TY - GEN
T1 - EXPERIMENTAL APPLICATION OF A METHOD FOR HIDDEN PARAMETER TRACKING IN A SLOWLY CHANGING, CHAOTIC SYSTEM
AU - Cusumano, J. P.
AU - Chelidze, D.
AU - Chatterjee, A.
N1 - Publisher Copyright:
© 1997 American Society of Mechanical Engineers (ASME). All rights reserved.
PY - 1997
Y1 - 1997
N2 - Results are presented of an experimental application of a method for tracking hidden parameters in slowly changing chaotic systems. The method exploits the time scale separation between fast dynamic variables and a slow drifting parameter. Locally linear tracking models are constructed using data from the reference system sampled on a fast time scale, employing delay coordinate embedding. These reference models are used to track parameter drift. The method is successfully applied to a forced oscillator with a two-well potential. The effect of the choice of prediction time interval is studied. It is also observed that a simple correction for estimated modeling error gives a more sensitive tracking metric. For purposes of comparison with such model-based tracking methods, a heuristic method is also presented for detecting parameter changes using the autocorrelation function of the recorded time series. The relative merits of heuristic versus model-based techniques are discussed. Directions for future work are suggested.
AB - Results are presented of an experimental application of a method for tracking hidden parameters in slowly changing chaotic systems. The method exploits the time scale separation between fast dynamic variables and a slow drifting parameter. Locally linear tracking models are constructed using data from the reference system sampled on a fast time scale, employing delay coordinate embedding. These reference models are used to track parameter drift. The method is successfully applied to a forced oscillator with a two-well potential. The effect of the choice of prediction time interval is studied. It is also observed that a simple correction for estimated modeling error gives a more sensitive tracking metric. For purposes of comparison with such model-based tracking methods, a heuristic method is also presented for detecting parameter changes using the autocorrelation function of the recorded time series. The relative merits of heuristic versus model-based techniques are discussed. Directions for future work are suggested.
UR - https://www.scopus.com/pages/publications/85210816549
UR - https://www.scopus.com/pages/publications/85210816549#tab=citedBy
U2 - 10.1115/IMECE1997-1270
DO - 10.1115/IMECE1997-1270
M3 - Conference contribution
AN - SCOPUS:85210816549
T3 - ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)
SP - 45
EP - 54
BT - Emerging Technologies for Machinery Health Monitoring and Prognosis
PB - American Society of Mechanical Engineers (ASME)
T2 - ASME 1997 International Mechanical Engineering Congress and Exposition, IMECE 1997
Y2 - 16 November 1997 through 21 November 1997
ER -