Research Assistant in Prognostics and Health Management
Singapore University of Technology and Design
Singapore

Research Assistant (Prognostics and Health Management)

Job no: 494436

Work type: Contract, full-time, Permanent, full-time

Location: Singapore

Categories: Bachelor Degree, Masters, Temasek Laboratory, Others

Prognostics and health management (PHM) is a domain that deals with the development of hardware (sensors) and software based algorithms (using physical or mathematical models) for the purpose of predicting the remaining useful life (RUL) of any component or system in operation that is subject to time based degradation (wear and tear or deterioration in performance due to physical aging) due to electro-mechanical-thermal factors in general. It involves prediction of RUL in real-time using the sensor data acquisition setup with computations that are either cloud or edge (node) based. While there are several advancements in the field of PHM for components / systems in operation (both electrical and mechanical), the prediction of RUL for components and systems in storage has been neglected or has received very less attention. In the context of defense technologies, it is all the more necessary to ensure that equipments, vehicles and associated infrastructure that are predominantly in storage are “operation ready” and “available” when the need arises unexpectedly. To ensure this, it is necessary to have the setup in place for prognosis of residual life of storage systems, which underlines the purpose of this project. While it is easy to say that existing methods of prognosis for systems in operation could just be applied straight to those in storage, this is certainly not true as the patterns of degradation are less evident, driving forces of failure and actual failure mechanism very different and available metrics for tracking much more limited (as storage systems for example are not subject to any vibrations). We hope to address these unique challenges in this project through a unique methodology that integrates data and model driven methods for RUL prediction.

The objectives of the project are to:

  • Propose a methodology for remaining useful life (RUL) prognosis that leverages on both model-driven and data-driven approaches for hybrid information fusion to enable accurate predictions (in the context of limited “signatory” data).
  • Illustrate and apply the methodology to a practical case study of vehicle systems in storage for defense application (by condition monitoring of say battery charge decay or lubrication oil wear debris collection).
  • Examine the effectiveness of the approach by comparison with off-the-shelf public data repositories that provide such data sets for batteries (NASA and CALCE, University of Maryland).

Two research assistant positions are immediately available for talented undergraduates or Master Degree graduates with a good background and passion in Statistical Modeling / AI / Machine Learning. Competitive pay package will be provided depending on the skill sets of the applicant and his qualifications. The position is open for a period of 8 months ending on March 31, 2021.

Applications close: 30 Sep 2020 Singapore Standard Time


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