Early detection of voltage imbalances in three-phase induction motors

Samsi, Rohan; Rajagopalan, Venkatesh; Mayer, Jeffrey S.; Ray, Asok

doi:10.1109/acc.2005.1469981

Cited by 5 publications

(6 citation statements)

References 18 publications

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“…Choosing α's based on the fault process information gained in the off-line analysis and using Eqn(2) helps into zooming into regions of interest [10]. Due to the nonlocalized nature of the fault, it is not possible to cluster the fault information into one particular scale.…”

Section: A Selection Of Wavelet and Wavelet Scalesmentioning

confidence: 99%

Broken Rotor Bar detection using Symbolic Wavelet Analysis

Samsi¹,

Mayer²,

Ray³

2006

Proceedings of the 45th IEEE Conference on Decision and Control

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Section: A Selection Of Wavelet and Wavelet Scalesmentioning

confidence: 99%

Broken Rotor Bar detection using Symbolic Wavelet Analysis

Samsi¹,

Mayer²,

Ray³

2006

Proceedings of the 45th IEEE Conference on Decision and Control

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“…From Equations (11) to (13), it is evident that as more story outputs are obtained, the more accurate the identification results will be.…”

Section: Guideline For Parameter Selectionmentioning

confidence: 99%

“…Several case studies [9][10][11] have shown that STSA is more effective at anomaly detection than pattern recognition techniques such as principal component analysis and neural networks. STSA has also been used for fault detection in electromechanical systems, such as in three-phase induction motors [12] and helical gearboxes in rotorcraft [13].…”

Section: Introductionmentioning

confidence: 99%

Feasibility Study of Parameter Identification Method Based on Symbolic Time Series Analysis and Adaptive Immune Clonal Selection Algorithm

Li¹,

Mita²,

Zhou³

2012

OJCE

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The feasibility of a parameter identification method based on symbolic time series analysis (STSA) and the adaptive immune clonal selection algorithm (AICSA) is studied. Data symbolization by using STSA alleviates the effects of harmful noise in raw acceleration data. The effect of the parameters in STSA is theoretically evaluated and numerically verified. AICSA is employed to minimize the error between the state sequence histogram (SSH) that is transformed from raw acceleration data by STSA. The proposed methodology is evaluated by comparing it with AICSA using raw acceleration data. AICSA combining STSA is proved to be a powerful tool for identifying unknown parameters of structural systems even when the data is contaminated with relatively large amounts of noise.

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“…Several case studies [17][18][19] have shown that STSA is more effective at anomaly detection than pattern recognition techniques such as principal component analysis and neural networks. STSA has also been used for fault detection in electromechanical systems, such as in three-phase induction motors [20] and helical gearboxes in rotorcraft [21]. The method that we have developed transforms time series acceleration data into symbolic data series.…”

Section: Introductionmentioning

confidence: 99%

Symbolization-based differential evolution strategy for identification of structural parameters

Mita

Zhou

2012

Struct. Control Health Monit.

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SUMMARY This new method of identifying structural parameters, called ‘symbolization‐based differential evolution strategy’ (SDES), merges the advantages of symbolic time series analysis and differential evolution (DE). Data symbolization in SDES alleviates the effects of harmful noise. SDES was numerically compared with particle swarm optimization and DE on raw acceleration data. These simulations revealed that SDES provided better estimates of structural parameters when the data were contaminated by noise. We applied SDES to experimental data to assess its feasibility in realistic problems. SDES performed much better than particle swarm optimization and DE on raw acceleration data. The simulations and experiments show that SDES is a powerful tool for identifying unknown parameters of structural systems even when the data are contaminated with relatively large amounts of noise. Copyright © 2012 John Wiley & Sons, Ltd.

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Early detection of voltage imbalances in three-phase induction motors

Cited by 5 publications

References 18 publications

Broken Rotor Bar detection using Symbolic Wavelet Analysis

Broken Rotor Bar detection using Symbolic Wavelet Analysis

Feasibility Study of Parameter Identification Method Based on Symbolic Time Series Analysis and Adaptive Immune Clonal Selection Algorithm

Symbolization-based differential evolution strategy for identification of structural parameters

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