Prognosis of Bearing Failures Using Hidden Markov Models and the Adaptive Neuro-Fuzzy Inference System

Prognostics and health management (PHM) play a key role in increasing the reliability and safety of systems especially in key sectors (military, aeronautical, aerospace, nuclear, etc.). This paper presents a new methodology which combines data-driven and experience-based approaches for the PHM of ro...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) 2014-06, Vol.61 (6), p.2864-2874
Main Authors: Soualhi, Abdenour, Razik, Hubert, Clerc, Guy, Dinh Dong Doan
Format: Article
Language:English
Subjects:
Adaptive systems
Algorithms
Artificial intelligence
Artificial neural networks
Bearing
Bearings
Degradation
Electric power
Engineering Sciences
Failure
Feature extraction
fuzzy neural networks
Hidden Markov models
hidden Markov models (HMMs)
Mathematical models
Methodology
pattern recognition
prognosis
Prognostics and health management
Reliability
Roller bearings
Time-domain analysis
vibration analysis
Vibrations
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Prognostics and health management (PHM) play a key role in increasing the reliability and safety of systems especially in key sectors (military, aeronautical, aerospace, nuclear, etc.). This paper presents a new methodology which combines data-driven and experience-based approaches for the PHM of roller bearings. The proposed methodology uses time domain features extracted from vibration signals as health indicators. The degradation states in bearings are detected by an unsupervised classification technique called artificial ant clustering. The imminence of the next degradation state in bearings is given by hidden Markov models, and the estimation of the remaining time before the next degradation state is given by the multistep time series prediction and the adaptive neuro-fuzzy inference system. A set of experimental data collected from bearing failures is used to validate the proposed methodology. Experimental results show that the use of data-driven and experience-based approaches is a suitable strategy to improve the PHM of roller bearings.
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2013.2274415