Comparison of techniques based on current signature analysis to fault detection and diagnosis in induction electrical motors

Fontes, Abrahão S.; Cardoso, Carlos Alberto Villacorta; Oliveira, Levi P. B.

doi:10.1109/eecon.2016.7830938

Cited by 15 publications

(5 citation statements)

References 17 publications

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“…The statistical distribution of these electrical and mechanical faults in induction motors has been proven by surveys undertaken by the IEEE and EPRI [18], and [19]. The statistical studies of the IEEE and EPRI of the motor faults are discussed in [20][21][22][23][24] and listed in table II.…”

Section: Fault Studymentioning

confidence: 99%

Condition-Based Health Monitoring of Electrical Machines Using DWT and LDA Classifier

Shaikh¹

2022

SSURJET

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In the industry, continuous health monitoring of electric motors is considered as an essential requirement. The continuous operation of the electric motor may cause malfunctions and addressing them timely is a critical challenge. The development of an efficient health monitoring system based on the identification of electrical motor faults is on great demand. This paper addresses the fault detection technique using discrete wavelet transform (DWT) algorithm for continuous health monitoring of electric motor-based systems. The faults have been detected through Motor Current Signature Analysis (MCSA) series procedures using the proposed method. Concurrently, the wavelet transform algorithm produces frequency-based spectrum related to the stator current parameters to accomplish the fault classification. This study provides an analysis of three motor faults of Phase imbalance, Rotor misalignment, and High contact resistance (HCR). DWT has the ability to categorize the input signals into approximate coefficient state for low frequency signals and detailed coefficient state for high frequency signals. In this research, this technique is used to detect faults because it is able of processing signals of very low frequency, and effectively deal with intermittent sharp signals that appear frequently during processing. DWT technique based on conditional monitoring of an induction motor with precise detailed coefficients and more skilled at light loads given on a motor-shaft with relatively fast execution time compared to FFT. Furthermore, the comparison of healthy and faulty induction motors has been compiled by Linear Discriminant Analysis (LDA) technique, a sub-application of MATLAB, and used for faults management purposes. LDA in comparison with PCA gives more perfect results. In this research, different faults have been detected with 100% accuracy using LDA classifier. The implementation of the proposed scheme will be beneficial in avoiding faults by ensuring that preemptive measures are taken timely against these faults, and the production of industries is protected from revenue losses.

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Section: Fault Studymentioning

confidence: 99%

Condition-Based Health Monitoring of Electrical Machines Using DWT and LDA Classifier

Shaikh¹

2022

SSURJET

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“…Based on different methods given in the literature [9]- [16], we deduce the simplest way to represent and simulate the short circuit in a stator phase. We break the proposed analytical development, into two independent circuits.…”

Section: Inter-turn Short Circuits Faultsmentioning

confidence: 99%

“…We introduced the different inductance formulas in the system equations of the induction machine as in [9], [14], [16], These equations can be solved by using fourth-order Runge-Kutta method. We take a simulated machine with 2 poles pairs, 3 phases, 50Hz and 22 rotor bars.…”

Section: Inductance Calculationsmentioning

confidence: 99%

Analytical calculation of inductances under stator inter-turn short circuits fault condition in operating squirrel cage induction motors

Touil

Babaa

Ouamane

et al. 2021

2021 9th International Conference on Systems and Control (ICSC)

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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“…The objective of this research is, therefore, to develop a technique for detecting faults that appear on operational motors. The main faults detected and analyzed during induction-motor processing are related to stator faults, broken rotor bars, and air-gap eccentricity-related faults that have been investigated and reported extensively in the literature [4][5][6][7]. The motivation of this paper is derived from Internet resources and articles exploring faults using none-ANN techniques.…”

Section: Introductionmentioning

confidence: 99%

Faults Feature Extraction Using Discrete Wavelet Transform and Artificial Neural Network for Induction Motor Availability Monitoring—Internet of Things Enabled Environment

Zuhaib¹,

Shaikh²,

Tanweer³

et al. 2022

Energies

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This paper presents the high contact resistance (HCR) and rotor bar faults by an extraction method for an induction motor using Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN). The root mean square (RMS) and mean features are obtained using DWT, and ANN is used for classification using activation functions. Activation provides output by assigning the specific input with respect to the transfer function according to the nature and type of the activation function. Method: The faulty conditions are induced using MATLAB by adopting the motor current signature analysis (MCSA) method to achieve current signature signals of the healthy and faulty motors. Results: The DWT technique has been applied to obtain fault-specific features of the average continuously varying signal (RMS) and an average of the data points (mean) at levels 5, 7, 8, and 9, followed by ANN to classify the faults for condition monitoring. Utility: The utility of the results is to reduce unscheduled downtime in the industry, thus saving revenue and reducing production losses. This work will help provide support to ensure early indication of faults in induction motors under operating conditions, enabling in-service engineers to take timely preventive measures as part of the availability of resources in IoT-enabled systems. Application: Resource availability and cybersecurity are becoming vital in an environment that supports the Internet of Things (IoT) as the essential components of Industry 4.0 scenarios. The novelty of this research lies in the implementation of high contact resistance and rotor bar faults using DWT and ANN with different activation functions to achieve accuracy up to 98%.

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Comparison of techniques based on current signature analysis to fault detection and diagnosis in induction electrical motors

Cited by 15 publications

References 17 publications

Condition-Based Health Monitoring of Electrical Machines Using DWT and LDA Classifier

Condition-Based Health Monitoring of Electrical Machines Using DWT and LDA Classifier

Analytical calculation of inductances under stator inter-turn short circuits fault condition in operating squirrel cage induction motors

Faults Feature Extraction Using Discrete Wavelet Transform and Artificial Neural Network for Induction Motor Availability Monitoring—Internet of Things Enabled Environment

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