Comparative influence of adjacent and non-adjacent broken rotor bars on the induction motor diagnosis through MCSA and ZSC methods

Antonino-Daviu, José A.; Gyftakis, Konstantinos N.; Garcia-Hernandez, Raul; Razik, Hubert; Cardoso, António J. Marques

doi:10.1109/iecon.2015.7392343

Cited by 34 publications

(37 citation statements)

References 16 publications

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“…A remedy to this situation is to extract features from the 0 sequence current instead of the phase current. The reason is that harmonic components of 0 sequence currents are more sensitive to spaced broken rotor bars …”

Section: Performance Evaluation On Simulated Datamentioning

confidence: 99%

Rotor fault diagnosis in induction motors by the matrix pencil method and support vector machine

Chahine

2018

Int Trans Electr Energ Syst

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Summary This paper proposes a high‐resolution motor‐current‐signature‐analysis method and a supervised learning algorithm for the detection and classification of broken rotor bars and broken end‐ring connectors in 3‐phase induction motors. Signature analysis relies on the matrix pencil method (MPM), a well‐known model‐based parameter estimation technique, to extract representative features or signatures from stator current signals. Extracted feature vectors are subsequently used to train off‐line a support vector machine classifier. Once trained, the classifier is tested on a benchmark dataset of simulated stator current signals representing healthy and faulty rotors with the aim of classifying the underlying motor condition. The obtained results validate the matrix pencil method as a feature extraction method and show that the trained classifier achieves a 100% success rate in identifying the number of broken bars and connectors. Moreover, the advantages of the matrix pencil method over fast Fourier transform are demonstrated using experimental data.

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Section: Performance Evaluation On Simulated Datamentioning

confidence: 99%

Rotor fault diagnosis in induction motors by the matrix pencil method and support vector machine

Chahine

2018

Int Trans Electr Energ Syst

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“…Condition monitoring of electric motors was developed for measurement and analysis of diagnostic signals such as acoustic [1,2], thermal [3,4], electric current [5][6][7], and vibration [8][9][10][11][12][13]. Each type of signal has advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

“…Motor current signature analysis (MCSA) is often used for current analysis. Usually MCSA provides good results [1,5,6]. Singh and Naikan [5] proposed a method using MUSIC analysis (MSC-MUSIC).…”

Section: Introductionmentioning

confidence: 99%

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Vibration‐Based Fault Diagnosis of Commutator Motor

Głowacz

2018

Shock and Vibration

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This paper presents a study on vibration-based fault diagnosis techniques of a commutator motor (CM). Proposed techniques used vibration signals and signal processing methods. The authors analysed recognition efficiency for 3 states of the CM: healthy CM, CM with broken tooth on sprocket, CM with broken rotor coil. Feature extraction methods called MSAF-RATIO-50-SFC (method of selection of amplitudes of frequencies ratio 50 second frequency coefficient), MSAF-RATIO-50-SFC-EXPANDED were implemented and used for an analysis. Feature vectors were obtained using MSAF-RATIO-50-SFC, MSAF-RATIO-50-SFC-EXPANDED, and sum of RSoV. Classification methods such as nearest mean (NM) classifier, linear discriminant analysis (LDA), and backpropagation neural network (BNN) were used for the analysis. A total efficiency of recognition was in the range of 79.16%–93.75% (TV). The proposed methods have practical application in industries.

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“…These kinds of signals do not have many disturbances. The recognition of electrical signals of motors was also described in the literature [30][31][32][33][34][35][36][37][38][39][40]. However more analyses are needed in this topic, to develop more efficient methods of fault diagnosis .…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Three Phase Induction Motor Using Current Signal, MSAF-Ratio15 and Selected Classifiers

Głowacz¹,

Głowacz²,

Głowacz³

et al. 2017

Archives of Metallurgy and Materials

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A degradation of metallurgical equipment is normal process depended on time. Some factors such as: operation process, friction, high temperature can accelerate the degradation process of metallurgical equipment. In this paper the authors analyzed three phase induction motors. These motors are common used in the metallurgy industry, for example in conveyor belt. The diagnostics of such motors is essential. An early detection of faults prevents financial loss and downtimes. The authors proposed a technique of fault diagnosis based on recognition of currents. The authors analyzed 4 states of three phase induction motor: healthy three phase induction motor, three phase induction motor with 1 faulty rotor bar, three phase induction motor with 2 faulty rotor bars, three phase induction motor with faulty ring of squirrel-cage. An analysis was carried out for original method of feature extraction called MSAF-RATIO15 (Method of Selection of Amplitudes of Frequencies -Ratio 15% of maximum of amplitude). A classification of feature vectors was performed by Bayes classifier, Linear Discriminant Analysis (LDA) and Nearest Neighbour classifier. The proposed technique of fault diagnosis can be used for protection of three phase induction motors and other rotating electrical machines. In the near future the authors will analyze other motors and faults. There is also idea to use thermal, acoustic, electrical, vibration signal together.

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Comparative influence of adjacent and non-adjacent broken rotor bars on the induction motor diagnosis through MCSA and ZSC methods

Cited by 34 publications

References 16 publications

Rotor fault diagnosis in induction motors by the matrix pencil method and support vector machine

Rotor fault diagnosis in induction motors by the matrix pencil method and support vector machine

Vibration‐Based Fault Diagnosis of Commutator Motor

Fault Diagnosis of Three Phase Induction Motor Using Current Signal, MSAF-Ratio15 and Selected Classifiers

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