Self-organizing maps
A fast fixed-point algorithm for independent component analysis
Neural Computation
A hybrid system for SPC concurrent pattern recognition
Advanced Engineering Informatics
Multichannel blind separation and deconvolution of images for document analysis
IEEE Transactions on Image Processing
Process control using VSI cause selecting control charts
Journal of Intelligent Manufacturing
Control chart pattern recognition using a novel hybrid intelligent method
Applied Soft Computing
Journal of Intelligent Manufacturing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Journal of Intelligent Manufacturing
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Identification of unnatural control chart patterns (CCPs) from manufacturing process measurements is a critical task in quality control as these patterns indicate that the manufacturing process is out-of-control. Recently, there have been numerous efforts in developing pattern recognition and classification methods based on artificial neural network to automatically recognize unnatural patterns. Most of them assume that a single type of unnatural pattern exists in process data. Due to this restrictive assumption, severe performance degradations are observed in these methods when unnatural concurrent CCPs present in process data. To address this problem, this paper proposes a novel approach based on singular spectrum analysis (SSA) and learning vector quantization network to identify concurrent CCPs. The main advantage of the proposed method is that it can be applied to the identification of concurrent CCPs in univariate manufacturing processes. Moreover, there are no permutation and scaling ambiguities in the CCPs recovered by the SSA. These desirable features make the proposed algorithm an attractive alternative for the identification of concurrent CCPs. Computer simulations and a real application for aluminium smelting processes confirm the superior performance of proposed algorithm for sets of typical concurrent CCPs.