Remaining Useful Lifetime Estimation of Bearings Operating Under Time-Varying Conditions

MCML Authors

Alireza Javanmardi

→ Group Eyke Hüllermeier
 Artificial Intelligence and Machine Learning

Eyke Hüllermeier

Prof. Dr.

Principal Investigator

Artificial Intelligence and Machine Learning

Abstract

This paper investigates the remaining useful lifetime (RUL) estimation of bearings under dynamic, i.e., time-varying, operating conditions (OC). Unlike conventional studies that assume constant OC in bearing accelerated life tests, we introduce a dataset with time-varying OC during run-to-failure experiments, simulating real-world scenarios. We explore data-driven approaches to identify the transition point from a healthy to an unhealthy state and estimate the RUL. Additionally, we examine strategies for integrating OC information to enhance RUL estimations. These methodologies are evaluated through numerical experiments using various machine learning algorithms.

inproceedings

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PHME 2024

8th European Conference of the Prognostics and Health Management Society 2024. Prague, Czech Republic, Jul 03-05, 2024.

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Authors

A. Javanmardi • O. K. Aimiyekagbon • A. Bender • J. K. Kimotho • W. Sextro • E. Hüllermeier

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Links

DOI

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Research Area

 A3 | Computational Models