Stator Current Demodulation Using Hilbert Transform for Inverter-Fed Induction Motor at Low Load Conditions

Khelfi, Hamid; Hamdani, S.; Nacereddine, K.; Chibani, Youcef

doi:10.1109/cistem.2018.8613439

Cited by 12 publications

(2 citation statements)

References 9 publications

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“…In their study, the authors employed HT to extract fault signatures at full load and no load conditions to develop a novel technique fort multiple broken bar fault locations in induction motors. In [15], the authors presented an offline framework for rotor fault detection in an IM using HT in light load conditions. The author's suggested methodology was based on an examination of the frequency domain of the HT-extracted stator current envelope.…”

Section: Motivation and Literature Reviewmentioning

confidence: 99%

A Filter-Based Feature-Engineering-Assisted SVC Fault Classification for SCIM at Minor-Load Conditions

Okwuosa

Hur

2022

Energies

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In most manufacturing industries, squirrel cage induction motors (SCIMs) are essential due to their robust nature, high torque generation, and low maintenance costs, so their failure often times affects productivity, profitability, reliability, etc. While various research studies presented techniques for addressing most of these machines’ prevailing issues, fault detection in cases of low slip or, low load, and no loading conditions for motor current signature analysis still remains a great concern. When compared to the impact on the machine at full load conditions, fault detection at low load conditions helps mitigate the impact of the damage on SCIM and reduces maintenance costs. Using stator current data from the SCIM’s direct online starter method, this study presents a feature engineering-aided fault classification method for SCIM at minor-load conditions based on a filter approach using the support vector classification (SVC) algorithm as the classifier. This method leverages the loop-hole of the Fourier Transform at minor-load conditions by harnessing the uniqueness of the Hilbert Transform (HT) to present a methodology that combines different feature engineering technologies to excite, extract, and select 10 discriminant information using a filter-based approach as the selection tool for fault classification. With the selected features, the SVC performed exceptionally well, with a significant diagnostic performance accuracy of 97.32%. Further testing with other well-known robust classifiers such as decision tree (DT), random forest (RF), k-nearest neighbor (KNN), gradient boost classifier (GBC), stochastic gradient descent (SGD), and global assessment metrics revealed that the SVC is reliable in terms of accuracy and computation speeds.

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Section: Motivation and Literature Reviewmentioning

confidence: 99%

A Filter-Based Feature-Engineering-Assisted SVC Fault Classification for SCIM at Minor-Load Conditions

Okwuosa

Hur

2022

Energies

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“…Várias pesquisas são feitas usando estas ferramentas para a detecção de barras quebradas, como por exemplo, em (Laala et al, 2011), é desenvolvido um método para analisar a corrente espectral do estator e assim detectar a falha. Já em (Khelfi et al, 2019) é estudado um método de análise de falhas através do envelope da corrente usando a ferramenta HT, que será também o foco deste artigo.…”

Section: Introductionunclassified

Analise da Transformada de Hilbert em baixo escorregamento

Araújo

Oliveira

Moreira

et al. 2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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The three-phase induction motors have a significant presence in the industrial sector, mainly due to the versatility, low cost and robustness. Electrical machines are subject to failures that depending on the type and importance of the factory can cause material, economic and social damages. The reliability required for electric motors is constantly increasing due to the importance of its applications and the technological advance. This work presents an analysis of the characteristic modulations of the fault of broken bars using a traditional methodology of signal processing known as Hilbert Transform (HT). The algorithm was implemented using Matlab TM software, where the results were analyzed. The obtained results showed this technique as a possible alternative for the detection of broken bars in the induction motor, avoiding invasive tests in the machine, and can be done even with the engine running. However, the statistical analysis of the results point to a particular condition of difficult identification of failures. Resumo: Os motores de indução trifásicos possuem uma presença significativa no setor industrial, principalmente devido a sua versatilidade, baixo custo e robustez. As máquinas elétricas estão sujeitas a falhas que, dependendo do tipo e da sua importância na planta fabril, podem causar prejuízos materiais, econômicos e sociais. Observa-se que a confiabilidade exigida para os motores elétricos cresce constantemente devido à importância de suas aplicações e do avanço tecnológico. Neste contexto, o presente trabalho traz uma análise das modulações características da falha de barras quebradas utilizando uma tradicional metodologia de processamento de sinais conhecida como Transformada de Hilbert (HT). O algoritmo foi implementado utilizando o software Matlab TM , onde também foi realizada as análises dos resultados. Os resultados obtidos mostraram essa técnica como uma possível alternativa para detecção de barra quebrada no motor de indução, evitando testes invasivos na máquina, podendo ser feitos ainda com o motor em funcionamento. No entanto, a análise estatística dos resultados, aponta para uma condição em particular de difícil identificação de falhas.

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Application of Temporal Envelope Extracted by Square Root of the Three-Phase Currents for Dynamic Eccentricity Fault Diagnosis Within Inverter-Fed Induction Machine

Khelfi,

Hamouda,

Hamdani

2024

Lecture Notes in Electrical Engineering

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Stator Current Demodulation Using Hilbert Transform for Inverter-Fed Induction Motor at Low Load Conditions

Cited by 12 publications

References 9 publications

A Filter-Based Feature-Engineering-Assisted SVC Fault Classification for SCIM at Minor-Load Conditions

A Filter-Based Feature-Engineering-Assisted SVC Fault Classification for SCIM at Minor-Load Conditions

Analise da Transformada de Hilbert em baixo escorregamento

Application of Temporal Envelope Extracted by Square Root of the Three-Phase Currents for Dynamic Eccentricity Fault Diagnosis Within Inverter-Fed Induction Machine

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