A current monitoring system for diagnosing electrical failures in induction motors

Acosta, Gerardo; Verucchi, Carlos; Gelso, Esteban R.

doi:10.1016/j.ymssp.2004.10.001

Cited by 109 publications

(58 citation statements)

References 21 publications

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“…The faults of induction motor may not only cause the interruption of production operation but also increase costs, decrease product quality and effect safety of operators. Most common faults of induction motors are bearing failures, stator winding failures, broken rotor bar or cracked rotor end-rings and air-gap irregularities (Acosta et al, 2006). To diagnose these faults in this case-study, a combination of wavelet transform and SVM, namely W-SVM, are employed.…”

Section: Industrial Case-studiesmentioning

confidence: 99%

An intelligent condition-based maintenance platform for rotating machinery

Tran

Yang

2012

Expert Systems with Applications

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Maintenance is of necessity for sustaining machinery availability and reliability in order to ensure productivity, product quality, on-time delivery, and safe working environment. The costly maintenance strategies such as corrective maintenance and scheduled maintenance have been progressively replaced by superior maintenance strategies in which condition-based maintenance (CBM) is one of the delegates. This strategy commonly consists of sequent modules such as data acquisition, signal processing, feature extraction and feature selection, condition monitoring, etc. However, approaches in literature which have been developed for each module and implemented for different applications are standalone instead of a comprehensive system. Furthermore, these approaches have been demonstrated in a laboratory environment without any industrial validations. For these reasons, an intelligent algorithm based CBM platform is proposed in this paper to be applied for rotating machinery easily and effectively. Subsequently, two case-studies are presented in order to evaluate the effectiveness of this platform in industrial applications.

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Section: Industrial Case-studiesmentioning

confidence: 99%

An intelligent condition-based maintenance platform for rotating machinery

Tran

Yang

2012

Expert Systems with Applications

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“…When an electrical fault occurs in any of the windings it will produce an electrical unbalance whose effect is that current space vector will not be centered in origin or will loose constant modulus. The fault can be detected using the spectral analysis of the space vector modulus (Cardoso et al, 1999;Acosta et al, 2006) or pattern recognition of the space vector representation during one or several cycles (Nejjari & Benbouzid, 2000;Diallo et al, 2005). Fig.…”

Section: Diagnosis Of Ac Motors Using Space Current Vectormentioning

confidence: 99%

Monitoring and Fault Diagnosis in Manufacturing Processes in the Automotive Industry

Arnanz¹,

Santiago²,

Domínguez³

et al. 2011

New Trends and Developments in Automotive Industry

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“…Among the most common faults encountered in the area of induction motors are: the opening or shorting of one or more of the stator's phase windings [3], the presence of broken rotor bar(s) or cracked rotor's end-rings [4], air-gap irregularities [5], bearing faults, and eccentricity faults [6].…”

Section: Introductionmentioning

confidence: 99%

The Use of a Multilabel Classification Framework for the Detection of Broken Bars and Mixed Eccentricity Faults Based on the Start-Up Transient

Georgoulas

Climente-Alarcón

Antonino-Daviu

et al. 2017

IEEE Trans. Ind. Inf.

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Abstract-In this article a data driven approach for the classification of simultaneously occurring faults in an induction motor is presented. The problem is treated as a multi-label classification problem with each label corresponding to one specific fault. The faulty conditions examined, include the existence of a broken bar fault and the presence of mixed eccentricity with various degrees of static and dynamic eccentricity, while three "problem transformation" methods are tested and compared. For the feature extraction stage, the startup current is exploited using two well-known time-frequency (scale) transformations. This is the first time that a multi-label framework is used for the diagnosis of co-occurring fault conditions using information coming from the start-up current of induction motors. The efficiency of the proposed approach is validated using simulation data with promising results irrespective of the selected time-frequency transformation.

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A current monitoring system for diagnosing electrical failures in induction motors

Cited by 109 publications

References 21 publications

An intelligent condition-based maintenance platform for rotating machinery

An intelligent condition-based maintenance platform for rotating machinery

Monitoring and Fault Diagnosis in Manufacturing Processes in the Automotive Industry

The Use of a Multilabel Classification Framework for the Detection of Broken Bars and Mixed Eccentricity Faults Based on the Start-Up Transient

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