A multi-layer neural network model for detecting changes in the process mean
Computers and Industrial Engineering
The nature of statistical learning theory
The nature of statistical learning theory
Pairwise classification and support vector machines
Advances in kernel methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Recognition of control chart patterns using multi-resolution wavelets analysis and neural networks
Computers and Industrial Engineering
A hybrid system for SPC concurrent pattern recognition
Advanced Engineering Informatics
Recognition of control chart patterns using improved selection of features
Computers and Industrial Engineering
A hybrid learning-based model for on-line detection and analysis of control chart patterns
Computers and Industrial Engineering
Simultaneous process mean and variance monitoring using artificial neural networks
Computers and Industrial Engineering
Computers and Industrial Engineering
Computers and Industrial Engineering
Neural networks for detecting cyclic behavior in autocorrelated process
Computers and Industrial Engineering
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
A fast iterative nearest point algorithm for support vector machine classifier design
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Detection and classification of defect patterns in optical inspection using support vector machines
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
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Since abnormal control chart patterns (CCPs) are indicators of production processes being out-of-control, it is a critical task to recognize these patterns effectively based on process measurements. Most methods on CCP recognition assume that the process data only suffers from single type of unnatural pattern. In reality, the observed process data could be the combination of several basic patterns, which leads to severe performance degradations in these methods. To address this problem, some independent component analysis (ICA) based schemes have been proposed. However, some limitations are observed in these algorithms, such as lacking of the capability of monitoring univariate processes with only one key measurement, misclassifications caused by the inherent permutation and scaling ambiguities, and inconsistent solution. This paper proposes a novel hybrid approach based on singular spectrum analysis (SSA) and support vector machine (SVM) to identify concurrent CCPs. In the proposed method, the observed data is first separated by SSA into multiple basic components, and then these separated components are classified by SVM for pattern recognition. The scheme is suitable for univariate concurrent CCPs identification, and the results are stable since it does not have shortcomings found in the ICA-based schemes. Furthermore, it has good generalization performance of dealing with the small samples. Superior performance of the proposed algorithm is achieved in simulations.