Short Circuit Incipient Fault Detection and Supervision in a Three-Phase Induction Motor with a SOM-Based Algorithm

Coelho, David N.; Medeiros, Cláudio M. S.

doi:10.1007/978-3-642-35230-0_32

Cited by 6 publications

(8 citation statements)

References 10 publications

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“…T verified with the confusion matrix of table 2. The same problem occurred in [16], w was 87.5%, but the accuracy specificall condition data set is just 52%. These are not desirable c incipient short circuit detectio bring unexpected downtime previously mentioned, reduce i In order to solve this pro method, the authors tried to i algorithms.…”

Section: Resultsmentioning

confidence: 58%

“…[16] done a statistic analysis of the variance of each harmonic generated by this method, noticed that some harmonics of the fundamental frequency inverter voltage varied the most with the different conditions of faults, and used these harmonics as attributes to this problem. In other hand, in [11], after taking into account other methods in addition to [18], and testing each component relevancy to an specific Artificial Neural Network (ANN), the authors chose the frequencies 0,5 , 1,5 , 2,5 , 3 , 5 e 7 as attributes to this problem, where " " is the source fundamental frequency.…”

Section: Experimental Data Acquisitonmentioning

confidence: 99%

“…The test bench made in order to apply these load levels can be seen in Figure 2. In [16] each part of the previously shown test bench is thoroughly described.…”

Section: Experimental Data Acquisitonmentioning

confidence: 99%

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Performance comparison of classifiers in the detection of short circuit incipient fault in a three-phase induction motor

Coelho

Barreto

Medeiros

et al. 2014

2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)

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This paper aims at the detection of short-circuit incipient fault condition in a three-phase squirrel-cage induction motor fed by a sinusoidal PWM converter. In order to detect this fault, different operation conditions are applied to an induction motor, and each sample of the real data set is taken from the line currents of the PWM converter aforementioned. For feature extraction, the Motor Current Signature Analysis (MCSA) is used. The detection of this fault is treated as a classification problem, therefore different supervised algorithms of machine learning are used so as to solve it: Multi-layer Perceptron (MLP), Extreme Learning Machine (ELM), Support-Vector Machine (SVM), Least-Squares Support-Vector Machine (LSSVM), and the Minimal Learning Machine (MLM). These classifiers are tested and the results are compared with other works with the same data set. In near future, an embedded system can be equipped with these algorithms.

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Section: Resultsmentioning

confidence: 58%

Section: Experimental Data Acquisitonmentioning

confidence: 99%

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Performance comparison of classifiers in the detection of short circuit incipient fault in a three-phase induction motor

Coelho

Barreto

Medeiros

et al. 2014

2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)

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“…Penman et al [10] develop an equation (1) to calculate which harmonic components in the axial leakage flux waveform are functions of the SWITSC and then they propose a method to detect the fault by monitoring those components.…”

Section: Stator Winding Interturn Short-circuit Overviewmentioning

confidence: 99%

“…Using converter-fed motors, Kowalski and Wolkiewicz [5] analyze the spectrum of instantaneous Park power and torque signals to diagnosis early SWITSC and broken rotor bars. Coelho and Medeiros [1] try to map the SWITSC fault using selforganized map and then classify the motor.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Used to Stator Winding Interturn Short-Circuit Fault Detection in an Induction Motor Driven by Frequency Converter

Oliveira

Pontes

Medeiros³

2013

2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence

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This work is the application of a Multilayer Perceptron Artificial Neural Network (MLP ANN) to detect early interturn short-circuit faults in a three-phase converter-fed induction motor. The quantity used to analyze the problem is the stator current or, more specifically, the harmonic content of its frequency spectrum, also called current signature. The analysis through the current signature is a non-invasive method and may be embedded in the frequency converter, what is a great advantage. The dataset used for training and validating the ANN is obtained using a test bench that allows applying different levels of interturn short-circuits in the machine. It is observed that the fault motor dataset and healthy motor dataset are difficult to separate, which demands a large computational effort to choose a proper MLP topology. The MLP is trained by two different algorithms (the classical error Backpropagation -BP -and an adaptation of the newer Extreme Learning Machine -ELM) and the results are thoroughly explored, including after the application of a pruning method called CAPE. Then it is slightly compared with the results of a Self-Organized Map ANN [1] obtained by using the same dataset.

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Classification and sensitivity analysis to detect fault in induction motors using an MLP network

Vieira

Medeiros

Silva

2016

2016 International Joint Conference on Neural Networks (IJCNN)

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Short Circuit Incipient Fault Detection and Supervision in a Three-Phase Induction Motor with a SOM-Based Algorithm

Cited by 6 publications

References 10 publications

Performance comparison of classifiers in the detection of short circuit incipient fault in a three-phase induction motor

Performance comparison of classifiers in the detection of short circuit incipient fault in a three-phase induction motor

Neural Network Used to Stator Winding Interturn Short-Circuit Fault Detection in an Induction Motor Driven by Frequency Converter

Classification and sensitivity analysis to detect fault in induction motors using an MLP network

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