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Data-driven approach to machine condition prognosis using least square regression trees

Tran, Van Tung (2007) Data-driven approach to machine condition prognosis using least square regression trees. In: The KSNVE Annual Autumn Conference, 2007, Korea.

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Machine fault prognosis techniques have been considered profoundly in the recent time due to their profit for reducing unexpected faults or unscheduled maintenance. With those techniques, the working conditions of components, the trending of fault propagation, and the time-to-failure are forecasted precisely before they reach the failure thresholds. In this work, we propose an approach of Least Square Regression Tree (LSRT), which is an extension of the Classification and Regression Tree (CART), in association with one-step-ahead prediction of time-series forecasting technique to predict the future conditions of machines. In this technique, the number of available observations is firstly determined by using Cao’s method and LSRT is employed as prognosis system in the next step. The proposed approach is evaluated by real data of low methane compressor. Furthermore, the comparison between the predicted results of
CART and LSRT are carried out to prove the accuracy. The predicted results show that LSRT offers a potential for
machine condition prognosis.

Item Type: Conference or Workshop Item (Paper)
Subjects: T Technology > TJ Mechanical engineering and machinery
Schools: School of Computing and Engineering > Diagnostic Engineering Research Centre
School of Computing and Engineering
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Depositing User: Van Tran
Date Deposited: 07 Feb 2013 13:41
Last Modified: 05 Nov 2015 08:41


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