Workdiamond2017

Recognition of Control Chart Pattern Using Improved Supervised Locally Linear Embedding and Support Vector Machine

33 citations0/0 files storedProcedia Engineering

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Advanced Statistical Process Monitoring Machine Fault Diagnosis Techniques Fault Detection and Control Systems

Abstract

Control chart patterns (CCPs) have been widely utilized for machining process control, the effective recognition of abnormal CCPs can significantly narrow the set of possible assignable causes with shortening the diagnostic process to improve the intelligence of quality monitoring. This paper proposes a method for control chart classification based on improved supervised locally linear embedding (SLLE) and support vector machine (SVM). The present work extracts 12 dimensional statistical feature and shape feature of the control chart, reduces the dimensionality of high dimensional feature set by SLLE, and estimates the neighborhood size and embedding dimension with normalized cuts (Ncut) criterion. Genetic algorithm (GA) is utilized to optimize SVM classifier by searching the best values of the SVM parameters. According to the data set generated by Monte Carlo simulation, the simulation result and performance are analyzed and compared the recognition accuracy with fore-and-aft dimensionality reduction, different descending dimension algorithms and various classifiers. The result demonstrates the proposed approach can recognize CCPs effectively.

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References

2012A survey of control-chart pattern-recognition literature (1991–2010) based on a new conceptual classification scheme 2008Recognition of control chart patterns using improved selection of features 2013Recognition of concurrent control chart patterns using wavelet transform decomposition and multiclass support vector machines 2011Mixture control chart patterns recognition using independent component analysis and support vector machine 2010Control chart pattern recognition using a novel hybrid intelligent method

Cited by

2021On-line concurrent control chart pattern recognition using singular spectrum analysis and random forest 2019A control chart pattern recognition system for feedback-control processes 2019Control Chart Pattern Recognition Method Based on Improved One-dimensional Convolutional Neural Network 2020A robust data simulation technique to improve early detection performance of a classifier in control chart pattern recognition systems 2024Control chart pattern recognition under small shifts based on multi-scale weighted ordinal pattern and ensemble classifier 2019Efficient mixture control chart pattern recognition using adaptive RBF neural network

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