System Identification and Damage Assessment of Deteriorating Hysteretic Structures

Lopez, Israel; Sarigul‐Klijn, Nesrin

doi:10.2514/6.2008-1738

Cited by 5 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In previous work, an adaptive tracking technique based on a recursive parameter estimation algorithm was implemented by Lopez et al to estimate changes in the system parameters due to degradation in the health condition [17]. The method presented was capable of running as a continuous monitoring tool or as an adaptive health recognition mechanism tracking slow changes due to natural and abrupt degradation effects on the system parameters caused by spontaneous damage events.…”

Section: Design Of the Monitoring Toolmentioning

confidence: 99%

Development of Real-Time System Identification to Detect Abnormal Operations in a Gas Turbine Cycle

Bonilla-Alvarado

Bryden

Shadle

et al. 2020

Journal of Energy Resources Technology

View full text Add to dashboard Cite

This paper presents a novel online system identification methodology for monitoring the performance of power systems. This methodology was demonstrated in a gas turbine recuperated power plant designed for a hybrid configuration. A 120-kW Garrett microturbine modified to test dynamic control strategies for hybrid power systems designed at the National Energy Technology Laboratory (NETL) was used to implement and validate this online system identification methodology. The main component of this methodology consists of an empirical transfer function model implemented in parallel to the turbine speed operation and the fuel control valve, which can monitor the process response of the gas turbine system while it is operating. During fully closed-loop operations or automated control, the output of the controller, fuel valve position, and the turbine speed measurements were fed for a given period of time to a recursive algorithm that determined the transfer function parameters during the nominal condition. After the new parameters were calculated, they were fed into the transfer function model for online prediction. The turbine speed measurement was compared against the transfer function prediction, and a control logic was implemented to capture when the system operated at nominal or abnormal conditions. To validate the ability to detect abnormal conditions during dynamic operations, drifting in the performance of the gas turbine system was evaluated. A leak in the turbomachinery working fluid was emulated by bleeding 10% of the airflow from the compressor discharge to the atmosphere, and electrical load steps were performed before and after the leak. This tool could detect the leak 7 s after it had occurred, which accounted for a fuel flow increase of approximately 15.8% to maintain the same load and constant turbine speed operations.

show abstract

Section: Design Of the Monitoring Toolmentioning

confidence: 99%

Development of Real-Time System Identification to Detect Abnormal Operations in a Gas Turbine Cycle

Bonilla-Alvarado

Bryden

Shadle

et al. 2020

Journal of Energy Resources Technology

View full text Add to dashboard Cite

show abstract

“…Markou 12 summarizes the necessary principles for novelty detection approaches in terms of robustness, uniform data scaling, minimization of parameters used, capability of generalization, feature independent, adaptive (re-trainable), and minimal computational complexity. Common feature extraction methods which have been used in damage assessment studies include neural network based techniques 5 , fuzzy methods 14 , multivariate statistical techniques, such as PCA 3,4,8 , and nonlinear dimensional reduction techniques [9][10][11] . Dimensionality reduction is a key step in feature extraction and selection since it simplifies the classification level.…”

Section: Introductionmentioning

confidence: 99%

A novel dimensional reduction approach for structural damage diagnosis using feature similarity

Lopez

Sarigul‐Klijn

2009

SPIE Proceedings

View full text Add to dashboard Cite

Dimensionality reduction is an essential data preprocessing technique for feature extraction, clustering and data classification in the area of Structural Health Monitoring (SHM). This paper presents a novel data-driven model for feature extraction and its application to damage identification by means of experimental case studies. The method obtains similarity matrix indices for individual dimensional reduction techniques whereby maximum compression of information is obtained and redundancy therein is removed by creating an ensemble of these indices. A systematic comparison of this novel technique to existing linear and nonlinear dimensional reduction methods is given. First case study investigates the aeroacoustic properties of a scaled wing model with penetrating impact damage. In the experimental vibration case study, we use the response of surface mounted accelerometers to detect and quantify damage of an aluminum plate. The dimensional reduction methods will be used to improve the efficiency and effectiveness of damage classifier. In this study, damage identification performances are evaluated using a one-class k-Nearest Neighbor classifier. Classification performance is measured in terms of rate of detection and false alarm via receiver operating characteristic (ROC) curves. The robustness of the damage detection approach to uncertainty in the input data is investigated using probabilistic-based confidence bounds of prediction accuracy. Experimental results show that proposed approach yields significant reduction of false-diagnosis and increasing confidence levels in damage detection.

show abstract

Intelligent Aircraft Damage Assessment, Trajectory Planning, and Decision-Making under Uncertainty

Lopez

Sarigul‐Klijn

2009

MICAI 2009: Advances in Artificial Intelligence

View full text Add to dashboard Cite

System Identification and Damage Assessment of Deteriorating Hysteretic Structures

Cited by 5 publications

References 10 publications

Development of Real-Time System Identification to Detect Abnormal Operations in a Gas Turbine Cycle

Development of Real-Time System Identification to Detect Abnormal Operations in a Gas Turbine Cycle

A novel dimensional reduction approach for structural damage diagnosis using feature similarity

Intelligent Aircraft Damage Assessment, Trajectory Planning, and Decision-Making under Uncertainty

Contact Info

Product

Resources

About