Session: 20-07 - Optimization II - Machine Learning, and the Digital Twin

Paper Number: 124967

124967 - Smart Industrial Gas Turbine Low-Cost IoT Architecture 

The smart energy systems, based on numerous digital technologies, became an integral part of the current efforts to revolutionize energy production. One of the main cornerstones of smart energy concept is connectivity, which is typically enabled through Industrial Internet of Things (IIoT) technology. In this contribution we present development of Low-Cost Internet-of-Thing (IoT) architecture with intention to provide a secure connection of Industrial Gas Turbine systems with Cloud platforms.

Developed IoT gateway architecture encompasses key features related to connectivity, computing power, and communication capability. Proposed Low-cost IoT architecture supports live data streaming process which allows remote monitoring and real-time analytics of Industrial Gas Turbine (IGT) system. The IoT gateway platform satisfy requirements attributed to secure and fast onboarding process, and moreover cost efficiency and scalability.

The devised Edge system, via reliable connection to IGT control system, is capable to collect, enrich and transfer data, for the purpose of local and remote analytics and visualization. The Gas Turbine trackable data (sensor measurements and control signals) are enriched with virtual engine data (soft sensors and internal health parameters) generated by Executable Digital Twin (xDT) and streamed to Cloud platform, where real-time analytics and optimization of assets is carried out.

In this contribution we explored integration of Executable Digital Twin with IoT Gateway, and subsequent deployment onto dedicated Low-Cost Edge platform. Feasibility and viability of proposed design are disused in this study, and we present operational results following fusion with Live Data Streaming (LDS) system.

 

Key Words: Edge, Cloud, Internet-of-Things, Gateway, Real-Time, Streaming, Analytics, Remote Monitoring, Low-Cost, Smart Energy Systems, Live Data Streaming.

Presenting Author: Vili Panov Siemens Energy Industrial Turbomachinery Ltd

Presenting Author Biography: Vili Panov is Chartered Engineer and Member of IMechE. Currently he is employed as Senior Key Expert by Siemens Energy Industrial Turbomachinery Ltd. Dr Panov is a Visiting Professor of Health Monitoring and Diagnostics in College of Science at University of Lincoln. He received the Dipl.-Ing. degree in Mechanical Engineering and the M.Sc. degree in Aeronautical Engineering, from University of Belgrade. Dr Panov was awarded a Ph.D. in Engineering Mechanics from Cranfield University in 2006. He has more than 25 years of industry experience in the turbomachinery sector, and has been awarded with several patents.

Authors:

Vili Panov Siemens Energy Industrial Turbomachinery Ltd
Samuel Cruz-Manzo Siemens Energy Industrial Turbomachinery Ltd
Egor Goloshchapov Siemens Energy ES