Abstract
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.
Highlights
Remaining Useful Life (RUL) prediction has been extensively studied in the literature, and many different methodologies are proposed to fulfill the prediction task
To address these challenges and expand the horizon of failure threshold (FT) determination in machine prognostics, this study proposes a risk quantification methodology to assist the optimization of FT
The proposed methodology is illustrated in the aero-engine RUL prediction problem using C-MAPSS dataset
Summary
Remaining Useful Life (RUL) prediction has been extensively studied in the literature, and many different methodologies are proposed to fulfill the prediction task. In multivariate survival analysis applications, improper dependency measures can render the life indicator over-dominant in the model. To address this challenge, this study proposes a risk quantification model to assist the optimization of failure threshold (FT). Based on the proposed risk model, a systematic methodology for RUL prediction is proposed, and the effects of varying tolerable risk on the predictive distribution RUL are studied and discussed based on the well-known C-MAPSS data about aero-engines degradation (Jia, Huang, Feng, Cai, & Lee, 2018).
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