公开(公告)号：US20230195100A1

公开(公告)日：2023-06-22

申请号：US17926244

申请日：2021-05-19

Applicant: Tata Consultancy Services Limited

Inventor： Sivakumar SUBRAMANIAN ,  Venkataraman RUNKANA ,  Sai Prasad PARAMESWARAN ,  Nital SHAH ,  Sandipan MAITI ,  Anagha Nikhil MEHROTRA ,  Moksha Sunil PADSALGI ,  Ratnamala MANNA ,  Rajan KUMAR ,  Sri Harsha NISTALA ,  Rohan PANDYA ,  Aditya PAREEK ,  Abhishek Krishnam Oorthy BAIKADI ,  Anirudh DEODHAR

IPC: G05B23/02

CPC classification number: G05B23/0283 ,  G05B23/0221 ,  G05B23/0243

Abstract: State of the art systems used for industrial plant monitoring have the disadvantage that they fail to correctly assess reason for dip in performance of the plant and in turn trigger appropriate corrective measures. The disclosure herein generally relates to industrial plant monitoring, and, more particularly, to a system and method for development and deployment of self-organizing cyber-physical systems for manufacturing industries. The system monitors and collects data with respect to various parameters, from the industrial plant. If any performance dip is detected, the system determines corresponding cause, and also triggers one or more corrective actions to improve performance of the plant and different plant components to a desired performance level.

公开(公告)号：US20240310793A1

公开(公告)日：2024-09-19

申请号：US18428109

申请日：2024-01-31

Applicant: Tata Consultancy Services Limited

Inventor： Sushant Shrinivas VALE ,  Sandipan MAITI ,  Subhrojyoti CHAUDHURI ,  Sri Harsha NISTALA ,  Sreedhar REDDY ,  Sivakumar SUBRAMANIAN ,  Anirudh Makarand DEODHAR ,  Venkataramana RUNKANA

IPC: G05B13/02

CPC classification number: G05B13/021

Abstract: Existing approaches for building digital twins specific to industrial plants require industry domain experts, process modeling engineers, data scientists, and solution developers to spend considerable time and effort to build the right solution. This is not an easily reproducible process. For each type of industry and for each specific plant, the design, and development process must start all over, more or less from scratch and the effort needs to be reinvested. Hence this is not a scalable proposition. Method and system disclosed herein provide a knowledge-based plant monitoring and optimization approach. In this approach, for a given high-level problem statement, a detailed problem definition is derived, a plant view of interest is identified using the knowledge based approach, and in turn plant data of interest is identified. Further, a digital twin is generated using the plant data of interest, which is then used for the plant monitoring and optimization.