conditions is of significance. This paper explores the use of torque ripple for online PM demagnetization fault diagnosis using continuous wavelet transforms (CWT) and grey system theory (GST). First, a torque-ripple-based rotor flux linkage detection model considering electromagnetic noises is proposed, which employs CWT filtering, wavelet ridge spectrum, and torque ripple energy extraction. This model is able to reveal the torque variation and eliminate the effect of electromagnetic interferences. Second, GST is employed to facilitate the detection of demagnetization ratios and torque ripple energy pulsations caused by demagnetization. Third, a current regulation strategy is proposed to minimize the torque ripples induced by PM demagnetization, which contributes to making the approach feasible to interior PMSM (IPMSM). Furthermore, the proposed real-time irreversible demagnetization detection approach can identify the demagnetization fault under different operating conditions. The proposed approach and current regulation strategy are experimentally verified on a down-scaled laboratory IPMSM.

Demagnetization Modeling and Fault Diagnosing Techniques in Permanent Magnet Machines Under Stationary and Nonstationary Conditions: An Overview

Permanent magnet (PM) machines are widely used in industrial processes due to their merits such as high power density and torque and low losses (high efficiency). These machines operate frequently under harsh conditions; therefore, they expose different types of faults. These faults can be diagnosed in initial stages using different techniques and prevent the faults' progress to catastrophic stages. One of the most important PM machine faults in terms of occurrence rate is demagnetization fault, whose causes can be heat, electrical, environmental or combinations. This may lead to efficiency drop, poor performance, and low reliability of the system. This paper attempts to review and summarize the demagnetization fault diagnosis methods in PM machines. First the fault generating factors and their impacts on the performance of the motor are discussed. Then, the recently developed techniques for demagnetization fault under stationary and nonstationary conditions in two separate sections are addressed. Presented methods are compared; their weaknesses and strengths are noted. Finally, suggestions for further research are proposed.

Demagnetization Fault Diagnosis of a PMSM Based on Structure Analysis of Motor Inductance

A demagnetization fault in a permanent-magnet synchronous machine (PMSM) degrades performance and energy efficiency. The demagnetization fault causes flux-density difference and changes the PMSM inductances. Such varying parameters make it difficult to detect and diagnose the exact severity of a demagnetization fault. In this paper, we suggest a use of structural analysis of the PMSMs to diagnose the demagnetization fault. We can calculate varying inductances and consider the magnetic saturation effect with varying flux linkages of the machine. Combination of the proposed technique with the least-square (LS) method shows better results than the conventional methods in the estimation of flux linkages in the PMSMs with the demagnetization fault. Experimental results demonstrate that the proposed method works well both in the tracking of varying inductances and the diagnosis of the demagnetization fault.

Demagnetization Fault Diagnosis in Surface Mounted Permanent Magnet Synchronous Motors

In this paper, a novel method is proposed to detect demagnetization fault in surface mounted permanent magnet synchronous motors (SMPMSMs). Hence, a theoretical analysis is presented for feature extraction and pattern recognition via torque analysis. In this analysis, impacts of the saturation profile, stator slots and the fault severity are taken into account. So, using torque spectra, an efficient index is introduced for demagnetization fault diagnosis in SMPMSMs. Based on the extracted features and the recognized pattern, time delay embedding (TDE) approach as a competent data mining technique is utilized to process the developed torque. Then, a criterion function is proposed to detect demagnetization fault. Furthermore, this criterion is used to estimate fault severity and determine demagnetization percentage. The two-dimensional (2-D) time stepping finite element method (TSFEM) is employed to model the SMPMSM under different demagnetization levels. The 3-D TSFEM is used to prove the theoretical and 2-D TSFEM results.