Prognostics of machine condition using soft computing

Authors:
B. Samanta;C. Nataraj
Affiliations:
Department of Mechanical Engineering, Villanova University, Villanova, PA 19085, USA;Department of Mechanical Engineering, Villanova University, Villanova, PA 19085, USA
Venue:
Robotics and Computer-Integrated Manufacturing
Year:
2008

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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.