Online Remaining Useful Lifetime Prediction Using Support Vector Regression

Martinez, Antonio Leonel Hernandez, Khursheed, Saqib, Alnuayri, Turki ORCID: 0000-0002-6884-4053 and Rossi, Daniele (2022) Online Remaining Useful Lifetime Prediction Using Support Vector Regression. IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING, 10 (3). pp. 1546-1557.

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Official URL: http://dx.doi.org/10.1109/tetc.2021.3106252

Abstract

An accurate prediction of remaining useful lifetime (RUL) in high reliability and safety electronic systems is required due to its wide use in industrial applications. In this paper, we propose a novel methodology for online RUL prediction, using support vector regression (SVR) model. Through Cadence simulations with 22nm CMOS technology library, we demonstrate that frequency degradation follows a trackable path and depends on temperature, voltage and aging. This characteristic is exploited for training the SVR model, validated over 20 years of aging degradation. Our methodology is capable of highly accurate RUL estimation, requiring a ring oscillator (RO), temperature sensor and trained SVR software model. Using a supply voltage of 0.9 V and variation in temperature from 0°C to 100°C, 13 and 21 stage RO show 90 percent cases with a RUL prediction deviation of ±0.2 years, and the remaining between ±0.75 and ±0.8 years, respectively. Furthermore, with voltage variation from 0.7 to 0.9V, with steps of 0.05V and four representative temperatures (25, 50, 75 and 100°C), the 13-RO shows 52 percent cases between ±0.2 years, 21-RO has 80.5 percent cases concentrated between ±0.2 years of RUL prediction deviation and remaining cases for both ROs are located between ±0.8 years.

Item Type:	Article
Uncontrolled Keywords:	Degradation, Aging, Semiconductor device modeling, CMOS technology, Temperature distribution, Ring oscillators, Estimation, Aging, e-waste, green ICT, IC recycling, remaining useful lifetime, machine learning, online prediction
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	23 Aug 2021 08:25
Last Modified:	17 Mar 2024 12:31
DOI:	10.1109/TETC.2021.3106252
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3134175