In this section
@article{Zagorowska:2020:10.1016/j.apenergy.2020.114934,
author = {Zagorowska, M and Schulze, Spüntrup F and Ditlefsen, A-M and Imsland, L and Lunde, E and Thornhill, NF},
doi = {10.1016/j.apenergy.2020.114934},
journal = {Applied Energy},
pages = {114934--114934},
title = {Adaptive detection and prediction of performance degradation in off-shore turbomachinery},
url = {http://dx.doi.org/10.1016/j.apenergy.2020.114934},
volume = {268},
year = {2020}
}
TY - JOUR
AB - Performance-based maintenance of machinery relies on detection and prediction of performance degradation. Degradation indicators calculated from process measurements need to be approximated with degradation models that smooth the variations in the measurements and give predictions of future values of the indicator. Existing models for performance degradation assume that the performance monotonically decreases with time. In consequence, the models yield suboptimal performance in performance-based maintenance as they do not take into account that performance degradation can reverse itself. For instance, deposits on the blades of a turbomachine can be self-cleaning in some conditions. In this study, a data-driven algorithm is proposed that detects if the performance degradation indicator is increasing or decreasing and adapts the model accordingly. A moving window approach is combined with adaptive regression analysis of operating data to predict the expected value of the performance degradation indicator and to quantify the uncertainty of predictions. The algorithm is tested on industrial performance degradation data from two independent offshore applications, and compared with four other approaches. The parameters of the algorithm are discussed and recommendations on the optimal choices are made. The algorithm proved to be portable and the results are promising for improving performance-based maintenance.
AU - Zagorowska,M
AU - Schulze,Spüntrup F
AU - Ditlefsen,A-M
AU - Imsland,L
AU - Lunde,E
AU - Thornhill,NF
DO - 10.1016/j.apenergy.2020.114934
EP - 114934
PY - 2020///
SN - 0306-2619
SP - 114934
TI - Adaptive detection and prediction of performance degradation in off-shore turbomachinery
T2 - Applied Energy
UR - http://dx.doi.org/10.1016/j.apenergy.2020.114934
UR - https://doi.org/10.1016/j.apenergy.2020.114934
UR - http://hdl.handle.net/10044/1/79386
VL - 268
ER -
Nina Thornhill, ABB/RAEng Professor of Process Automation
Centre for Process Systems Engineering
Department of Chemical Engineering
Imperial College London
South Kensington Campus, London SW7 2AZ
Tel: +44 (0)20 7594 6622
Email: n.thornhill@imperial.ac.uk