Diagnosis of Stator Faults Severity in Induction Motors Using Two Intelligent Approaches

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

doi:10.1109/tii.2017.2696978

Cited by 45 publications

(25 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Regarding the comparison of the obtained results with the others found in recent literature the following apply: an MAS‐based approach for fault diagnosis of IMs has been presented by [26, 31]. The authors proposed an MAS in which fault diagnosis was made based on mutual agreement between agents employing intelligent classifiers, i.e.…”

Section: Resultsmentioning

confidence: 87%

“…Agents representing the stator, rotor and bearings were trained with the k ‐NN rule, a multilayer perceptron (MLP) and the SVM/sequential minimal optimisation method, respectively. Palácios et al [31] used current signal analysis in the time domain and an MAS with a classifier behaviour to make the fault diagnosis of stator faults caused by short circuits among stator coil turns. Drakaki et al [32] proposed an intelligent MAS in order to make the rotor bar fault diagnosis of a three‐phase SCIM.…”

Section: Brief Literature Review Of Ai Approachesmentioning

confidence: 99%

See 1 more Smart Citation

Study on fault diagnosis of broken rotor bars in squirrel cage induction motors: a multi‐agent system approach using intelligent classifiers

Drakaki

Karnavas

Karlis

et al. 2020

IET Electric Power Applications

View full text Add to dashboard Cite

A critical factor in industrial production maintenance is decision‐making in fault diagnosis. Motor current signature analysis (MCSA) is an established condition‐based maintenance method to make fault diagnosis in induction motors (IMs). The occurrence of multiple interacting faults, as well as emergent behaviour, can be tackled efficiently especially when MCSA is combined with distributed problem‐solving artificial intelligence (AI) techniques. Therefore, in this study, an intelligent multi‐agent system (MAS) is presented to make decisions on the fault conditioning of a three‐phase squirrel cage IM. The incorporated agents represent different health conditions of the same IM, with faults that may occur in the rotor bars. All agents utilise an AI method, and use for training MCSA experimental data. A supervisor agent (SA) initially communicates with agents employing feed‐forward artificial neural networks trained with the back‐propagation algorithm and performs the final fault diagnosis by evaluating their responses. When a decision cannot be made on the fault type, the SA employs another agent that uses the k‐nearest neighbour rule. The proposed method achieves high fault diagnosis accuracy. Its performance is also compared with results previously obtained from the same motor when an adaptive neuro‐fuzzy inference system combined with a subtractive clustering method had been used.

show abstract

Section: Resultsmentioning

confidence: 87%

Section: Brief Literature Review Of Ai Approachesmentioning

confidence: 99%

Study on fault diagnosis of broken rotor bars in squirrel cage induction motors: a multi‐agent system approach using intelligent classifiers

Drakaki

Karnavas

Karlis

et al. 2020

IET Electric Power Applications

View full text Add to dashboard Cite

show abstract

“…Squirrel cage induction motors are widely used in the industrial sector, due to low-cost and low maintenance requirements, high reliability and easy adaptation to various mechanical loads. However, despite their robustness and reliability, induction motors are subjected to electrical or mechanical failures, which occurs due to prolonged and severe operating conditions such as overload, overvoltage, voltage and current unbalance, among others [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Stator Winding Fault Detection Using External Search Coil and Artificial Neural Network

et al. 2020

Self Cite

View full text Add to dashboard Cite

This paper presents a methodology for winding stator fault detection of induction motors, using an external search coil, which is a noninvasive technique and can be applied during motor operation. The dispersion magnetic flux of the motor operating in abnormal conditions induces a voltage in the search coil that differs from a reference pattern corresponding to the healthy stator winding. Experimental data were obtained in a test bench using a 0.75 kW three-phase squirrel-cage induction motor with the stator winding modified to allow the introduction of short circuits. This work considered short circuits in one phase, involving 1%, 3%, 5% and 10% of the turns, with the motor loaded with a varying torque. Fault diagnosis is obtained through two models of artificial neural networks, implemented with the signals in the time domain. The obtained results demonstrated that the developed methodology presents difficulties in predicting short circuits in incipient stages, but for short circuits of higher severity, the behaviour improved substantially, being 100% successful for faults with 10% turns short-circuited.

show abstract

“…All these stresses interact with each other in such a way that to degrade the insulation system. According to [23], a stator winding defects can be classified into: short-circuit between turns of the same coil, short-circuit between coils of the same phase, short-circuit between coils of different phases, short-circuit between a phase and the earth and open-circuit in a phase. In [24], the authors state that the short-circuit between turns of the same coils in stator windings represent approximately 31 % of the reported faults.…”

Section: Introductionmentioning

confidence: 99%

Sensorless Control Technique of Open-End Winding Five Phase Induction Motor under Partial Stator Winding Short-Circuit

Khadar

Kouzou²,

Rezzaoui

et al. 2019

Period. Polytech. Elec. Eng. Comp. Sci.

View full text Add to dashboard Cite

Open-end winding induction machines are gaining more attention in the last years due to their attractive advantages in the industrial applications, where high reliability is required. However, despite their inherit robustness, they are subjected to various electrical or mechanical faults that can ultimately reduce the motor efficiency and later leads to full failure. This paper proposes a method of modeling the five phase induction machine with open end stator winding taking into consideration the short-circuit fault between turns.The fault modeling is based on the theory of electromagnetic coupling of electrical circuits. In addition, a sliding mode observer is used to estimate the speed rotor. The idea of proposed backstepping strategy is used in this paper to allow to the studied machine to continue its operating state under short circuit fault between turns. The proposed sensorless control strategy is evaluated in terms of the healthy and faulty performances through the simulation results presented in this paper. The obtained results prove that the proposed sensorless control technique allows to the open-end winding five phase induction machine to continue its operation mode under the specified fault of partial short-circuit of the stator winding. This can be a very practical situation in the industrial applications, especially in the case where the maintenance is not easy and the operation of the industrial process should not be interrupted suddenly. Keywordsopen-end winding topology, five phase induction motor, short-circuit fault between turns, sensorless control, sliding mode observer, backstepping control

show abstract

Diagnosis of Stator Faults Severity in Induction Motors Using Two Intelligent Approaches

Cited by 45 publications

References 28 publications

Study on fault diagnosis of broken rotor bars in squirrel cage induction motors: a multi‐agent system approach using intelligent classifiers

Study on fault diagnosis of broken rotor bars in squirrel cage induction motors: a multi‐agent system approach using intelligent classifiers

Stator Winding Fault Detection Using External Search Coil and Artificial Neural Network

Sensorless Control Technique of Open-End Winding Five Phase Induction Motor under Partial Stator Winding Short-Circuit

Contact Info

Product

Resources

About