GTC-2 port automation in martime industries

GTC-2 port automation

1. GENERAL TEST COMMITTEE MEETING-2 Presented by Udhayakumar.P EE 22M2201 - M.Tech (By Research) Department of EEE National Institute of Technology Puducherry Under the guidance of Dr. Revathi S Assistant Professor Department of EEE National Institute of Technology Puducherry 10/1/2024 EE22M2201 1

2. GENERAL TEST COMMITTEE MEMBERS 10/1/2024 EE22M2201 2 CHAIRPERSON Dr. Surendiran B Associate Professor Department of CSE National Institute of Technology Puducherry RESEARCH SUPERVISOR Dr. Revathi S Assistant Professor Department of EEE National Institute of Technology Puducherry GTC INTERNAL MEMBER Dr. Thangavel S Associate Professor Department of EEE National Institute of Technology Puducherry GTC INTERNAL MEMBER Dr. Rajvir Kaur Assistant Professor Department of EEE National Institute of Technology Puducherry

3. Proposed Area of Research Plant wide industrial process monitoring and control. Research objectives To Design the attack resilient networked Distributed Control System. To Develop Cloud based data storage along with protection of cyber security. To enable schedule maintenance through analysis of real-time data and facilitate prediction for Remaining useful life (RUL) . 10/1/2024 EE22M2201 3

4. COMPLETED COURSEWORK 10/1/2024 EE22M2201 4 S. No Subject Code Subject Title Credits 1 MA801 Advanced Mathematics 3 2 EE659 Transducers and instrumentation 3 3 EE674 Computer control of process 3 4 EE664 Industrial automation and control 3 5 EE816 Cyber security in industrial automation 3 6 EE654 Fuzzy systems 3

5. External course work 10/1/2024 EE22M2201 5

6. Fuzzy Model Reference Learning Control for Hazard Identification in Port’s Material Handling Operations and Maintenance Author Names Udhayakumar palanivel 1 ; Richa Ahuja 2 ; Revathi S 3 1 Adani Ports and Special Economic Zone, [email protected] 2 CoESEA, IIT Kharagpur , [email protected] 3 NIT,Pudhucherry , [email protected]

7. Abstract Ports are vital for maritime transportation but face safety issues in design, installation, operation, and maintenance. Port material handling operations and maintenance are crucial for ensuring efficient and safe handling of goods. Hazards, which can lead to difficulties in emergency response and significant losses in terms of personnel, property, and social security. The paper presents a fuzzy-model reference learning (FMRL) based framework for conveyor motor health and safety risk assessment in ports, demonstrated through a case study. To identify hazards related to conveyor motors in ports and predict system failures, a FMRL based prediction system can be utilized. By integrating fuzzy inference system with model reference learning strategy, the system can effectively assess risks associated with conveyor motor operations in port environments. This approach can help in identifying failure modes, evaluating risk factors, and prioritizing protective measures for conveyor motors in port facilities. Predictive maintenance is most useful for minimizing unanticipated downtime. In order to foresee when machinery might break down, predictive maintenance analyses data collected by sensors. Port operations can be better managed with the help of early problem detection, which improves safety, increases productivity, and reduces costs. 10/1/2024 EE22M2201 7

8. Certification 10/1/2024 EE22M2201 8

9. International Conference on “Advances in Sensors, Controls and Safety Networks” OPERATION AND CONTROL OF INDUSTRIAL BELT CONVEYOR SYSTEM – A PORT OPERATION CASE STUDY (ASCSN-2023) Paper ID:AS-20 Session : TRACK-3 Name of Presenter: UDHAYAKUMAR Presenter Affiliation: NIT PY NITPY ,Karaikal ASCSN-2023 2 nd & 3 rd November-2023

10. Abstract Efficient port operations rely on accurate estimation and control of state variables, particularly in the context of bulk material handling using squirrel cage induction motors in belt conveyors. The operational speed of these motors is a critical factor for enhancing productivity and involves a cascaded process. Traditional methods for estimating the drive speed in conveyors often require the use of costly instruments to capture both measured and unmeasured state variables. This study proposes a sensor less control approach for three-phase induction motors utilizing a Fractional Order estimator. This method lies in its ability to eliminate the need for speed sensors, thereby reducing costs significantly. The Fractional Order Extended Kalman filter (FEKF) algorithm, offers the flexibility to incorporate effects such as friction and slipping, which are challenging to model with integer-order methods. This filter acts as a specialized observer that effectively mitigates measurement noise. The results highlight the benefits of this sensor less control approach in the optimization of conveyor system operations. 10/1/2024 EE22M2201 10

11. Certification 10/1/2024 EE22M2201 11

12. THANK YOU 10/1/2024 EE22M2201 12