Remaining Useful Life Prediction of Rolling Element Bearings Based on Health State Assessment

Abstract

Remaining useful life (RUL) prediction is one of key technologies to realize prognostics and health management that is being widely applied in many industrial systems to ensure high system availability over their life cycles. The present work proposes a data-driven method of RUL prediction based on multiple health state assessment for rolling element bearings. Instead of finding a unique RUL prediction model, the life cycle of bearings is clustered into three health states: the normal state, the degradation state, and the failure state. A local RUL prediction model is separately built in each health state. Support vector machine is the technology to implement both health state assessment(classification) and RUL prediction modeling (regression).Experimental results on two accelerated life tests of rolling element bearings demonstrate the effectiveness of the proposed method.

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Document Details

Document Type
Technical Report
Publication Date
Oct 02, 2014
Accession Number
AD1002291

Entities

People

  • Longlong Zhang
  • Ming J. Zuo
  • Zhiliang Liu

Organizations

  • University of Electronic Science and Technology of China

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Bayesian Networks
  • Data Mining
  • Dimensionality Reduction
  • Engineering
  • Engineers
  • Failure Mode And Effect Analysis
  • Feature Selection
  • Industrial Engineering
  • Information Science
  • Kernel Functions
  • Life Tests
  • Machine Learning
  • Mechanical Engineering
  • Reliability
  • Reliability Engineering
  • Signal Processing
  • Supervised Machine Learning

Readers

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  • Tribology (the study of the boundary interaction between sliding surfaces, lubrication, wear and friction).

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms