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- 저자 : Dimitrios Kateris (검색결과 1 건)
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A machine learning approach for the condition monitoring of rotating machinery†
Dimitrios Kateris,Dimitrios Moshou,Xanthoula-Eirini Pantazi,Ioannis Gravalos,Nader Sawalhi,Spiros Loutridis 대한기계학회 2014 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.28 No.1
Rotating machinery breakdowns are most commonly caused by failures in bearing subsystems. Consequently, condition monitoring ofsuch subsystems could increase reliability of machines that are carrying out field operations. Recently, research has focused on the implementationof 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 processingtechniques which enable the accurate discrimination of faults based on their location. In this paper, the architecture of a diagnostic systemfor extended faults in bearings based on neural networks is presented. The multilayer perceptron (MLP) with Bayesian automatic relevancedetermination has been applied in the classification of accelerometer data. New features like the line integral and feature basedsensor fusion are introduced which enhance the fault identification performance. Vibration feature selection based on Bayesian automaticrelevance determination is introduced for finding better feature combinations.
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