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A Classification and Regression Trees (CART) Model of Parallel Structure and Long-term Prediction Prognosis of Machine Condition

Author(s):


Medium: journal article
Language(s): English
Published in: Structural Health Monitoring, , n. 2, v. 9
Page(s): 121-132
DOI: 10.1177/1475921709352148
Abstract:

This article presents a combined prediction model involving the parallel of classification and regression trees (CART) model, namely p-CART, and a long-term direct prediction methodology of time series techniques to predict the future stages of the machine’s operating conditions. p-CART model consists of multiple CART models which are connected in parallel. Each sub-model in the p-CART is trained independently. Based on the observations, these sub-models are subsequently used to predict the future values of the machine’s operating conditions separately with the same embedding dimension but different observations’ indices. Finally, the predicted results of sub-models are combined to produce the final results of the predicting process. Real trending data acquired from condition monitoring routine of compressor are employed to evaluate the proposed method. A comparative study of the predicted results obtained from traditional CART and p-CART models is also carried out to appraise the prediction capability of the proposed model.

Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.1177/1475921709352148.
  • About this
    data sheet
  • Reference-ID
    10561663
  • Published on:
    11/02/2021
  • Last updated on:
    19/02/2021
 
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