Incipient fault detection in mechanical power transmission systems

Bhatnagar, Sankalp; Rajagopalan, Venkatesh; Ray, Asok

doi:10.1109/acc.2005.1469980

Cited by 4 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several case studies [2][3][4][5] in anomaly detection show that STSA can be more effective than existing pattern recognition techniques (e.g., principal component analysis and neural networks). The STSA method has also been demonstrated for fault detection in electromechanical systems, such as three-phase induction motors [6] and helical gearbox in rotorcraft [7].…”

Section: Introductionmentioning

confidence: 99%

Symbolic time series analysis via wavelet-based partitioning

2006

View full text Add to dashboard Cite

Symbolic time series analysis (STSA) of complex systems for anomaly detection has been recently introduced in literature. An important feature of the STSA method is extraction of relevant information, imbedded in the measured time series data, to generate symbol sequences. This paper presents a wavelet-based partitioning approach for symbol generation, instead of the currently practiced method of phase-space partitioning. Various aspects of the proposed technique, such as wavelet selection, noise mitigation, and robustness to spurious disturbances, are discussed. The waveletbased partitioning in STSA is experimentally validated on laboratory apparatuses for anomaly/damage detection. Its efficacy is investigated by comparison with phase-space partitioning.

show abstract

Section: Introductionmentioning

confidence: 99%

Symbolic time series analysis via wavelet-based partitioning

2006

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…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.

View full text Add to dashboard Cite

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.

show abstract

Incipient fault detection in mechanical power transmission systems

Cited by 4 publications

References 7 publications

Symbolic time series analysis via wavelet-based partitioning

Symbolic time series analysis via wavelet-based partitioning

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

Contact Info

Product

Resources

About