Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/66408
Title: Unsupervised data clustering for production mode identification through energy consumption monitoring and analysis
Authors: Toh, Zi Kai
Keywords: DRNTU::Engineering::Computer science and engineering::Mathematics of computing::Numerical analysis
Issue Date: 2016
Abstract: High competitive pressure in the manufacturing industry has contributed in ensuring manufacturing processes need to be closely monitored for any deviation in the process. Proper analysis of control charts that are used to determine the mode of the process not only requires a thorough knowledge and understanding of the underlying theories but also the expertise for decision making. In this paper, a methodology is adapted from the Fayyad model in searching for the most appropriate algorithm that could identify and interpret the production operational modes based on various patterns of variation energy measurement that can occur in a manufacturing process. This methodology uses both internal cluster validity measures and external cluster validity measure in evaluating the most appropriate clustering algorithm. To justify the proposed model, experiment is conducted on an industrial application, an injection moulding system. Experimental result show that the hierarchical agglomerative (complete-link) clustering is more effective in labeling the production operational modes using the energy patterns.
URI: http://hdl.handle.net/10356/66408
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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