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Prognostics of machine condition using soft computing

Authors
Journal
Robotics and Computer-Integrated Manufacturing
0736-5845
Publisher
Elsevier
Publication Date
Volume
24
Issue
6
Identifiers
DOI: 10.1016/j.rcim.2008.03.011
Keywords
  • Machine Fault Prognostics
  • Neuro-Fuzzy Systems
  • Support Vector Regression
  • Soft Computing
  • Computational Intelligence
  • Machine Learning
Disciplines
  • Computer Science
  • Logic

Abstract

Abstract This paper presents a system for monitoring and prognostics of machine conditions using soft computing (SC) techniques. The machine condition is assessed through a suitable ‘monitoring index’ extracted from the vibration signals. The progression of the monitoring index is predicted using an SC technique, namely adaptive neuro-fuzzy inference system (ANFIS). Comparison with a machine learning method, namely support vector regression (SVR), is also presented. The proposed prediction procedures have been evaluated through benchmark data sets. The prognostic effectiveness of the techniques has been illustrated through previously published data on several types of faults in machines. The performance of SVR was found to be better than ANFIS for the data sets used. The results are helpful in understanding the relationship of machine conditions, the corresponding indicating features, the level of damage/degradation and their progression.

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