Computer Sciences and Information Technology
Title : | Predictive Maintenance and Quality Control in industries under Industry 4.0 |
Area of research : | Computer Sciences and Information Technology, Engineering Sciences |
Focus area : | Predictive maintenance for industry 4.0 |
Principal Investigator : | Dr Sumit Kalra, Assistant Professor, Indian Institute of Technology (IIT) Jodhpur |
Timeline Start Year : | 2019 |
Contact info : | sumitk@iitj.ac.in ; subhajit@iitbhilai.ac.in |
Details
Executive Summary : | Today’s industrial IoT solutions are judged on their ability to adapt to various data acquisition needs and how they transform data collected from devices into useful business insights for decision makers. In addition, injecting local intelligence into IIoT gateways and edge devices can provide faster response times, and reduce the data load on an industrial network. However, for an IIoT solution to work efficiently, each intelligent gateway should be backed up by robust software and a reliable and powerful industrial wireless network. The volume of data generated by edge devices is growing exponentially. Industrial IoT solutions must include a strategy to handle such large volumes of data so that the data collected from the edge devices is processed and available in the right place at the right time, and in the right format. Transmitting data to a centralized system for processing could take as long as a few minutes, which could be too late if the application includes critical industrial processes. Tools and applications that enable local intelligence in edge devices can bridge this gap and deliver faster response times.
Objective: Predictive maintenance for industry 4.0 is a method of preventing asset failure by analyzing production data to identify patterns and predict issues before they happen. Implementing industrial IoT technologies to monitor asset health, optimize maintenance schedules, and gaining real-time alerts to operational risks, allows manufacturers to lower service costs, maximize uptime, and improve production throughput.
Background: The real manufacturing world is converging with the digital manufacturing world to enable organizations to digitally plan and project the entire lifecycle of products and production facilities. IoT along with Data analytics, with a vast range of sensors that can be placed at strategic points on a production assembly line, provides manufacturers a comprehensive view of what’s occurring at every point in the production process and helps make real-time adjustments.
Process: Information Technology and Operational Technology (IT & OT) with the Internet provide the core structure of the proposed solution architecture for Industry 4.0. The condition of various entities can be measured by equipping machines with various special-purpose sensors. At the OT side in a factory, a central control section where the Supervisory Control and Data Acquisition (SCADA) takes place, relying on unstable sensors. SCADA concerns the measurement and regulation of Industrial Automation and presents an insightful analysis. All this data from SCADA/PLC will be sent to the server and analyzed for predictive maintenance and quality control by Machine Learning (ML) algorithms.
Deliverables: Algorithms/IP, System Modeling, Process flow Architecture, Data Processing Platform, An interactive dashboard, Mobile App. |
Co-PI: | Dr Subhajit Sidhanta, Assistant Professor, Dr Chiranjoy Chattopadhyay, Assistant Professor, Santanu Chaudhury, Professor, Indian Institute of Technology (IIT) Jodhpur |
Total Budget (INR): | 33,54,065 |
Achievements : | 1. A Blockchain-Based Solution for Securing Data of IoT Devices Authors: Jaspreet Kaur, Vinayak Singla, Sumit Kalra Publication date: 2019/10/28 Conference: International Conference on Service-Oriented Computing Pages: 122-129 Publisher: Springer, Cham Description: In today’s time, the number of IoT devices is increasing rapidly. We every day hear about Amazon echo, Google Mini, Smartwatches, etc. These devices collect confidential data of a person and as most of these systems follow a centralized architecture approach, most of the data on the internet is basically managed by some central authority or organization. |
Publications : | 3 |
Organizations involved