Broken bars fault diagnosis based on uncertainty bounds violation for three-phase induction motors

Mustafa, Mohammed Obaid; Nikolakopoulos, George; Gustafsson, Thomas

doi:10.1002/etep.1843

Cited by 12 publications

(13 citation statements)

References 41 publications

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“…Broken rotor bars (BRB) or end ring cracking is one of the most common faults of induction motor, which accounts for about 10% of all failures [3]. This percentage is inconspicuous, but BRB fault will not only reduce the efficiency, but also cause other faults, such as bearing fault, air-gap eccentricity [4]. In severe cases, broken pieces of the rotor bar may even damage the stator windings during operation [5].…”

Section: Introductionmentioning

confidence: 99%

IBPSO-Based MUSIC Algorithm for Broken Rotor Bars Fault Detection of Induction Motors

Wang

Chen

Zhang

et al. 2018

Chin. J. Mech. Eng.

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In spectrum analysis of induction motor current, the characteristic components of broken rotor bars (BRB) fault are often submerged by the fundamental component. Although many detection methods have been proposed for this problem, the frequency resolution and accuracy are not high enough so that the reliability of BRB fault detection is affected. Thus, a new multiple signal classification (MUSIC) algorithm based on particle swarm intelligence search is developed. Since spectrum peak search in MUSIC is a multimodal optimization problem, an improved bare-bones particle swarm optimization algorithm (IBPSO) is proposed first. In the IBPSO, a modified strategy of subpopulation determination is introduced into BPSO for realizing multimodal search. And then, the new MUSIC algorithm, called IBPSO-based MUSIC, is proposed by replacing the fixed-step traversal search with IBPSO. Meanwhile, a simulation signal is used to test the effectiveness of the proposed algorithm. The simulation results show that its frequency precision reaches 10 −5 , and the computational cost is only comparable to that of traditional MUSIC with 0.1 search step. Finally, the IBPSO-based MUSIC is applied in BRB fault detection of an induction motor, and the effectiveness and superiority are proved again. The proposed research provides a modified MUSIC algorithm which has sufficient frequency precision to detect BRB fault in induction motors. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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Section: Introductionmentioning

confidence: 99%

IBPSO-Based MUSIC Algorithm for Broken Rotor Bars Fault Detection of Induction Motors

Wang

Chen

Zhang

et al. 2018

Chin. J. Mech. Eng.

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“…During early stage, such as one broken bar, this type of fault may not show any symptoms. If no detection system or program, such fault will gradually deteriorate, causing other faults or leading to the sudden collapse [1][2][3]. It is known that unplanned downtime often causes a great economic loss [3].…”

Section: Introductionmentioning

confidence: 99%

Detection of Broken Rotor Bars Fault in Induction Motors by Using an Improved MUSIC and Least-Squares Amplitude Estimation

Wang

Duan

et al. 2018

Mathematical Problems in Engineering

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The frequencies and amplitudes of the broken rotor bar (BRB) fault features are the basis for the accurate diagnosis of the BRB fault. However, how to accurately detect their frequency and amplitudes has always been a difficult problem for induction motor fault detection. For this problem, a new fault detection method based on an improved multiple signal classification (MUSIC) and least-squares magnitude estimation is proposed. First, since the fixed-step traversal search reduces the computational efficiency of MUSIC, a niche bare-bones particle swarm optimization (NBPSO) for multimodal peaks search is proposed to improve MUSIC, which is used to compute the frequency values of fault-related and fundamental components in stator current signal. Second, using these frequency values, a fault current signal model is established to convert the magnitude estimation problem into a linear least-squares problem. On this basis, the amplitudes and phases of fault-related and fundamental components could be estimated accurately with the singular value decomposition (SVD). A simulation signal is used to test the new method and the results show that the proposed method not only has higher frequency resolution, but also improves estimation accuracy of parameters greatly even with short data window. Finally, experiments for a real induction motor are performed, and the effectiveness and superiority of the proposed method are proved again.

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“…In industrial applications, where motor overload is needed for long time intervals, the motor overload produces thermal stress in the motor's internal components, since the machine is forced to work in different operating conditions from those envisaged by its original design [8].…”

Section: Introductionmentioning

confidence: 99%

“…The presence of a fault in an electric motor associated with a critical industrial process leads to emergency interruptions [9], which causes production process losses [8,10,11]. Therefore, early fault detection is required to identify incipient fault presence in induction motors, and consequently to avoid these problems [6,12].…”

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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Broken bars fault diagnosis based on uncertainty bounds violation for three-phase induction motors

Cited by 12 publications

References 41 publications

IBPSO-Based MUSIC Algorithm for Broken Rotor Bars Fault Detection of Induction Motors

IBPSO-Based MUSIC Algorithm for Broken Rotor Bars Fault Detection of Induction Motors

Detection of Broken Rotor Bars Fault in Induction Motors by Using an Improved MUSIC and Least-Squares Amplitude Estimation

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

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