Access Restriction

Author Nurprasetio, Pulung ♦ Bagiasna, Komang ♦ Suharto, Djoko ♦ Tjahjowidodo, Tegoeh
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Effective Time-series ♦ Fault Identification Technique ♦ Parametric-distance Method ♦ Parametric Distance ♦ Euclidean Distance ♦ Index Term Time Series Modeling ♦ Unknown Fault ♦ Common Fault ♦ Learning Stage ♦ Neighbor Classification Method ♦ Identified Time-series Model ♦ Fault Identification ♦ Simple Vibration Test Rig
Abstract Abstract — This paper presents the implementation of the combination of time-series modeling and nearest neighbor classification method in detecting common faults in rotating machineries. In this paper we propose the utilization of parametric distance as an instrument to diagnose faults. The parametric distance is defined as the Euclidean distance between the vector of parameters of an unknown fault and the vector of parameters of known faults obtained from the learning stage. Since the vectors are defined in a hyperspace spanned by the parameters of the identified time-series model, the parametric distance is definitely metric. The method has been successfully implemented in the laboratory using a simple vibration test rig. Index Terms — Time series modeling, Euclidean distance, fault identification, rotating machinery
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article