Journal of Mechanical Science and Technology

, Volume 28, Issue 1, pp 61–71

A machine learning approach for the condition monitoring of rotating machinery

  • Dimitrios Kateris
  • Dimitrios Moshou
  • Xanthoula-Eirini Pantazi
  • Ioannis Gravalos
  • Nader Sawalhi
  • Spiros Loutridis
Article

DOI: 10.1007/s12206-013-1102-y

Cite this article as:
Kateris, D., Moshou, D., Pantazi, XE. et al. J Mech Sci Technol (2014) 28: 61. doi:10.1007/s12206-013-1102-y

Abstract

Rotating machinery breakdowns are most commonly caused by failures in bearing subsystems. Consequently, condition monitoring of such subsystems could increase reliability of machines that are carrying out field operations. Recently, research has focused on the implementation of vibration signals analysis for health status diagnosis in bearings systems considering the use of acceleration measurements. Informative features sensitive to specific bearing faults and fault locations were constructed by using advanced signal processing techniques which enable the accurate discrimination of faults based on their location. In this paper, the architecture of a diagnostic system for extended faults in bearings based on neural networks is presented. The multilayer perceptron (MLP) with Bayesian automatic relevance determination has been applied in the classification of accelerometer data. New features like the line integral and feature based sensor fusion are introduced which enhance the fault identification performance. Vibration feature selection based on Bayesian automatic relevance determination is introduced for finding better feature combinations.

Keywords

Condition monitoring Vibrations Neural networks Sensor fusion Feature selection 

Copyright information

© The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Dimitrios Kateris
    • 1
  • Dimitrios Moshou
    • 1
  • Xanthoula-Eirini Pantazi
    • 1
  • Ioannis Gravalos
    • 2
  • Nader Sawalhi
    • 3
  • Spiros Loutridis
    • 4
  1. 1.Agricultural Engineering Laboratory, School of AgricultureAristotle UniversityThessalonikiGreece
  2. 2.Department of Biosystems Engineering, School of Agricultural TechnologyTechnological Educational Institute of LarissaLarissaGreece
  3. 3.School of Mechanical EngineeringPrince Mohammad Bin Fahd UniversityAl KhobarKingdom of Saudi Arabia
  4. 4.Department of Electrical Engineering, School of Technological ApplicationsTechnological Educational Institute of LarissaLarissaGreece

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