Development of a tool to detect faults in induction motors via current signature analysis

Fenger, M.; Lloyd, B.A.; Thomson, William T.

doi:10.1109/citcon.2003.1204707

Cited by 40 publications

(13 citation statements)

References 9 publications

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“…The observation of motors working in the normal state or at faulty conditions of the bars allows to point out some typical symptoms indicating the bar faults [4][5][6]. To the most important belong changes of the harmonic spectrum in the phase currents and voltages, change of the shaft flux, increased vibration and noise of the machine [4,7,8,17].…”

Section: Introductionmentioning

confidence: 98%

“…To the most important belong changes of the harmonic spectrum in the phase currents and voltages, change of the shaft flux, increased vibration and noise of the machine [4,7,8,17]. These symptoms let us create a diagnostic model of the machine, responsible for early estimation of the technical bar fault of the motor.…”

Section: Introductionmentioning

confidence: 99%

“…All these methods of current preprocessing are combined with different tools of analysis of the results of this preprocessing stage, forming the final classification stage. Among them, we can mention the statistical approach to classification [4,6,8], the artificial neural networks [5,9,17] or support vector machine [1,3,10]. They are responsible for the automatic recognition of fault.…”

Section: Introductionmentioning

confidence: 99%

“…Most of them use Fourier transformation of the stator current in a steady state [4,14,19]. Some apply more sophisticated method of wavelet analysis of stator current in transient state [18].…”

Section: Introductionmentioning

confidence: 99%

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Support vector machine for fault diagnosis of the broken rotor bars of squirrel-cage induction motor

Kurek

Osowski

2009

Neural Comput & Applic

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The paper presents an automatic computerized system for the diagnosis of the rotor bars of the induction electrical motor by applying the support vector machine. Two solutions of diagnostic system have been elaborated. The first one, called fault detection, discovers only the case of the fault occurrence. The second one (complex diagnosis) is able to find which bars have been damaged. The most important problem is concerned with the generation and selection of the diagnostic features, on the basis of which the recognition of the state of the rotor bars is done. In our approach, we use the spectral information of the motor current, voltage and shaft field of one phase registered in an instantaneous form. The selected features form the input vector applied to the support vector machine, used as the classifier. The results of the numerical experiments are presented and discussed in the paper.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Support vector machine for fault diagnosis of the broken rotor bars of squirrel-cage induction motor

Kurek

Osowski

2009

Neural Comput & Applic

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“…However, these machines will have a wider chance to fail because of heavy duty cycles, installation, poor working environment, improper excitation and manufacturing factors, etc. With increasing demands for efficiency and reliability, the field of fault diagnosis in induction motors is gaining importance [1]- [3]. There are plentiful methods are presented in existing like based on chemical monitoring, machine pulsation (vibration) monitoring, thermal monitoring, aural secretion monitoring, but all have necessitate of exclusive dedicated sensors or equipments to obtain the specific gesture to process, whereas in this projected current monitoring procedure there is no supplementary sensors required [2].…”

Section: Introductionmentioning

confidence: 99%

Testing and Analysis of Induction Motor Electrical Faults Using Current Signature Analysis

Prakasam¹,

Ramesh²

2016

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The proposed method deals with the emerging technique called as Motor Current Signature Analysis (MCSA) to diagnosis the stator faults of Induction Motors. The performance of the proposed method deals with the emerging technique called as Motor Current Signature Analysis (MCSA) and the Zero-Sequence Voltage Component (ZSVC) to diagnose the stator faults of Induction Motors. The unalleviated study of the robustness of the industrial appliances is obligatory to verdict the fault of the machines at precipitate stages and thwart the machine from brutal damage. For all kinds of industry, a machine failure escorts to a diminution in production and cost increases. The Motor Current Signature Analysis (MCSA) is referred as the most predominant way to diagnose the faults of electrical machines. Since the detailed analysis of the current spectrum, the method will portray the typical fault state. This paper aims to present dissimilar stator faults which are classified under electrical faults using MCSA and the comparison of simulation and hardware results. The magnitude of these fault harmonics analyzes in detail by means of Finite-Element Method (FEM). The anticipated method can effectively perceive the trivial changes too during the operation of the motor and it shows in the results.

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Analysis of stator current of induction motor used in transport system at single phasing by measuring phase angle, symmetrical components, Skewness, Kurtosis and harmonic distortion in Park plane

Chattopadhyay

Chattopadhyay²,

Sengupta³

2014

IET Electrical Systems in Transportation

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Stator current of induction motor is analysed at single phasing by measuring phase angle, symmetrical components, Skewness, Kurtosis and harmonic distortion in Park plane. Diagnosis of single phasing of induction motor has been achieved by sequence components and stator current based pattern assessment. Distinct phase shift from 120°to 180°is observed between healthy phases. Patterns are formed using line currents and single phasing is assessed by using rule sets developed from features extracted from those patterns. Experimental result agrees with rule set and result obtained by assessment using sequence components. Then assessment is done by analysing wavelet decomposition based Skewness and Kurtosis of stator current in Park plane. At last, area based approach is used for assessment of single phasing and different distortion factors because of harmonics in Park plane are assessed. Accuracy increases with the increase of sampling rate of data acquisition system and use of Park plane has reduced computation effort by two-thirds. Thus single phasing is detected by sequence components supported by pattern formation and feature extraction and then harmonics during single phasing are assessed by wavelet decomposition and area-based approach in Park plane.

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Development of a tool to detect faults in induction motors via current signature analysis

Cited by 40 publications

References 9 publications

Support vector machine for fault diagnosis of the broken rotor bars of squirrel-cage induction motor

Support vector machine for fault diagnosis of the broken rotor bars of squirrel-cage induction motor

Testing and Analysis of Induction Motor Electrical Faults Using Current Signature Analysis

Analysis of stator current of induction motor used in transport system at single phasing by measuring phase angle, symmetrical components, Skewness, Kurtosis and harmonic distortion in Park plane

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