Discrete wavelet transform and energy eigen value for rotor bars fault detection in variable speed field-oriented control of induction motor drive

Ameid, Tarek; Menacer, Arezki; Talhaoui, Hicham; Azzoug, Younes

doi:10.1016/j.isatra.2018.04.019

Cited by 50 publications

(43 citation statements)

References 33 publications

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“…To ensure IMs meet the requirements of EV systems, vector control is used. Vector control, also called field-oriented control (FOC), and which brought a fundamental change in the control of the IM [102], can provide a wide range of speed up to 3-4 times base speed; however, the high-speed range efficiency may suffer. This control scheme can reduce the total losses under any loading condition by controlling the currents of the stator and rotor [103].…”

Section: Induction Motormentioning

confidence: 99%

“…PMBLDCs are broadly employed in EVs because of their high-power density and high efficiency. High-quality rare earth permanent magnet materials, such as samarium cobalt (Sm-Co) and neodymium-iron-boron (Nd-Fe-B), have been used on its rotor [102]. As the rotor has no windings, no rotor copper loss exists [100].…”

Section: Permanent Magnet Brushless DC Motor (Pmbldc)mentioning

confidence: 99%

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Technology Development of Electric Vehicles: A Review

et al. 2019

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To reduce the dependence on oil and environmental pollution, the development of electric vehicles has been accelerated in many countries. The implementation of EVs, especially battery electric vehicles, is considered a solution to the energy crisis and environmental issues. This paper provides a comprehensive review of the technical development of EVs and emerging technologies for their future application. Key technologies regarding batteries, charging technology, electric motors and control, and charging infrastructure of EVs are summarized. This paper also highlights the technical challenges and emerging technologies for the improvement of efficiency, reliability, and safety of EVs in the coming stages as another contribution.

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Section: Induction Motormentioning

confidence: 99%

Section: Permanent Magnet Brushless DC Motor (Pmbldc)mentioning

confidence: 99%

Technology Development of Electric Vehicles: A Review

et al. 2019

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“…It loses the information of the time when transforming to the frequency domain and is not appropriate for analysing non‐stationary signals [42]. In addition, it does not provide good accuracy (ACC) in fault detection [44]. To overcome these disadvantages, another technique called WT has been introduced.…”

Section: Wavelet Transform (Wt)mentioning

confidence: 99%

Hybrid detection algorithm for online faulty sensors identification in wireless sensor networks

Gabr¹,

Ahmed²,

Salim³

2020

IET wirel. sens. syst.

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“…On the other hand, in the classification stage, the obtained features are employed for designing and training different pattern recognition algorithms, which automatically determine the IM condition [ 16 ]. In this regard, the fast Fourier transform [ 17 , 18 ], statistical methods [ 19 , 20 ], Welch method [ 21 ], regressive-based models [ 22 ], fractality-based method [ 23 ], entropy-based methods [ 24 , 25 ], multiple signal classification method [ 26 ], wavelet transform [ 27 , 28 , 29 ], empirical mode decomposition [ 30 , 31 ], and principal component analysis [ 32 ], among other indices or methods, have been explored to extract patterns about the IM condition. In a similar venue, different pattern recognition algorithms have already been presented to diagnose the IM condition automatically, e.g., artificial neural networks [ 4 ], fuzzy logic systems [ 23 ], k-means [ 33 ], support vector machines [ 34 ], and decision trees [ 35 ], among others.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network and Motor Current Signature Analysis during the Transient State for Detection of Broken Rotor Bars in Induction Motors

Valtierra-Rodríguez

Rivera-Guillen

Basurto-Hurtado

et al. 2020

Sensors

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Although induction motors (IMs) are robust and reliable electrical machines, they can suffer different faults due to usual operating conditions such as abrupt changes in the mechanical load, voltage, and current power quality problems, as well as due to extended operating conditions. In the literature, different faults have been investigated; however, the broken rotor bar has become one of the most studied faults since the IM can operate with apparent normality but the consequences can be catastrophic if the fault is not detected in low-severity stages. In this work, a methodology based on convolutional neural networks (CNNs) for automatic detection of broken rotor bars by considering different severity levels is proposed. To exploit the capabilities of CNNs to carry out automatic image classification, the short-time Fourier transform-based time–frequency plane and the motor current signature analysis (MCSA) approach for current signals in the transient state are first used. In the experimentation, four IM conditions were considered: half-broken rotor bar, one broken rotor bar, two broken rotor bars, and a healthy rotor. The results demonstrate the effectiveness of the proposal, achieving 100% of accuracy in the diagnosis task for all the study cases.

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Discrete wavelet transform and energy eigen value for rotor bars fault detection in variable speed field-oriented control of induction motor drive

Cited by 50 publications

References 33 publications

Technology Development of Electric Vehicles: A Review

Technology Development of Electric Vehicles: A Review

Hybrid detection algorithm for online faulty sensors identification in wireless sensor networks

Convolutional Neural Network and Motor Current Signature Analysis during the Transient State for Detection of Broken Rotor Bars in Induction Motors

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