Anomaly Detection in Aircraft Gas Turbine Engines

Tolani, Devendra; Yasar, Murat; Ray, Asok; Yang, Vigor

doi:10.2514/1.15768

Cited by 30 publications

(13 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results are compared with those derived from traditional pattern recognition tools, such as Principal Component Analysis (PCA) and Artificial Neural Network (ANN) [5,6]. PCA has been used also for ADT in computer network security problem and the PCC method is found to be superior to the k-nearest neighbor (KNN) method, density-based local outliers (LOF) approach, and the outlier detection algorithm based on the Canberra metric [7] The authors have earlier proposed anomaly detection algorithm based on two hypothesis tests [8].…”

Section: Introductionmentioning

confidence: 99%

Real time data anomaly detection in operating engines by statistical smoothing technique

Kumar¹,

Srivastava²,

Bansal³

et al. 2012

2012 25th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)

View full text Add to dashboard Cite

Time series temperature data from an industrial steam turbine are used in the present analysis to develop methodology for anomaly detection. Simple and exponential smoothing techniques are used to study the effectiveness of the technique for prediction considering different periods for analysis. The analysis of the lags between the predicted and observed data is performed using associated parameters like average deviation, root mean square deviation and split error. Exceedance test is also applied to the data set and the results obtained are found to be consistent and satisfactory in identifying sharp anomaly in the observed real time data.

show abstract

Section: Introductionmentioning

confidence: 99%

Real time data anomaly detection in operating engines by statistical smoothing technique

Kumar¹,

Srivastava²,

Bansal³

et al. 2012

2012 25th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)

View full text Add to dashboard Cite

show abstract

“…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.

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

“…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

View full text Add to dashboard Cite

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.

show abstract

Anomaly Detection in Aircraft Gas Turbine Engines

Cited by 30 publications

References 15 publications

Real time data anomaly detection in operating engines by statistical smoothing technique

Real time data anomaly detection in operating engines by statistical smoothing technique

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

Contact Info

Product

Resources

About