IMS Researcher Presenting at the 2020 Annual Conference of the PHM Society

IMS Center PhD student and Graduate Research Assistant Wenzhe Li will present at the upcoming 2020 Annual Conference of the Prognostics and Health Management Society, which will be held virtually on November 9th-13th, 2020. This presentation will be based on a paper composed by Li and Dr. Xiaodong Jia, Dr. Wei Wang, Dr. Xiang Li, and Professor Jay Lee titled Development of Multivariate Failure Threshold with Quantifiable Operation Risks in Machine Prognostics. Mr. Li's presentation will be delivered on November 11th, 2020 at 2:00 PM during the session on Prognostics - Methods and Algorithms.

For more information about this paper, please see the abstract below.


Prognostics and Health Management (PHM) is attracting the attention from both academia and industry due to its great potential to enhance the resilience and responsiveness of the equipment to the potential operation risks. In literature, many methodologies are proposed to predict the Remaining Useful Life (RUL) of the equipment. However, there are two major challenges that limit the practicality of these methodologies. 1) How to generate a quantifiable Health Indicator (HI) to represent the operation risks? 2) How to define a reasonable failure threshold to predict RUL? To answer these two questions, this paper proposes a novel methodology for failure threshold determination with quantifiable operation risk in machine prognostics. In the proposed methodology, Fisher distance and Mann-Kendall (MK) test are firstly used to extract useful sensors based on which HI is estimated by applying Principle Component Analysis (PCA). Then, Rao-Blackwellized Particle Filter (RBPF) is employed to obtain the HI prediction and the uncertainties. Afterwards, a Bivariate-Weibull-distribution-based risk quantification model is designed to quantify the cumulative risk over time and over the increase of HI. The failure threshold, which is the ending point of the RUL, varies over different users and applications depending on the level of risk they want to tolerate. The validation of the methodology is based on the C-MAPSS data from the PHM data competition 2008 hosted by PHM society. The results validate the effectiveness of the proposed risk quantification method and its potential application on machine prognostics.

To learn more about this event and to see the full schedule, please visit the PHM Society website here.


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Professor Jay Lee
Wenzhe Li
Dr. Xiaodong Jia
Xiang Li.jpg
Dr. Xiang Li