A Systematic Review of Fault Detection and Diagnosis Methods for Induction Motors

Maciejewski, Narco Afonso Ravazzoli; Treml, Aline; Flauzino, Rogério Andrade

doi:10.1109/fortei-icee50915.2020.9249890

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…State-of-art works: Multiple machine condition-monitoring strategies have already been investigated in the literature [9,10], including partial discharge, vibration analysis, current signature investigation, magnetic flux, and acoustic noise monitoring. However, the Motor Current Signature Analysis (MCSA) is considered to be the most commonly reported technique applied to fault analysis [11,12].…”

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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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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“…Many field have demonstrated interest in ML and AI in order to improve their maintenance strategies: Rogers et al (Rogers, Guo, & Rasmussen, 2019) offer a review of Fault Detection and Diagnosis (FDD) methods for residential air conditioning systems, Datta et al (Datta & Sarkar, 2016) report different pipeline fault detection methods, Meng et al (Meng & Li, 2019) investigate Prognostics and Health Management (PHM) methods of lithium-ion batteries, Maciejewski et al (Maciejewski, Treml, & Flauzino, 2020) deal with fault detection and diagnosis methods for induction motors, Li et al (Li, Delpha, Diallo, & Migan-Dubois, 2020) study the application of Artificial Neural Networks (ANN) to to photovoltaic FDD, Liu et al (Liu, Yang, Zio, & Chen, 2018) face another blooming subject for AI that is fault detection in rotating machinery while Kumar (Kumar, 2018) takes into account fault detection in a more specific context (bearings and gears), Shi et al (Shi & O'Brien, 2019) give a comprehensive overview of automated FDD in buildings while Mirnaghi et al (Mirnaghi & Haghighat, 2020) focus on large-scale HVAC, Gururajapathy et al review fault location and detection in power distribution systems and, in the end, Habibi et al (Habibi, Howard, & Simani, 2019). are interested in fault detection techniques for wind turbine power generation.…”

Section: Introductionmentioning

confidence: 99%

Wrong Injection Detection in a Small Diesel Engine, a Machine Learning Approach

2022

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In the last ten years, Machine Learning (ML) and Artificial Intelligence (AI) have overwhelmed every engineering research branch finding a broad variety of applications; anomaly detection and anomaly classification are two of the topics that have benefited mostly by data-driven methods’ insights. On the other side, in the small diesel engine domain, the current trend is to lean on traditional anomaly detection/classification procedures and do not foster the use of AI. The goal of this work is to detect anomalies in the in-cylinders injectors of a small diesel engine as soon as a wrong quantity of fuel is inputted into one or more cylinders by means of ML approaches. Part of the analysis aim to understand which measurements are the most relevant for the detection and to compare different techniques to select the most suitable one. Furthermore, a condition-based methodology for maintenance is proposed. After a brief review of the state-of-the-art, the case study scenario is presented grouping sensors accordingly to their degree of accessibility; then, the implemented techniques are explained, and results are discussed.

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“…Da mesma forma, os métodos de diagnóstico no estado estacionário geralmente são baseados em sistemas inteligentes, combinando técnicas de seleção de atributos e aprendizado de máquina para identificar a evolução dos componentes de falha. Para tanto, os atributos extraídos dos sinais constituem a etapa fundamental do diagnóstico de defeitos no regime transiente de operação (PONS-LLINARES et al, 2016;MACIEJEWSKI;TREML;FLAUZINO, 2020). Como a detecção e o diagnóstico de defeitos ainda no regime transiente de operação estão sujeitos a diversos fatores aleatórios, como ruído, as técnicas utilizadas devem ser robustas ao ruído, reduzindo a taxa de erros e falsos alarmes (ROCHA et al, 2020; MALEK; ABDELSALAM; HASSAN, 2017).…”

Section: Introductionunclassified

Detecção e diagnóstico de defeitos no regime transitório de motores de indução baseado em sistemas inteligentes

Maciejewski¹

Self Cite

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Desafio tão grande quanto escrever esta tese de doutorado, foi utilizar algumas poucas frases para agradecer as pessoas que fizeram parte desta minha trajetória.Na minha primeira viagem a Foz do Iguaçu no ano de 2012, na procura de um apartamento, que conheci meu grande amigo, companheiro e colega de república Jefferson Tales Oliva, que além de nossos pais serem grandes amigos, ainda não o conhecia. Agradeço pela amizade, parceiria e conversas que me abriram vários horizontes.Agradeço aos meus amigos de infância, Lucas Fávero e Matheus Boaretto. Agradeço pela amizade, carinho e pelos momentos de descontração que até hoje são presentes.

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A Systematic Review of Fault Detection and Diagnosis Methods for Induction Motors

Cited by 5 publications

References 14 publications

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

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

Wrong Injection Detection in a Small Diesel Engine, a Machine Learning Approach

Detecção e diagnóstico de defeitos no regime transitório de motores de indução baseado em sistemas inteligentes

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