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• W2076006837 abstract "In this paper, a unified decision support system (DSS) is proposed, which uses real-time energy measurements and process operational states to make effective decisions, en- abling high-performance manufacturing. To reduce the number of required sensors and amount of logged data, our proposed DSS includes an intelligent framework which identifies the process operational states based on energy measurements. This process identification framework uses Haar transform and empirical Bayesian threshold to segment the power time series and support vector machines to cluster the power segments into groups according to the underlying process operational states. To justify our proposed framework, comparative experiments with an existing framework are evaluated on two industrial applications, an injection moulding system and a stamping system. Experiment results show that our proposed framework is more effective in identifying the process operational states using the energy patterns. logged and collected from the various sources of systems and sensors at a fixed sampling rate in a big data matrix. This impedes the effectiveness of a DSS in extracting useful information and features from the data matrix for making effective decisions (3). DSS is an information system that support decision-making processes and problem solving ac- tivities. As a concept, DSS has been proliferated and evolved over the past few decades (7). With advancing information and communications technology, DSS is nowadays widely implemented in global industries. In this paper, we provide a unified DSS architecture. Us- ing real-time energy measurements and process operational states, our proposed DSS aims to make energy-efficient, cost-effective, and reliable decisions for the next generation of high-performance manufacturing. In addition, life-cycle analysis can be partially supported, as energy consumption during the production stage of a product's life-cycle is accurately logged and documented. To reduce the number of required sensors and amount of logged data, our proposed DSS includes an intelligent framework which identifies the process operational states based on energy measurements. This process identification framework uses Haar transform and empirical Bayesian (EBayes) threshold to segment the power time series and support vector machines (SVMs) to cluster the power segments into groups according to the underlying process operational states. To justify our proposed framework, comparative exper- iments with an existing framework (8) are evaluated on two industrial applications, an injection moulding system and a stamping system. Experiment results show that our proposed framework is more effective in identifying the process operational states using the energy patterns." @default.
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• W2076006837 date "2014-06-01" @default.
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• W2076006837 title "An energy data-driven decision support system for high performance in industrial injection moulding and stamping systems" @default.
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• W2076006837 doi "https://doi.org/10.1109/icca.2014.6871074" @default.
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