A review on location, detection and fault diagnosis in induction machines

Hurtado, ZulmaYadira Medrano; Tello, Carlos Pérez; Sarduy, Julio R. Gómez

doi:10.25103/jestr.083.25

Cited by 6 publications

(2 citation statements)

References 31 publications

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“…Review papers presented in Refs. [158][159][160][161][162][163] on the condition monitoring techniques for the induction motors demonstrate that extensive research has been carried out in the last decade on the development of a non-invasive condition monitoring system for diagnosis of gear defects and bearing localized faults. It has been found that the mechanical vibration produce characteristic frequencies in stator current spectrum.…”

Section: Condition Monitoring Issuesmentioning

confidence: 99%

Fault Diagnosis and Detection

Mahmoud

Xia

2013

Analysis and Synthesis of Fault‐Tolerant Control Systems

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Here he has been involved in research on structural health monitoring and energy harvesting. He currently works at the Department of Mechatronics Engineering, Marmara University, as an Assoc. Prof. Dr. His research interests are fault diagnosis, energy harvesting, intelligent materials, automation systems, structural health monitoring, and artificial intelligence systems. He is the director of Fault Diagnosis and Structural Health Monitoring Lab in Marmara University. He is an editorial board member at many international journals. He has publications in many international journals and symposiums on failure diagnosis and fault detection.Dr. Muhammet Ünal works as an assistant professor in the Department of Mechatronics Engineering, Marmara University, Istanbul (Turkey). His research interests are system identification, parameter optimization, real-time control, and structural health monitoring. His primary research interests are vibration analysis and fault diagnosis of mechanical systems. Dr. Ünal has written several papers in vrious fields of mechatronics engineering and one book on control engineering. He is also the first author of the book Optimization of PID Controllers Using Ant Colony and Genetic Algorithms, which is published in Springer.

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Section: Condition Monitoring Issuesmentioning

confidence: 99%

Fault Diagnosis and Detection

Mahmoud

Xia

2013

Analysis and Synthesis of Fault‐Tolerant Control Systems

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“…This technique was named motor current analysis (MCA). 30–34 Several papers have been published in the past decade to improve the performance of the MCA by developing pre-processing algorithms to make the condition monitoring system reliable, efficient, economical, and to reduce the complexity. 35–38 Although MCA is a better alternative to intrusive techniques but harmonics associated with defects are suppressed by the amplitude of line frequency and give a false alarm.…”

Section: Introductionmentioning

confidence: 99%

Condition monitoring of water pump bearings using ensemble classifier

Irfan

Althobiani

Alwadie

et al. 2022

Advances in Mechanical Engineering

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The bearings faults are reported to be the major reason for centrifugal pump (CPs) failures. Limited literature is available to diagnose the minor scratches in the bearing surface through non-intrusive condition monitoring techniques. Recent research on the analysis of bearing scratches through non-intrusive motor current analysis (MCA) has shown encouraging results where the comparison of machine learning and convolutional neural networks (CNNs) was performed in the classification of healthy bearings and faulty bearings (holes and scratches). The fault classification accuracy of 89.26% through MCA combination with machine learning and CNN algorithm was reported which is very low. The key factors of low accuracies were identified as low amplitudes of the harmonics in the MCA spectrum, the magnitude of environmental noise, and utilization of conventional feature extraction techniques. This problem has been tackled in this paper by developing a novel feature extractor (NFE) that extracts powerful features from the integrated current and voltage sensors data. The NFE has been derived using the threshold-based decision mechanism which has the capability to identify the location of the feature harmonic, feature extraction, measure the amplitude of the fault component, and compare it with the derived threshold. The experimental data has been collected for the bearing balls (BB), bearing cage (BC), inner race (IR) and the outer race (OR) faults, and the performance of the NFE has been tested on an ensemble classifier (CatBoost) and the better classification accuracy (99.2% for an individual feature and 100% with the combination of two or more features) of NFE has been achieved as compared to previously reported methods.

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Effects of induction motor end ring faults on motor performance. Experimental results

Yetgin

2019

Engineering Failure Analysis

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A review on location, detection and fault diagnosis in induction machines

Cited by 6 publications

References 31 publications

Fault Diagnosis and Detection

Fault Diagnosis and Detection

Condition monitoring of water pump bearings using ensemble classifier

Effects of induction motor end ring faults on motor performance. Experimental results

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