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

Authors:
Shu-Guang He;Zhen He;Gang A. Wang
Affiliations:
School of Management, Tianjin University, Tianjin, China 300072;School of Management, Tianjin University, Tianjin, China 300072;Department of Business Information Technology, Virginia Tech, Blacksburg, USA 24061
Venue:
Journal of Intelligent Manufacturing
Year:
2013

Citing 10
Cited 1

Simplifying decision trees

International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Multivariate generalizations of cumulative sum quality-control schemes

Technometrics
A multivariate exponentially weighted moving average control chart

Technometrics
Induction of Decision Trees

Machine Learning
A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression

Bioinformatics
Intelligent monitoring and diagnosis of manufacturing processes using an integrated approach of KBANN and GA

Computers in Industry
An effective application of decision tree learning for on-line detection of mean shifts in multivariate control charts

Computers and Industrial Engineering
A neural network ensemble-based model for on-line monitoring and diagnosis of out-of-control signals in multivariate manufacturing processes

Expert Systems with Applications: An International Journal
Improved use of continuous attributes in C4.5

Journal of Artificial Intelligence Research
A hybrid learning-based model for on-line detection and analysis of control chart patterns

Computers and Industrial Engineering

Fault diagnosis and prognosis using wavelet packet decomposition, Fourier transform and artificial neural network

Journal of Intelligent Manufacturing

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.