Abstract:
This thesis presents a novel hybrid prognostic methodology, integrating
physics-based and data-driven prognostic models, to enhance the prognostic
accuracy, robustness, and applicability. The presented prognostic
methodology integrates the short-term predictions of a physics-based model
with the longer term projection of a similarity-based data-driven model, to
obtain remaining useful life estimations. The hybrid prognostic methodology
has been applied on specific components of 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 the publicly available Virkler
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. This data is thought to be a good benchmark dataset for prognostic
models.
The performance of the presented methodology has been evaluated by
comparing remaining useful life estimations obtained from both hybrid and
individual prognostic models. This comparison has been based on the most
recent prognostic evaluation metrics. 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.