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Many industries are applying various methods for optimizing energy use across the manufacturing life cycle. These methods are either physics-based or data-driven. Manufacturing systems generate a vast amount of data from operations and in simulations. Advances in data collection systems and data analytics (DA) tools have enabled the development of predictive analytics for energy prediction. Many of...
In this paper, we propose an architectural design and software framework for fast development of descriptive, diagnostic, predictive, and prescriptive analytics solutions for dynamic production processes. The proposed architecture and framework will support the storage of modular, extensible, and reusable Knowledge Base (KB) of process performance models. The approach requires the development of automatic...
In this paper, we discuss data-driven discovery challenges of the Big Data era. We observe that recent innovations in being able to collect, access, organize, integrate, and query massive amounts of data from a wide variety of data sources have brought statistical data mining and machine learning under more scrutiny and evaluation for gleaning insights from the data than ever before. In that context,...
Data analytics is increasingly becoming recognized as a valuable set of tools and techniques for improving performance in the manufacturing enterprise. However, data analytics requires data and a lack of useful and usable data has become an impediment to research in data analytics. In this paper, we describe issues that would help aid data availability including data quality, reliability, efficiency,...
Manufacturing generates a vast amount of data both from operations and simulation. Extracting appropriate information from this data can provide insights to increase a manufacturer's competitive advantage through improved sustainability, productivity, and flexibility of their operations. Manufacturers, as well as other industries, have successfully applied a promising statistical learning technique,...
One of the most important challenges in manufacturing is the continuous process improvement that requires new insights about the behavior/quality control of processes in order to understand the optimization/improvement potential. The paper elaborates on usage of big data-driven clustering for an efficient discovering of real-time unusualities in the process and their route-cause analysis. Our approach...
Condition and cloud based prognostics offers the opportunities to reduce loss due to machine failures and delay in planning for maintenance activities and spare parts. The time complexity of the prognostics algorithms used for predicting imminent component failures or the remaining useful life of critical components plays a vital role in determining the accuracy and feasibility of prognostics systems...
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