Reducing the Aleatoric Uncertainties of Failure Prediction Using Singular Value Decomposition

Nor, A.K.M. and Pedapati, S.R. and Muhammad, M. (2022) Reducing the Aleatoric Uncertainties of Failure Prediction Using Singular Value Decomposition. Lecture Notes in Electrical Engineering, 758. pp. 755-774. ISSN 18761100

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Abstract

Uncertainty is a vital indicator in evaluating the prediction of real-world deep learning models. Without uncertainty, important decision-making process linked to safety, security and investment cannot be executed correctly by the users. While probabilistic deep learning methods have shown promise in quantifying prediction uncertainty, very few works in managing these uncertainties can be found in the literature. In failure prediction especially, it is favourable that the model produces the lowest level of prediction uncertainty, i.e., the highest level of confidence in order to initiate any degree of problem-solving mechanism. In this work, we present a technique to reduce Aleatoric uncertainty associated with noisy data using Singular Value Decomposition (SVD). Sensor data from industrial assets are presented in SVD matrix form and higher SVD modes more susceptible to contamination are eliminated to denoise the data. We compare the uncertainty level between the original dataset with the ones denoised by SVD and several known denoising methods in a Remaining Useful Life (RUL) prediction problem. Our results show that SVD denoising treatment outperforms the other denoising methods in reducing prediction uncertainty and improving prediction performance. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Item Type: Article
Additional Information: cited By 0; Conference of 1st International Conference on Artificial Intelligence for Smart Community, AISC 2020 ; Conference Date: 17 December 2020 Through 18 December 2020; Conference Code:286319
Uncontrolled Keywords: Decision making; Deep learning; Forecasting; Learning systems; Systems engineering, CMAPSS; Denoising methods; Failures prediction; Learning models; PHM; Prediction uncertainty; Prognostic; Real-world; Remaining useful lives; Uncertainty, Singular value decomposition
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 19 Dec 2023 03:23
Last Modified: 19 Dec 2023 03:23
URI: https://khub.utp.edu.my/scholars/id/eprint/17407

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