Master of Science Project Defense: Nihanth Kolli
When: Tuesday,
January 10, 2017
1:00 PM
-
3:00 PM
Where: Science & Engineering Building, Lester W. Cory Conference Room: Room 213A
Description: Topic: Real-Time Predictive Maintenance Using Internet of Things
Location: Lester W. Cory Conference Room, Science & Engineering Building (SENG), Room 213A
Abstract:
The Internet of Things (IoT) is focused on making new services and improving productivity and efficiency of existing frameworks by providing insightful and intelligent decision making in real-time. Based on the Microsoft Azure IoT hub and cloud, this project implements a real-time predictive maintenance technique, which is used to predict when and what type of maintenance should be conducted based on conditions of in-service equipment acquired through different sensors. A case study of predictive maintenance for the wind turbine system is demonstrated, where an alert message containing data about the part to be maintained in the turbine can be sent to smart phones of related officials in a real-time and synchronous manner.
Note: All ECE Graduate Students are ENCOURAGED to attend.
All interested parties are invited to attend. Open to the public.
Advisor: Dr. Liudong Xing
Committee Members: Dr. Lance Fiondella and Dr. Honggang Wang, Department of Electrical & Computer Engineering
*For further information, please contact Dr. Liudong Xing at 508.999.8883, or via email at lxing@umassd.edu.
Location: Lester W. Cory Conference Room, Science & Engineering Building (SENG), Room 213A
Abstract:
The Internet of Things (IoT) is focused on making new services and improving productivity and efficiency of existing frameworks by providing insightful and intelligent decision making in real-time. Based on the Microsoft Azure IoT hub and cloud, this project implements a real-time predictive maintenance technique, which is used to predict when and what type of maintenance should be conducted based on conditions of in-service equipment acquired through different sensors. A case study of predictive maintenance for the wind turbine system is demonstrated, where an alert message containing data about the part to be maintained in the turbine can be sent to smart phones of related officials in a real-time and synchronous manner.
Note: All ECE Graduate Students are ENCOURAGED to attend.
All interested parties are invited to attend. Open to the public.
Advisor: Dr. Liudong Xing
Committee Members: Dr. Lance Fiondella and Dr. Honggang Wang, Department of Electrical & Computer Engineering
*For further information, please contact Dr. Liudong Xing at 508.999.8883, or via email at lxing@umassd.edu.
Contact:
ECE: Electrical & Computer Engineering Department 508.999.9164 http://www.umassd.edu/engineering/ece/
Topical Areas: General Public, University Community, College of Engineering, Electrical and Computer Engineering