Eccentricity fault diagnosis indices for permanent magnet machines: state‐of‐the‐art

Faiz, Jawad; Nejadi-Koti, Hossein

doi:10.1049/iet-epa.2018.5751

Cited by 25 publications

(15 citation statements)

References 127 publications

(197 reference statements)

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“…However, the additional harmonic order of cogging torque is generated by the rotor eccentricity. SE or DE is diagnosed using the additional harmonic order [13][14][15][16][17][18][19]. Vibration caused by rotor eccentricity is analyzed by measuring the amplitude and frequency of the vibration and calculating the deformation of the motor.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis and Robust Design Optimization of SPMSM Considering Back EMF and Cogging Torque due to Static Eccentricity

Park

Kim

et al. 2021

Energies

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Static eccentricity (SE) is frequently generated by manufacturing processes. As the nonuniformity of the air-gap length is caused by the SE, the torque ripple and cogging torque increase in the motor. This study analyzes the distorted back electromotive force (EMF) and cogging torque due to SE. Further, a motor design considering SE is performed for stable back EMF and low cogging torque. First, the SE was diagnosed and analyzed using the back EMF and cogging torque measured from the test results of the base model. In addition, the rotor position was calculated using the unbalanced back EMF due to the SE. The calculated rotor position is used when analyzing phenomena due to SE and applied to robust design. Subsequently, robust design optimization was performed to improve the unbalanced back EMF and cogging torque due to SE. Using finite element analysis (FEA) considering SE, the shape of the stator was designed based on the base model. The estimated rotor position from the base model was applied to the optimum model to confirm its robustness from SE’s effects. Finally, the base and optimum models were compared through the test results.

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Section: Introductionmentioning

confidence: 99%

Diagnosis and Robust Design Optimization of SPMSM Considering Back EMF and Cogging Torque due to Static Eccentricity

Park

Kim

et al. 2021

Energies

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“…The eccentricity fault is divided into static, dynamic and mixed eccentricities [30]. For SE fault, the rotor symmetrical axis coincides with the rotor rotational axis, and it is displaced from the stator symmetrical axis.…”

Section: Eccentricity Faultmentioning

confidence: 99%

Static and dynamic eccentricity fault diagnosis of large salient pole synchronous generators by means of external magnetic field

Ehya

Nysveen

Nilssen

et al. 2021

IET Electric Power Appl

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Although synchronous generators are robust and long-lasting equipment of power plants, consistent electricity production depends on their health conditions. Static and dynamic eccentricity faults are among the prevalent faults that may have a costly effect. Although several methods have been proposed in the literature to detect static and dynamic eccentricity faults in salient pole synchronous generators (SPSGs), they are non-sensitive to a low degree of failure and require a predefined threshold to recognise the fault occurrence that may vary based on machine configuration. This article presents a detailed magnetic analysis of the SPSGs with static and dynamic eccentricity faults by focusing on the external magnetic field. The external magnetic field was measured using two search coils installed on the backside of the stator yoke. Also, advanced signal processing tools based on wavelet entropy were used to analyse the induced electromotive force (emf) in search coils to extract the fault index. The proposed index required no threshold to recognise the starting point of fault occurrence and was sensitive to a low degree of fault. It was also non-sensitive to load variation and noise that may induce a false alarm. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…MCSA methods are well developed to show the effects of faults in electrical signatures properly. However, in the initial stages of electrical and mechanical faults and different load levels, the extracted features used for signal processing techniques such as time domain, frequency domain, and time scale cannot show the severity of fault properly [8]. Fault detection and severity identification based on machine learning methods have recently been introduced [9].…”

Section: Introductionmentioning

confidence: 99%

Classification of Fault Severity in Induction Machine Systems Based on Temporal Convolutions and Recurrent Networks

Mashayekhi

Borzadaran

Marzebali

2022

International Transactions on Electrical Energy Systems

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Detection and severity identification of mechanical and electrical faults by means of noninvasive methods such as electrical signatures of induction machine have attracted much attention in recent years. Since operating conditions of machines and severity of faults in incipient stages influence the amplitude of fault index in the fault detection process, diagnosing fault occurrence and severity can be more complicated. In this study, an efficient method for fault detection and classification in induction machine based on deep neural networks is introduced. The introduced method applies the long short-term memory (LSTM) and fully convolutional neural networks (FCNs) in a conjoined manner. The authors use the FCN architecture for feature extraction from the time-series signal and augment it with LSTM to improve classification performance. This structure has not been previously applied for fault severity detection in induction machine systems. The authors avoid manual feature engineering and, by eliminating the preprocessing phase, directly use time series of electrical signals for fault detection and classifications. The experimental results have been carried out in different fault severities and loads. The analysis of the results and comparison with other deep and classical methods show that the faulty cases can be separated based on severity and load levels with a high accuracy (98.92%), which shows that the adopted architecture is successful in automatically extracting discriminative features from the signal.

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Eccentricity fault diagnosis indices for permanent magnet machines: state‐of‐the‐art

Cited by 25 publications

References 127 publications

Diagnosis and Robust Design Optimization of SPMSM Considering Back EMF and Cogging Torque due to Static Eccentricity

Diagnosis and Robust Design Optimization of SPMSM Considering Back EMF and Cogging Torque due to Static Eccentricity

Static and dynamic eccentricity fault diagnosis of large salient pole synchronous generators by means of external magnetic field

Classification of Fault Severity in Induction Machine Systems Based on Temporal Convolutions and Recurrent Networks

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