Detection of Induction Motor Broken Bar Fault Through Envelope Analysis Using Start-Up Current

Ahamed, Syed Kamruddin; Sarkar, Apurba; Mitra, Madhuchhanda; Sengupta, Samarjit

doi:10.1016/j.protcy.2012.05.104

Cited by 23 publications

(7 citation statements)

References 9 publications

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“…The reliability of the algorithm was proven from the results and its ability to automatically diagnose the fault regardless the machine size and load conditions. Another technique blends WT and HHT is proposed in [105, 106]. As fault parameter; it uses power detail density and power detail energy.…”

Section: Fault Signaturesmentioning

confidence: 99%

Induction motor broken rotor bar fault detection techniques based on fault signature analysis – a review

Hassan

Amer

Abdelsalam

et al. 2018

IET Electric Power Applications

102

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The induction motor is the most popular motor in energy conversion and industrial drive systems. This popularity is due to its robustness, low cost, and easy maintenance. Electrical, mechanical, thermal, magnetic and environmental stresses cause faults in the induction motor during the operation process. One of the challenging topics for many researchers is the development of a reliable efficient induction motor fault diagnostic techniques. The broken rotor bar fault is one of the critical faults that need to be early detected due to its sudden severe damages. The objective of this study is to present a survey of existing broken rotor bar fault detection techniques with new classification based on fault signature. Various monitoring conditions and signal processing techniques are considered for the detection process. A comprehensive list of references is reported for each fault signature and classified based on (i) loading level, (ii) the number of broken bars, (iii) validation and (vi) signal processing.

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Section: Fault Signaturesmentioning

confidence: 99%

Induction motor broken rotor bar fault detection techniques based on fault signature analysis – a review

Hassan

Amer

Abdelsalam

et al. 2018

IET Electric Power Applications

102

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“…By using HT, low sensitivity to different motor loading performance also analysed [17]. In [18, 19], the fault analysis is based on the extraction of stator current envelope by using HT. The envelope provides information about the low frequency component, which helps to detect fault accurately.…”

Section: Introductionmentioning

confidence: 99%

Broken rotor bar fault detection using Hilbert transform and neural networks applied to direct torque control of induction motor drive

R¹,

Irudayaraj

Saravanan³

et al. 2020

IET power electron.

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“…In order to prevent the premature failure of induction motors [37], the MCSA method and signal processing techniques are applied for early broken bar detection in three-phase induction motors. The lack of this early fault detection may cause damage spread and motor destruction [37,38]. The existence of broken bars in an electric machine, which is the main reason for rotor asymmetry and motor magnetic field changes [6], is evidenced by the appearance of two frequency components located close to the motor operating frequency [15,20,39].…”

Section: Introductionmentioning

confidence: 99%

Broken Bar Diagnosis for Squirrel Cage Induction Motors Using Frequency Analysis Based on MCSA and Continuous Wavelet Transform

Granda

Aguilar

Arcos-Avilés

et al. 2017

MCA

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Abstract:The importance of early fault detection in electric motors has attracted the attention of research groups, as the detection of incipient faults can prevent damage spreading and increase the lifetime of the motor. At present, studies have focused their attention on optimization procedures used for fault detection in induction machines to achieve a quick and easy-to-interpret assessment at an industrial level. This paper proposes an alternative approach based on the Continuous Wavelet Transform (CWT) for broken bar diagnosis in squirrel cage induction motors. This work uses the Motor Current Signature Analysis (MCSA) method to acquire the current signal of the induction motor. The novelty of this study lies in broken bar detection in electric machines operating at non-load by analyzing variations in the spectrum of the motor's current signal. This way, the faults are presented as oscillations in the current signal spectrum. Additionally, a quantification of broken bars for the same type of motors operating at full-load is performed in this study. An experimental validation and the comparison with the Fast Fourier Transform (FFT) technique are provided to validate the proposed technique.

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Detection of Induction Motor Broken Bar Fault Through Envelope Analysis Using Start-Up Current

Cited by 23 publications

References 9 publications

Induction motor broken rotor bar fault detection techniques based on fault signature analysis – a review

Induction motor broken rotor bar fault detection techniques based on fault signature analysis – a review

Broken rotor bar fault detection using Hilbert transform and neural networks applied to direct torque control of induction motor drive

Broken Bar Diagnosis for Squirrel Cage Induction Motors Using Frequency Analysis Based on MCSA and Continuous Wavelet Transform

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