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IEEE Transactions on Reliability· 2018Q1

A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings

Biao Wang, Yaguo Lei, Naipeng Li, Ningbo Li

Short summary

A novel hybrid prognostics method, combining sparse representation via relevance vector machine regression with exponential degradation models and Fréchet distance, significantly improves the accuracy and convergence of remaining useful life (RUL) predictions for rolling element bearings.

AI-generated from the title and abstract; the full text is not read.

Key points

  • Proposes a hybrid prognostics approach for RUL prediction of rolling element bearings.
  • Utilizes relevance vector machine regression for sparse representation of degradation data.
  • Employs exponential degradation models and Fréchet distance for adaptive RUL estimation.
  • Validated using vibration data from accelerated tests and PRONOSTIA datasets.
  • Demonstrates improved accuracy and convergence in RUL prediction.

AI-generated from the title and abstract; the full text is not read.

Abstract

Remaining useful life (RUL) prediction of rolling element bearings plays a pivotal role in reducing costly unplanned maintenance and increasing the reliability, availability, and safety of machines. This paper proposes a hybrid prognostics approach for RUL prediction of rolling element bearings. First, degradation data of bearings are sparsely represented using relevance vector machine regressions with different kernel parameters. Then, exponential degradation models coupled with the Fréchet distance are employed to estimate the RUL adaptively. The proposed approach is evaluated using the vibration data from accelerated degradation tests of rolling element bearings and the public PRONOSTIA bearing datasets. Experimental results demonstrate the effectiveness of the proposed approach in improving the accuracy and convergence of RUL prediction of rolling element bearings.

The authors' abstract, as published at the source. IEEE Transactions on Reliability, 2018 · DOI ↗

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Field: Control and Systems Engineering

Control and Systems EngineeringEngineering