Beschreibung
This book presents a novel hybrid prognostic methodology, integrating the short-term predictions of a physics-based model with the longer term projection of a data-driven model to enhance the prognostic accuracy, robustness, and applicability. The methodology is applied on two different engineering systems, one which represents accelerated, and the other a nominal degradation process. Clogged filter and fatigue crack propagation failure cases are selected as case studies. An experimental rig has been developed to investigate the accelerated clogging phenomena whereas a fatigue crack propagation dataset is chosen after an extensive literature search and dataset analysis. The filter clogging experimental rig is designed to obtain reproducible filter clogging data under different operational profiles. The performance of the presented methodology has been evaluated by comparing future health estimations obtained from both hybrid and individual prognostic models. The results show that the presented methodology improves accuracy, robustness and applicability. The work contained herein is therefore expected to contribute to scientific knowledge as well as industrial technology development
Autorenporträt
Dr. Eker works with Artesis. He previously worked as a post-doctorate research fellow at the Integrated Vehicle Health Management Centre funded by Boeing and Rolls Royce. He holds a PhD from School of Aerospace, Transport and Manufacturing, Cranfield University, UK. His research interests include prognostics, machine learning and big data.
Herstellerkennzeichnung:
BoD - Books on Demand
In de Tarpen 42
22848 Norderstedt
DE
E-Mail: info@bod.de
