Publication

Abstract : Abstract of the topic: Industry is currently undergoing its 4th revolution termed as I 4.0 [Industry 4.0], where the industrial machines with intelligent controllers and sensoronic systems as a result of Industry 3.0 is now connected together to form an Industrial Internet of Things [IIOT] with the advancement in Information and communication technology. Industrial Experts state as “Data” from this IIOT’s are going to be the “Crude Oil”. Data Analyst role in this new scenario is going to be vital to refine out insights from this data collected to make informed data driven decisions like improve productivity or utilization, improve efficiency of process or method, improve life or predict failures in machines etc., In this project we attempt to follow the Cross Industry Standard Process of Data Mining frame work[CRISP DM] using a data set from existing IIOT systems with a business understanding of I 4.0 and analyze the data. We also attempt to develop a dashboard for asset utilization and energy management, which can enable Continual Manufacturing Improvement in a typical industrial setup. The ETL [Extract, Transform & Load] & visual analytic feature of Microsoft Power Business Intelligence is used in this work. An attempt to create custom visual using advanced data analysis feature of R Script were also explored as a decision support system for the shop floor managers of a typical industrial setup. Use cases of Predictive, Prescriptive & Prognostic analysis to improve asset management are also explored and the results are documented.