Detection of fatigue crack anomaly: a symbolic dynamics approach

Khatkhate, Amol M; Ray, Asok; Chin, Shin; Rajagopalan, Venkatesh; Keller, Eric

doi:10.23919/acc.2004.1384494

Cited by 9 publications

(9 citation statements)

References 10 publications

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“…STSA for anomaly detection in complex systems [6] has the potential to deal with noise. Several case studies [7][8][9] 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, e.g., three-phase induction motors [10].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Methodology for Structural Health Monitoring Based on Immune Algorithms and Symbolic Time Series Analysis

Li¹,

Mita²,

Zhou³

2013

JILSA

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This hybrid methodology for structural health monitoring (SHM) is based on immune algorithms (IAs) and symbolic time series analysis (STSA). Real-valued negative selection (RNS) is used to detect damage detection and adaptive immune clonal selection algorithm (AICSA) is used to localize and quantify the damage. Data symbolization by using STSA alleviates the effects of harmful noise in raw acceleration data. This paper explains the mathematical basis of STSA and the procedure of the hybrid methodology. It also describes the results of an simulation experiment on a five-story shear frame structure that indicated the hybrid strategy can efficiently and precisely detect, localize and quantify damage to civil engineering structures in the presence of measurement noise.

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Section: Introductionmentioning

confidence: 99%

Hybrid Methodology for Structural Health Monitoring Based on Immune Algorithms and Symbolic Time Series Analysis

Li¹,

Mita²,

Zhou³

2013

JILSA

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“…In addition, symbolic time series analysis (STSA) for anomaly detection in complex systems [16] has the potential to deal with noise. 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].…”

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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“…Symbolic time series analysis (STSA) for anomaly detection in complex systems [8] has the potential to deal with noise. 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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Detection of fatigue crack anomaly: a symbolic dynamics approach

Cited by 9 publications

References 10 publications

Hybrid Methodology for Structural Health Monitoring Based on Immune Algorithms and Symbolic Time Series Analysis

Hybrid Methodology for Structural Health Monitoring Based on Immune Algorithms and Symbolic Time Series Analysis

Symbolization-based differential evolution strategy for identification of structural parameters

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

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