IMS Researcher Presents at the 2021 Annual Conference of the PHM Society
IMS Center PhD student and Graduate Research Assistant Wenzhe Li recently presented at the 13th Annual Conference of the Prognostics and Health Management Society, which was held virtually this past November 29th through December 3rd, 2021. This presentation was based on a paper composed by Wenzhe Li, Dr. Xiaodong Jia, Yuan-Ming Hsu, Youwen Liu, and Professor Jay Lee titled Methodology on Establishing Multivariate Failure Thresholds for Improved Remaining Useful Life Prediction in PHM. Mr. Li's presentation was delivered on December 1st, 2021 at 1:30 PM during paper session #11.
For more information about this paper, please see the abstract below.
Abstract
Prognostic approaches commonly try to predict the Remaining Useful Life (RUL) based on machine health status by either directly establish a mapping or setting up a failure threshold to determine the End-of-Life (EoL). On the one hand, determining a failure threshold is crucial but subjective for most reported cases. Machine operation risks, which are intuitional but difficult to quantify, can be used to bridge the gap between prediction and determining a multivariate failure threshold. On the other hand, historical machine life information is rarely considered together with the condition indicators for such prognostic tasks. Building multivariate failure thresholds based on quantifiable operation risks for prognostic tasks is the general topic that is rarely studied due to the following challenges: 1) How to quantify operation risks under multiple variables? 2) How to determine the multivariate failure thresholds? 3) How to make reliable extrapolations of future conditions? To address these questions, as the extension of our previous work (Jia, Li, Wang, Li, & Lee, 2020), this paper proposes 1) a Gaussian Copula model-based risk quantification method to determine multivariate failure thresholds, and 2) a Similarity enhanced Blackwellized Particle Filter (RBPF) to predict future system conditions. Two examples of establishing tri-variate and bi-variate failure thresholds are given. The proposed methodology is validated on the aero-engine RUL prediction task based on the C-MAPSS dataset from the PHM society data competition 2008. The result suggests that the proposed methodology has better explainability and practicability with comparable prediction capability.
Benefits & Impacts
The presented methodology provides a reliable decision-making mechanism for prognostics based on quantified operation risks. The method presented in this paper has great potential for improving prognostic accuracy when setting thresholds for what constitutes a failure . This critical step in determining the remaining useful life of a machine is often left to subjective opinion or incomplete historical information. This method can be used for multivariate risk quantification, based on which establishing reliable multivariate thresholds, improving prediction, assessing remaining useful life, and ensuring actionable results.
To learn more about this event and to see the full schedule, please visit the PHM Society website here.
