This post is an excerpt from the journal ISA Transactions. All ISA Transactions articles are free to ISA members, or can be purchased from Elsevier Press.
Abstract:
Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in manufacturing processes. This paper presents a novel hybrid intelligent method (HIM) for recognition of the common types of control chart pattern (CCP). The proposed method includes two main modules: a clustering module and a classifier module. In the clustering module, the input data is first clustered by a new technique. This technique is a suitable combination of the modified imperialist competitive algorithm (MICA) and the K-means algorithm.
Then the Euclidean distance of each pattern is computed from the determined clusters. The classifier module determines the membership of the patterns using the computed distance. In this module, several neural networks, such as the multilayer perceptron, probabilistic neural networks, and the radial basis function neural networks, are investigated. Using the experimental study, we choose the best classifier in order to recognize the CCPs.
Simulation results show that a high recognition accuracy, about 99.65%, is achieved.
Free Bonus! To read the full version of this ISA Transactions article, click here.
Join ISA and get free access to all ISA Transactions articles as well as a wealth of other technical content, plus discounts on events, webinars, training & education courses, and professional certification.
Click here to join ... learn, advance, succeed!
Copyright 2006-2017Elsevier Science Ltd. All rights reserved.
