Online Diagnosis and Severity Estimation of Partial and Uniform Irreversible Demagnetization Fault in Interior Permanent Magnet Synchronous Motor

Ullah, Zia; Lee, Seung Tae; Siddiqi, Mudassir Raza; Hur, Jin

doi:10.1109/ecce.2019.8912744

Cited by 25 publications

(8 citation statements)

References 8 publications

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“…The significant changes in stator phase currents, motor back‐EMF and radial magnetic flux aid in diagnosing the partial demagnetization faults in the machine. However, the advanced classification of PD can be achieved through harmonic/frequency analysis of currents (MCSA) and BEMF signatures [15]. The mathematical expression is derived through (12),

\begin{equation}{f_q} = \left({\frac{k}{P/2}}\right)\; {f_s} = z{f_r}, z\ = 1,2,3 \ldots ..\end{equation}

where f q is the frequency of z th component in the spectrum, f s is the supply (electrical) frequency and f r is the rotational frequency while P is the number of poles.…”

Section: Frequency and Field Analysis Of Bldc Motor Under Healthy And...mentioning

confidence: 99%

“…Some diagnostic techniques deploy voltagedriven based-MMF analysis [13], in addition to flux-based analysis [14], which are generally used as fault indicators. Excluding uniform/irreversible demagnetization faults, the other types of PM-related faults are categorized under PD faults [15]. Comprehensive studies [8,9,14,15] show that because rotor PMs are in the direct vicinity of the motor's airgap and stator tooth, SPMtype BLDC motors are more vulnerable to PD fault conditions.…”

mentioning

confidence: 99%

“…Excluding uniform/irreversible demagnetization faults, the other types of PM-related faults are categorized under PD faults [15]. Comprehensive studies [8,9,14,15] show that because rotor PMs are in the direct vicinity of the motor's airgap and stator tooth, SPMtype BLDC motors are more vulnerable to PD fault conditions. Therefore, the possibilities of PD faults in an eight-pole SPMtype BLDC motor when subjected to high stator phase currents were investigated in this study.…”

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confidence: 99%

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Development of an online condition monitoring based system for the partial demagnetization fault diagnosis of SPM‐type BLDC motor

Usman

Rajpurohit

2022

Energy Conversion and Econom

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This study investigates partial demagnetization faults arising from stator interturn faults in a surface-mounted permanent-magnet-type brushless direct current motor. Because of rotor demagnetization, the fault severity increases significantly owing to an increase in the stator phase current and temperature. The effect of such a fault is reflected in machine parameters such as the motor back-EMF and radial magnetic flux, which are used to analyse the characteristics of faults. A mathematical model of a machine under possible fault conditions is developed using the finite element method and advanced hybrid model approaches. Experimental investigations are conducted to validate the proposed methodology. Subsequently, the machine parameters used for fault diagnosis are employed to develop an online expert-based system that can detect, classify and estimate the percent increase in the values of the parameters to determine the fault severity of the machine under fault conditions. It is discovered that the proposed approach is suitable for industrial and commercial applications in electric vehicles, where the machine's state-of-health estimation is crucial for avoiding major faults that may result in its failure. INTRODUCTIONPermanent magnet (PM) synchronous machines, such as brushless direct current (BLDC) motors, are widely deployed in commercial and e-mobility applications, including electric vehicles (EVs). PM synchronous machines comprise high-flux density PMs that are placed either on the surface or interior of the machine rotor. Among the many available PM machines, BLDC motors are preferred because of their simple compact structure, which is characterized by a high torque-to-weight ratio, an efficient power/torque density, and a highly dynamic performance [1,2]. The placement of rotor PMs in the machine depends on the motor type, i.e., surface-mounted permanent magnet (SPM)-type or interior permanent magnet (IPM)-type BLDC motors [3,4]. When the rotor PMs are directly exposed to the air gap and stator teeth, the SPM-type BLDC motor is more susceptible to physical and thermal variations in the machine during operation. Consequently, this results in rotorThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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\begin{equation}{f_q} = \left({\frac{k}{P/2}}\right)\; {f_s} = z{f_r}, z\ = 1,2,3 \ldots ..\end{equation}

where f q is the frequency of z th component in the spectrum, f s is the supply (electrical) frequency and f r is the rotational frequency while P is the number of poles.…”

Section: Frequency and Field Analysis Of Bldc Motor Under Healthy And...mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Development of an online condition monitoring based system for the partial demagnetization fault diagnosis of SPM‐type BLDC motor

Usman

Rajpurohit

2022

Energy Conversion and Econom

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“…IDF is one of the major hurdles for PM type machines in achieving high power/torque density while operating in harsh environments. High operating temperature, vibration, physical damage, and aging cause permanent reduction in the remanence magnetic flux density of the embedded permanent magnets (PMs) in the rotor of a PMSM which is called IDF [33]. To realize the IDF in the experiment, reduced size PMs are designed and inserted in the rotor of the PMSM, as shown in Figure 2.…”

Section: Irreversible Demagnetizationmentioning

confidence: 99%

Detection and Identification of Demagnetization and Bearing Faults in PMSM Using Transfer Learning-Based VGG

2020

Self Cite

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Predictive maintenance in the permanent magnet synchronous motor (PMSM) is of paramount importance due to its usage in electric vehicles and other applications. Recently various deep learning techniques are applied for fault detection and identification (FDI). However, it is very hard to optimally train the deeper networks like convolutional neural network (CNN) on a relatively fewer and non-uniform experimental data of electric machines. This paper presents a deep learning-based FDI for the irreversible-demagnetization fault (IDF) and bearing fault (BF) using a new transfer learning-based pre-trained visual geometry group (VGG). A variant of ImageNet pre-trained VGG network with 16 layers is used for the classification. The vibration and the stator current signals are selected for the feature extraction using the VGG-16 network for reliable detection of faults. A confluence of vibration and current signals-based signal-to-image conversion approach is also introduced for exploiting the benefits of transfer learning. We evaluate the proposed approach on ImageNet pre-trained VGG-16 parameters and training from scratch to show that transfer learning improves the model accuracy. Our proposed method achieves a state-of-the-art accuracy of 96.65% for the classification of faults. Furthermore, we also observed that the combination of vibration and current signals significantly improves the efficiency of FDI techniques.

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“…The irreversible demagnetization (IDF) severely affect the performance of the machine. It increase the losses, cause huge vibration and acustic noise and the machine draws higher current for the demanded load [25]. Therefore, a detailed thermal, mechanical, and demagnetization analysis is necessary to carefully understand the performance of these machines and take step towards thermally and mechanically optimized model to further improve the reliability of PMVMs.…”

Section: Introductionmentioning

confidence: 99%

Demagnetization Risk Assessment in a Dual Stator Permanent Magnet Vernier Machines

Ullah

Siddiqi

Ahmed

2022

2022 IEEE Energy Conversion Congress and Exposition (ECCE)

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As the topologies of permanent magnet vernier machines (PMVM) is getting more complex such as dual rotor and its variants. The thermal, mechanical, and especially demagnetization concern increasing. In this paper, the demagnetization risk evaluation of three similar topologies of dual stator radial type PMVM is presented. Three recently published topologies: dual winding with rotor-yoke, dual winding without rotor-yoke, and single winding without yoke are selected. This design highly improved the torque density and reduced the overall volume. However, the permanent magnets (PMs) in these topologies are at huge risk of irreversible demagnetization. Furthermore, the overall performance of PMtype machines is incomprehensible without a detailed demagnetization analysis. Therefore, a comprehensive mechanical, thermal, and demagnetization analysis considering various operating points and temperatures is conducted to evaluate the risk of demagnetization in these topologies. Finally, some modification are made to optimize of these designs. All analyses are carried out using finite element analysis and cosimulation in ANSYS maxwell and mechanical.

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Online Diagnosis and Severity Estimation of Partial and Uniform Irreversible Demagnetization Fault in Interior Permanent Magnet Synchronous Motor

Cited by 25 publications

References 8 publications

Development of an online condition monitoring based system for the partial demagnetization fault diagnosis of SPM‐type BLDC motor

Development of an online condition monitoring based system for the partial demagnetization fault diagnosis of SPM‐type BLDC motor

Detection and Identification of Demagnetization and Bearing Faults in PMSM Using Transfer Learning-Based VGG

Demagnetization Risk Assessment in a Dual Stator Permanent Magnet Vernier Machines

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