Decision tree based control chart pattern recognition
- 18 July 2008
- journal article
- research article
- Published by Taylor & Francis Ltd in International Journal of Production Research
- Vol. 46 (17), 4889-4901
- https://doi.org/10.1080/00207540701294619
Abstract
This paper presents a new approach to classify six anomaly types of control chart patterns (CCP), of systematic pattern, cyclic pattern, upward shift, downward shift, upward trend, and downward trend. Current CCP recognition methods use either unprocessed raw data or complex transformed features (via principal component analysis or discrete wavelet transform) as the input representation for the classifier. The objective of using selected features is not only for dimension reduction of input representation, but also implies the process of data compression. In contrast, using raw data is often computationally inefficient while using transformed features is very tedious in most cases. Therefore, owing to its computational advantage, using appropriate features of CCP to achieve good classification accuracy becomes more promising in real process implementation. In this study, using three features of CCP shows quite a competitive performance in terms of classification accuracy and computational loading. More importantly, the proposed method presented here has potential to be generalized to medical, financial, and other application of temporal data.This publication has 20 references indexed in Scilit:
- On-line identification of control chart patterns using self-organizing approachesInternational Journal of Production Research, 2005
- Wavelet-based multiscale statistical process monitoring: A literature reviewIIE Transactions, 2004
- Improved SPC chart pattern recognition using statistical featuresInternational Journal of Production Research, 2003
- An integrated neural network approach for simultaneous monitoring of process mean and variance shifts a comparative studyInternational Journal of Production Research, 1999
- A two-stage neural network approach for process variance change detection and classificationInternational Journal of Production Research, 1999
- A neural network based model for abnormal pattern recognition of control chartsComputers & Industrial Engineering, 1999
- A neural network approach to characterize pattern parameters in process control chartsJournal of Intelligent Manufacturing, 1999
- AUTOMATED UNNATURAL PATTERN RECOGNITION ON CONTROL CHARTS USING CORRELATION ANALYSIS TECHNIQUESComputers & Industrial Engineering, 1997
- A neural network approach for the analysis of control chart patternsInternational Journal of Production Research, 1997
- Detecting process non-randomness through a fast and cumulative learning ART-based pattern recognizerInternational Journal of Production Research, 1995