A comprehensive evaluation of intelligent classifiers for fault identification in three-phase induction motors

Palácios, Rodrigo Henrique Cunha; Silva, Ivan Nunes da; Goedtel, Alessandro; Godoy, Wagner Fontes

doi:10.1016/j.epsr.2015.06.008

Cited by 106 publications

(48 citation statements)

References 39 publications

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“…Recently, the works of Godoy et al (2014), Godoy et al (2015) and Palácios et al (2015) employ the same technique to generate relevant results. Additionally, Godoy et al (2014) and Godoy et al (2015) study the number of points per half-cycle to be used for classifying the fault state.…”

Section: Data Acquisition and Processingmentioning

confidence: 97%

“…Four types of faults were recreated in a controlled manner, such as an outer race fault (Zarei et al 2014;Prieto et al 2013;Vakharia et al 2015;Pandya et al 2014;Palácios et al 2015), inner race fault (Zarei et al 2014;Prieto et al 2013;Vakharia et al 2015;Pandya et al 2014), short circuit and excessive wear (Prieto et al 2013).…”

Section: Data Acquisition and Processingmentioning

confidence: 99%

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Bearing fault identification of three-phase induction motors bases on two current sensor strategy

et al. 2016

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Three-phase induction motors are the most commonly used devices for electromechanical energy conversion. This study proposes an alternative approach for identifying bearing faults in induction motors, using two current sensors and a pattern classifier, based on artificial neural networks. To validate the methodology, results are given from experiments carried out on a test bench where the motors operate with different types of bearing faults, under varying conditions of load torque and voltage unbalance. This paper also provides the comparative performance of neural network and random forest classifiers. This study also presents an analysis of the current signals in the time domain, applied to different neural structures.

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Section: Data Acquisition and Processingmentioning

confidence: 97%

Section: Data Acquisition and Processingmentioning

confidence: 99%

Bearing fault identification of three-phase induction motors bases on two current sensor strategy

et al. 2016

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“…Asr et al have applied wavelet packet transform and fast Fourier transform and used rms value of wavelet coefficients as fault features for classification. Palacios et al have worked upon stator current signals for feature extraction and compared different classifiers, like naive Bayes, SVM, kNN, multilayer perceptron, C4.5 decision tree, and repeated incremental pruning for fault detection of stator, rotor, and bearings . Multiple signatures, i.e., current and voltage Park's vector components, have been used as the features to be selected further by sequential backward selection and genetic algorithm and classified by using artificial ant clustering …”

Section: Introductionmentioning

confidence: 99%

Detection and diagnosis of induction motor bearing faults using multiwavelet transform and naive Bayes classifier

Saini

Aggarwal

2018

Int Trans Electr Energ Syst

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Summary A novel framework is proposed for the classification of motor bearing faults by using multiwavelet transform and naive Bayes classifier. This work has explored the application of multiwavelet transform to the vibration signatures for extracting the effective fault features. Geronimo‐Hardin‐Massopust multiwavelet filter bank has been employed in this work for multiresolution analysis up to fourth decomposition level. This work has relied upon multiwavelets as the multiwavelets are enriched with the properties which wavelets do not offer simultaneously like compact support and orthogonality without compromising with symmetricity and higher order of vanishing moments. After that, fault features have been extracted from the decomposed vibration signals to utilize the fault information present at different resolution levels. The fault classification has been accomplished with high success rate by using the extracted fault features with the naive Bayes classifier due to its inherent features like robustness to noise, simplicity, and quick convergence even with less training data. The efficacy of the proposed classifier has been verified with both the offline signals and the real‐time signals having different fault severity levels. The outperformance of the proposed classifier has been shown by comparing the classification results with other established supervised classification techniques like probabilistic neural network, Gaussian‐support vector machine, polynomial‐support vector machine, and linear‐support vector machine.

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“…In general, an MFNN consists of an input layer, one or more hidden layers, and an output layer [36]. Different topologies of the feedforward neural network with different numbers of hidden layers and hidden neurons have been used to create a suboptimal network that correlates the extracted features with its corresponding number of shorted turns.…”

Section: Neural Network Selection Training and Testingmentioning

confidence: 99%

“…Huge research efforts have been undertaken worldwide to develop incipient fault (before the actual occurrence of faults) diagnostic techniques. Neural network (NN) or known as artificial neural network (ANN) is a tool that plays an important role in developing online and offline diagnostic tools for motors, generators, transmission lines, cables, and transformers [32][33][34][35][36][37]. A mathematical model of an induction machine with stator inter-turn fault has been derived based on winding function theory [8].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Stator Inter-Turn Fault Diagnosis Tool for Induction Motors

2018

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Induction motors constitute the largest proportion of motors in industry. This type of motor experiences different types of failures, such as broken bars, eccentricity, and inter-turn failure. Stator winding faults account for approximately 36% of these failures. As such, condition monitoring is used to protect motors from sudden breakdowns. This paper proposes the use of neural networks as an efficient diagnostic tool for estimating the percentage of stator winding shorted turns in three-phase induction motors. A MATLAB-based model was developed and simulated under different fault-load combination cases for different sizes of motors. The motor's developed electromechanical torque was selected as a fault indicator. For the design and training of the neural network, the mean, variance, max, min, and F120 time based on statistical and frequency-related features were found to be very distinct for correlating the captured electromechanical torque with its corresponding percentage of shorted turns. In the training phase of the neural network, five different motors were used and are referred to as seen motors. On the other hand, for testing the efficiency of the developed diagnostic tool, the electromechanical torque under different fault-load combination cases, previously never seen from the first five motors and those of two new motors (referred to as unseen), was used. Testing results revealed accuracy in the range of 88-99%.

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A comprehensive evaluation of intelligent classifiers for fault identification in three-phase induction motors

Cited by 106 publications

References 39 publications

Bearing fault identification of three-phase induction motors bases on two current sensor strategy

Bearing fault identification of three-phase induction motors bases on two current sensor strategy

Detection and diagnosis of induction motor bearing faults using multiwavelet transform and naive Bayes classifier

An Efficient Stator Inter-Turn Fault Diagnosis Tool for Induction Motors

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