Optimal MLP neural network classifier for fault detection of three phase induction motor

Ghate, Vilas N.; Dudul, S. V.

doi:10.1016/j.eswa.2009.10.041

Cited by 135 publications

(66 citation statements)

References 27 publications

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“…More detailed studies presented by Frosini and Bassi (2010) and Zarei et al (2014) show that bearings are responsible for over 40 % of motor faults, 30 % are related to stator windings, and 10 % to rotor bars. These mechanical and electrical defects can be detected and analyzed directly or indirectly by reading signals from the electrical power supply or voltage, monitoring of the electromagnetic field, temperature measurements, or other metrics (Ghate and Dudul 2010).…”

Section: Three-phase Induction Motor Faultsmentioning

confidence: 99%

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: Three-phase Induction Motor Faultsmentioning

confidence: 99%

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

et al. 2016

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“…They have been extensively used to monitor broken bars [6]− [11], eccentricity [12]− [14], and bearingrelated faults [7]− [10], [15]− [21]. Similarly, the use of support vector machines to diagnose motors faults has been widely reported in literature: for broken bars [6], [22]− [25], bearings [19], [26]− [33], and eccentricity [29].…”

Section: Index Terms-diagnosticmentioning

confidence: 99%

Robust Detection of Incipient Faults in VSI-Fed Induction Motors Using Quality Control Charts

García-Escudero

Duque-Pérez

Fernandez-Temprano

et al. 2017

IEEE Trans. on Ind. Applicat.

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Abstract-A considerable amount of papers has been published in recent years proposing supervised classifiers to diagnose the health of a machine. The usual procedure with these classifiers is to train them using data acquired through controlled experiments, expecting them to perform well on new data, classifying correctly the condition of a motor. But, obviously, the new motor to be diagnosed cannot be the same that has been used during the training process; it may be a motor with different characteristics and fed from a completely different source. These different conditions between the training process and the testing one can deeply influence the diagnosis. To avoid these drawbacks, in this paper, a new method is proposed, which is based on robust statistical techniques applied in quality control applications. The proposed method is based on the online diagnosis of the operating motor and can detect deviations from the normal operational conditions. A robust approach has been implemented using high-breakdown statistical techniques, which can reliably detect anomalous data that often cause an unexpected overestimation of the data variability, reducing the ability of standard procedures to detect faulty conditions in earlier stages. A case study is presented to prove the validity of the proposed approach. Motors of different characteristics, fed from the power line and several different inverters, are tested. Three different fault conditions are provoked: a broken bar, a faulty bearing, and mixed eccentricity. Experimental results prove that the proposed approach can detect incipient faults.

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“…The MLP neural network is composed of input layer, output layer and hidden layer, and the training process of the MLP neural network [13,14] is expressed as follows: Step1: The connection weights of the MLP neural network are initialized. The inputted features are sent to the hidden layer, and the calculation of each neuron of the hidden layer is expressed as follows:…”

Section: Multi-layer Perceptron Neural Networkmentioning

confidence: 99%

Fault Diagnosis of Planetary Gear Based on Fuzzy Entropy of CEEMDAN and MLP Neural Network by Using Vibration Signal

Chen

Liu

2017

ITM Web Conf.

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Abstract.A method of planetary gear fault diagnosis based on the fuzzy entropy of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multi-layer perceptron (MLP) neural network is proposed. The vibration signal is decomposed into multiple intrinsic mode functions (IMFs) by CEEMDAN, and the fuzzy entropy that combines the fuzzy function and sample entropy is proposed and used to extract the feature information contained in each IMF. The fuzzy entropies of each IMF are defined as the input of the MLP neural network, and the planetary gear status can be recognized by the output. The experiments prove the proposed method is effective.

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Optimal MLP neural network classifier for fault detection of three phase induction motor

Cited by 135 publications

References 27 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

Robust Detection of Incipient Faults in VSI-Fed Induction Motors Using Quality Control Charts

Fault Diagnosis of Planetary Gear Based on Fuzzy Entropy of CEEMDAN and MLP Neural Network by Using Vibration Signal

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