Similarity-Based Remaining Useful Lifetime Prediction Method Considering Epistemic Uncertainty

Author:

Wu Wenbo123,Zou Tianji123ORCID,Zhang Lu123,Wang Ke123ORCID,Li Xuzhi123

Affiliation:

1. University of Chinese Academy of Sciences, Beijing 101408, China

2. Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing 100094, China

3. The Key Laboratory of Space Utilization, Beijing 10094, China

Abstract

Measuring the similarity between two trajectories is fundamental and essential for the similarity-based remaining useful life (RUL) prediction. Most previous methods do not adequately account for the epistemic uncertainty caused by asynchronous sampling, while others have strong assumption constraints, such as limiting the positional deviation of sampling points to a fixed threshold, which biases the results considerably. To address the issue, an uncertain ellipse model based on the uncertain theory is proposed to model the location of sampling points as an observation drawn from an uncertain distribution. Based on this, we propose a novel and effective similarity measure metric for any two degradation trajectories. Then, the Stacked Denoising Autoencoder (SDA) model is proposed for RUL prediction, in which the models can be first trained on the most similar degradation data and then fine-tuned by the target dataset. Experimental results show that the predictive performance of the new method is superior to prior methods based on edit distance on real sequence (EDR), longest common subsequence (LCSS), or dynamic time warping (DTW) and is more robust at different sampling rates.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

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