A Framework for Novel Hybrid Prognostics System PhD,,

Cranfield University

This PhD project will focus on developing, evaluating, and demonstrating a framework of novel hybrid prognostics solution for selected system use case (e.g. clogging filter, linear actuator, lithium-ion batteries, rotating machinery, aircraft fuel system, APU, and electrical power generation system). A comprehensive test-bed for in-depth studies will be used for experiments for demonstration and evaluation. Prognostics is an essential part of condition-based maintenance (CBM), described as predicting the remaining useful life (RUL) of a system. It is also a key technology for an integrated vehicle health management (IVHM) system that leads to enhance safety, reliability, maintainability and readiness. Generally, prognostics models can be broadly categorised into experience-based models, data-driven models and physics-based models. More recently, hybrid prognostics approaches have been presented, attempting to leverage the advantages of combining the prognostics models in the aforementioned different categories for a better capability of managing the uncertainty related to system complexity and data availability to achieve more accurate RUL estimations The student will have the opportunity to work with experts in the prognostics and condition monitoring field, as well as being part of our strong and dynamic research centre at Cranfield University.