Detection of Partial Demagnetization Fault in PMSMs Operating Under Nonstationary Conditions

Wang, Chao; Delgado-Prieto, Miguel; Romeral, L.; Chen, Zhe; Blaabjerg, Frede; Liu, Xiao

doi:10.1109/tmag.2015.2511003

Cited by 61 publications

(38 citation statements)

References 15 publications

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“…In recent years, many scholars have performed many experiments on PMSM demagnetization fault. In [6,7], advanced signal processing technology is used to extract stator current harmonics from demagnetization fault, and the local demagnetization fault diagnosis of PMSM permanent magnet is realized. In [8], the demagnetization fault of PMSM is diagnosed by the harmonic characteristics of the back EMF.…”

Section: Introductionmentioning

confidence: 99%

Mode Recognition and Fault Positioning of Permanent Magnet Demagnetization for PMSM

Gao

Nie

et al. 2019

Energies

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This paper proposes a demagnetization fault detection, mode recognition, magnetic pole positioning, and degree evaluation method for permanent magnet synchronous motors. First, the analytical model of the single-coil no-load back electromotive force (EMF) of demagnetization fault for Permanent magnet synchronous motor (PMSM) arbitrary magnetic poles is established. In the analytical model, the single-coil no-load back EMF residual of the health state and the single magnetic pole sequential demagnetization fault are calculated and normalized. Model results are used as the fault sample database. Second, the energy interval database of the single-coil no-load back EMF residual with different numbers of magnetic pole demagnetization is established. Demagnetization fault detection and degree evaluation are performed by the real-time acquired amplitudes of the single-coil no-load back EMF residual. The number of demagnetization poles is determined by comparing the energy of the single-coil no-load back EMF residual with the energy interval database. Demagnetization mode recognition and magnetic pole positioning are realized by analyzing the correlation coefficients between normalized the single-coil no-load back EMF residual and the fault sample database. Finally, results of analysis of the finite element simulation validate the feasibility and effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Mode Recognition and Fault Positioning of Permanent Magnet Demagnetization for PMSM

Gao

Nie

et al. 2019

Energies

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“…In the literature, some experimental analysis and detection of the demagnetization conditions have been done by breaking or cutting out parts of the original magnets of the machine as shown in (Le Roux et al 2007) with a surface permanent magnet (SPM) machine. Also in (Yang et al 2014), the authors analyzed the partial demagnetization and inter-turn short circuit faults experimentally by intentionally breaking a piece of the SPM and analyzing the vibration signal (accelerometer) spectrum; in (Wang et al 2016), the authors analyzed the partial demagnetization of an SPM machine through advanced signal processing (Vold-Kalman tracking filter) of current signal measurements. For the IPM machine, more recent literature also shows the detection and analysis through current measurements in an experimental setup; in (Hong et al 2012), the authors analyzed the change of the inductance in the rotating reference frame due to demagnetization.…”

Section: Introductionmentioning

confidence: 99%

“…In (Yang 2013), they propose the Teager-Huang Transform (THT) with the energy operator decomposition (EOD) as a superior alternative to HHT. And lately, (Wang et al 2016) proposed a Vold-Kalman filter to track harmonics connected with demagnetization even through nonstationary conditions. Most of the previous authors criticize the classical methods, e.g., fast Fourier transform (FFT) because their dependency on stationary conditions, although all of them depend on the ab initio identification of the frequencies related to certain faults, e.g., for demagnetization the work done in (Le Roux et al 2003).…”

Section: Introductionmentioning

confidence: 99%

Analysis of Current Signals in a Partially Demagnetized Vector Controlled Interior Permanent Magnet Generator

Quintal-Palomo

2019

Power Electronics and Drives

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This manuscript analyzes the operation of an interior permanent magnet (IPM) machine working as a permanent magnet synchronous generator (PMSG). The partial demagnetization operation is analyzed. To obtain more accurate voltages and currents of the machine, finite element analysis (FEA) is used in co-simulation with the full converter and the converter’s control algorithm. Direct field oriented control (DFOC) shows robustness by maintaining the speed even with a 25% demagnetized PMSG. Also, an analysis of the rotating reference frame DQ signals is done to asses demagnetization.

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“…Currently, many literatures are concerned with fault diagnosis of motor [1][2][3][4][5][6][7]. An intelligent diagnosis based on an adaptive neuro-fuzzy inference system was presented to identify electrical faults of in-wheel motor [1].…”

Section: Introductionmentioning

confidence: 99%

“…A novel approach based on tracking characteristic orders of stator current using Vold-Kalman filter is proposed to detect the partial demagnetization fault in PMSMs running at nonstationary conditions. The amplitude of envelope of the fault characteristic orders is used as fault indictor [5]. The above methods mainly researched the diagnosis methods of electrical faults of a motor.…”

Section: Introductionmentioning

confidence: 99%

Sequential fault detection for sealed deep groove ball bearings of in-wheel motor in variable operating conditions

Xue¹,

Wang²,

Li³

et al. 2017

J. vibroeng.

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Sealed deep groove ball bearings (SDGBBs) are employed to perform the relevant duties of in-wheel motor. However, the unique construction and complex operating environment of in-wheel motor may aggravate the occurrence of SDGBB faults. Therefore, this study presents a new intelligent diagnosis method for detecting SDGBB faults of in-wheel motor. The method is constructed on the basis of optimal composition of symptom parameters (SPOC) and support vector machines (SVMs). SPOC, as the objects of a follow-on process, is proposed to obtain from symptom parameters (SPs) of multi-direction. Moreover, the optimal hyper-plane of two states is automatically obtained using soft margin SVM and SPOC, and then using multi-SVMs, the system of intelligent diagnosis is built to detect many faults and identify fault types. The experiment results confirmed that the proposed method can excellently perform fault detection and fault-type identification for the SDGBB of in-wheel motor in variable operating conditions.

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Detection of Partial Demagnetization Fault in PMSMs Operating Under Nonstationary Conditions

Cited by 61 publications

References 15 publications

Mode Recognition and Fault Positioning of Permanent Magnet Demagnetization for PMSM

Mode Recognition and Fault Positioning of Permanent Magnet Demagnetization for PMSM

Analysis of Current Signals in a Partially Demagnetized Vector Controlled Interior Permanent Magnet Generator

Sequential fault detection for sealed deep groove ball bearings of in-wheel motor in variable operating conditions

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