Utilizing uncertainty information in remaining useful life estimation via Bayesian neural networks and Hamiltonian Monte Carlo

被引:53
|
作者
Benker, Maximilian [1 ]
Furtner, Lukas [1 ]
Semm, Thomas [1 ]
Zaeh, Michael F. [1 ]
机构
[1] Tech Univ Munich TUM, Inst Machine Tools & Ind Management, Boltzmannstr 15, D-85748 Garching, Germany
基金
欧盟地平线“2020”;
关键词
Prognostics and health management; Bayesian neural networks; Remaining useful life; Uncertainty quantification; C-MAPSS; HEALTH PROGNOSTICS; METHODOLOGY;
D O I
10.1016/j.jmsy.2020.11.005
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The estimation of remaining useful life (RUL) of machinery is a major task in prognostics and health management (PHM). Recently, prognostic performance has been enhanced significantly due to the application of deep learning (DL) models. However, only few authors assess the uncertainty of the applied DL models and therefore can state how certain the model is about the predicted RUL values. This is especially critical in applications, in which unplanned failures lead to high costs or even to human harm. Therefore, the determination of the uncertainty associated with the RUL estimate is important for the applicability of DL models in practice. In this article, Bayesian DL models, that naturally quantify uncertainty, were applied to the task of RUL estimation of simulated turbo fan engines. Inference is carried out via Hamiltonian Monte Carlo (HMC) and variational inference (VI). The experiments show, that the performance of Bayesian DL models is similar and in many cases even beneficial compared to classical DL models. Furthermore, an approach for utilizing the uncertainty information generated by Bayesian DL models is presented. The approach was applied and showed how to further enhance the predictive performance.
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页码:799 / 807
页数:9
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