Online monitoring and fault identification of mean shifts in bivariate processes using decision tree learning techniques

With modern data collection system and computers used for on-line process monitoring and fault identification in manufacturing processes, it is common to monitor more than one correlated process variables simultaneously. The main problems in most multivariate control charts (e.g., T 2 charts, MCUSUM...

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Bibliographic Details
Published in:Journal of intelligent manufacturing 2013-02, Vol.24 (1), p.25-34
Main Authors: He, Shu-Guang, He, Zhen, Wang, Gang A.
Format: Article
Language:English
Subjects:
Analysis
Business and Management
Charts
Control
Control charts
Control systems
Data collection
Decision trees
Faults
Learning
Machine learning
Machines
Manufacturing
Manufacturing execution systems
Manufacturing industry
Mathematical analysis
Mathematical models
Mechatronics
Monitoring
Normal distribution
On-line systems
Principal components analysis
Process controls
Process planning
Processes
Production
Robotics
Statistical process control
Studies
Variables
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Summary:With modern data collection system and computers used for on-line process monitoring and fault identification in manufacturing processes, it is common to monitor more than one correlated process variables simultaneously. The main problems in most multivariate control charts (e.g., T 2 charts, MCUSUM charts, MEWMA charts) are that they cannot give direct information on which variable or subset of variables caused the out-of-control signals. A Decision Tree (DT) learning based model for bivariate process mean shift monitoring and fault identification is proposed in this paper under the assumption of constant variance-covariance matrix. Two DT classifiers based on the C5.0 algorithm are built, one for process monitoring and the other for fault identification. Simulation results show that the proposed model can not only detect the mean shifts but also give information on the variable or subset of variables that cause the out-of-control signals and its/their deviate directions. Finally a bivariate process example is presented and compared with the results of an existing model.
ISSN:0956-5515
1572-8145
DOI:10.1007/s10845-011-0533-5