Session: 17-01-01 Research Posters

Paper Number: 73780

Start Time: Thursday, 02:25 PM

73780 - Experimental Investigation, Modeling and Simulation for Industry 4.0 Case Studies in Rainwater Harvesting, and Predictive Maintenance 

Organizations are making transformation toward Industry 4.0 based on innovation by Cyber-Physical System (CPS). CPS Systems integrate computation, networking, physical processes that will provide new functionalities to improve quality of life and enable technological advances in critical areas. Besides CPS, transformative technologies impacting Industry 4.0 are, Machine-to-machine learning, Big-Data analysis, Cloud computing, Mobile internet, Autonomous vehicles, Advanced materials. Industry 4.0 is also influenced by topics such as Enhanced Visualization and Simulation, Additive Manufacturing and Augmented Reality. The combination of Internet and future-oriented technologies has resulted in a new fundamental paradigm shift in Industry.  One area where major applications are observed is the healthcare industry. Growing heath care needs have seen the introduction of Artificial intelligence and data science integrated to tele-sensing of vital signs. Sensory perception and model-based decision making has helped the study of human gait to assist in balance.  This paper includes two recent case studies demonstrating the impact of Industry 4.0 Experimental investigation, analytical modeling and numerical simulations are important tools to validation technology used in Industry 4.0           (A). Predictive maintenance takes advantage of data-driven insights on the measurement of operating conditions. In an example with automobile, this could be measurement of oil viscosity or engine speed. These analytics could be bolstered with data from external factors such as outside air temperature or geo-location. Statistical modeling and forecasting tools calculate when repairs are required. Organizations, who adopt predictive maintenance strategies are able to better manage parts and labor costs. The analytics required to perform effective predictive maintenance requires convergence of data. This example examines the predictive maintenance in typical elevator and escalator industry which calculates Elevator/Escalator Condition Index (EC) I based on original equipment reliability, projected average life cycle of key wear components, and number of run cycles since last maintenance on each component, cost of emergency repair vs. cost of maintenance vs. likelihood of failure. It is expected that the corporation would likely see higher signup rates from elevators with a better ECI, as the maintenance cost should decrease as a result.                                                                  (B). Industry 4.0 in an Adaptively Controlled Rainwater Harvesting System. Recent advances in information technology are now, however, providing cost-effective opportunities to achieve better performance of conventional storm water infrastructure through a Continuous Monitoring and Adaptive Control (CMAC) approach. The primary objective of this study is to demonstrate that a CMAC approach can be applied to a conventional rainwater harvesting system in New York City to improve performance by minimizing discharge to the combined sewer during rainfall events, reducing water use for irrigation of local vegetation, and optimizing vegetation health. To achieve this objective, a hydrologic and hydraulic model was developed for a planned and designed rainwater harvesting system to explore multiple potential scenarios prior to the system’s actual construction. Model results indicate that the CMAC rainwater harvesting system is expected to provide significant performance improvements over conventional rainwater harvesting systems. The flexibility of the CMAC approach to meet competing objectives is promising for widespread implementation in New York City and other heavily urbanized areas challenged by storm water management issues.

Presenting Author: Devdas Shetty University of District of Columbia

Authors:

Devdas Shetty University of District of Columbia
Nandan Shetty CITADEL