Comprehensive Course Structure
The Diploma in Instrumentation Engineering program at Shri Vaishnav Polytechnic College is structured over eight semesters, with a carefully balanced mix of core subjects, departmental electives, science electives, and laboratory sessions. This structure ensures that students develop both theoretical knowledge and practical skills essential for success in the field.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1st | IE 101 | Basic Electrical Engineering | 3-1-0-4 | - |
| 1st | IE 102 | Mathematics I | 3-1-0-4 | - |
| 1st | IE 103 | Physics for Engineers | 3-1-0-4 | - |
| 1st | IE 104 | Introduction to Programming | 2-1-0-3 | - |
| 1st | IE 105 | Workshop Practice | 0-0-2-1 | - |
| 1st | IE 106 | English for Engineers | 3-0-0-3 | - |
| 2nd | IE 201 | Electronic Devices and Circuits | 3-1-0-4 | IE 101, IE 102 |
| 2nd | IE 202 | Digital Electronics | 3-1-0-4 | IE 101, IE 102 |
| 2nd | IE 203 | Measurement and Instrumentation | 3-1-0-4 | IE 101, IE 102 |
| 2nd | IE 204 | Control Systems | 3-1-0-4 | IE 102, IE 201 |
| 2nd | IE 205 | Mathematics II | 3-1-0-4 | IE 102 |
| 2nd | IE 206 | Chemistry for Engineers | 3-1-0-4 | - |
| 3rd | IE 301 | Process Control | 3-1-0-4 | IE 204 |
| 3rd | IE 302 | Industrial Instrumentation | 3-1-0-4 | IE 203 |
| 3rd | IE 303 | Sensors and Transducers | 3-1-0-4 | IE 201, IE 203 |
| 3rd | IE 304 | Data Structures and Algorithms | 3-1-0-4 | IE 104 |
| 3rd | IE 305 | Microprocessor and Microcontroller Applications | 3-1-0-4 | IE 201, IE 202 |
| 3rd | IE 306 | Industrial Electronics | 3-1-0-4 | IE 201, IE 202 |
| 4th | IE 401 | Advanced Control Systems | 3-1-0-4 | IE 301 |
| 4th | IE 402 | Industrial Automation | 3-1-0-4 | IE 301, IE 305 |
| 4th | IE 403 | Process Simulation and Modeling | 3-1-0-4 | IE 301, IE 302 |
| 4th | IE 404 | Embedded Systems Design | 3-1-0-4 | IE 305 |
| 4th | IE 405 | Industrial Data Analytics | 3-1-0-4 | IE 304 |
| 4th | IE 406 | Project Management | 3-1-0-4 | - |
| 5th | IE 501 | Artificial Intelligence in Instrumentation | 3-1-0-4 | IE 405 |
| 5th | IE 502 | Cybersecurity for Control Systems | 3-1-0-4 | IE 402 |
| 5th | IE 503 | Renewable Energy Integration | 3-1-0-4 | IE 301, IE 302 |
| 5th | IE 504 | Advanced Sensors and Instrumentation Techniques | 3-1-0-4 | IE 303 |
| 5th | IE 505 | Control System Design for Robotics | 3-1-0-4 | IE 401, IE 402 |
| 5th | IE 506 | Industrial IoT and Wireless Networks | 3-1-0-4 | IE 404 |
| 6th | IE 601 | Process Optimization and Automation | 3-1-0-4 | IE 501, IE 502 |
| 6th | IE 602 | Advanced PLC Programming | 3-1-0-4 | IE 402 |
| 6th | IE 603 | Industrial Data Analytics and Visualization | 3-1-0-4 | IE 505 |
| 6th | IE 604 | Research Methodology | 3-1-0-4 | - |
| 6th | IE 605 | Capstone Project I | 0-0-6-4 | IE 501, IE 502 |
| 7th | IE 701 | Advanced Control System Design | 3-1-0-4 | IE 601 |
| 7th | IE 702 | Smart Manufacturing Systems | 3-1-0-4 | IE 602 |
| 7th | IE 703 | Industrial Safety and Environmental Compliance | 3-1-0-4 | - |
| 7th | IE 704 | Capstone Project II | 0-0-6-4 | IE 605 |
| 8th | IE 801 | Final Year Thesis/Capstone Project | 0-0-12-8 | IE 704 |
| 8th | IE 802 | Internship | 0-0-0-6 | - |
| 8th | IE 803 | Career Development Workshop | 0-0-2-1 | - |
Detailed Course Descriptions
The department's philosophy on project-based learning emphasizes the importance of applying theoretical knowledge to solve real-world engineering problems. This approach ensures that students not only understand concepts but also gain practical experience in designing, implementing, and evaluating engineering solutions.
Mini-projects are conducted throughout the program, starting from the second year. These projects allow students to explore specific areas of interest under the guidance of faculty mentors. The evaluation criteria include design documentation, presentation skills, technical proficiency, and teamwork effectiveness.
The final-year thesis or capstone project is a significant component of the curriculum. Students are expected to select a topic relevant to their specialization and work independently or in small teams for 12 months. Faculty mentors are assigned based on student interests and faculty expertise. The evaluation process includes periodic progress reports, mid-term presentations, and a final comprehensive report with oral defense.
Advanced Departmental Elective Courses
Artificial Intelligence in Instrumentation is designed to introduce students to machine learning algorithms, neural networks, and data analytics as applied to industrial processes. Students learn how to build predictive models for process optimization and fault detection. The course includes hands-on sessions using TensorFlow, PyTorch, and scikit-learn.
Cybersecurity for Control Systems focuses on protecting industrial networks from cyber threats. Topics include network security protocols, threat modeling, intrusion detection systems, and incident response strategies. Students engage in simulations and case studies involving actual attacks on SCADA systems.
Renewable Energy Integration explores how renewable energy sources can be integrated into existing power grids. The course covers solar and wind power technologies, energy storage systems, and smart grid concepts. Practical sessions involve designing and simulating renewable energy systems using MATLAB/Simulink.
Advanced Sensors and Instrumentation Techniques delves into modern sensor technologies including MEMS sensors, optical sensors, and wireless communication modules. Students learn how to design custom sensor interfaces and integrate them into larger systems, providing practical experience in sensor development.
Control System Design for Robotics introduces students to the integration of control theory with robotics applications. The course involves designing and building autonomous robots using sensors, actuators, and control algorithms. Practical sessions include robot programming, sensor calibration, and path planning.
Industrial Data Analytics and Visualization teaches students how to collect, analyze, and visualize industrial data to derive meaningful insights for decision-making. Tools like Python, R, Tableau, and Power BI are used to process large datasets and create interactive dashboards.
Process Optimization and Automation covers techniques for optimizing industrial processes using advanced control strategies. Students learn about PID controllers, cascade control, feedforward control, and model predictive control through simulations and lab work.
Embedded Systems Design focuses on designing and developing embedded devices used in various instrumentation applications. The course covers microcontrollers, real-time operating systems, hardware-software co-design techniques, and practical implementation using development boards.
Industrial IoT and Wireless Networks explores the integration of wireless communication technologies into industrial settings. Students learn about LoRaWAN, Zigbee, Bluetooth Low Energy, and other protocols used in smart factories and IoT deployments.
Smart Manufacturing Systems introduces students to concepts such as Industry 4.0, digital twins, and predictive maintenance. The course includes visits to smart manufacturing plants and hands-on sessions with simulation tools.
Advanced PLC Programming builds upon basic PLC knowledge by introducing advanced programming techniques and structured control systems. Students learn ladder logic, function blocks, data types, and communication protocols used in industrial automation.
Industrial Safety and Environmental Compliance covers regulatory standards and best practices for ensuring safety and environmental compliance in industrial settings. Topics include hazard identification, risk assessment, emergency response planning, and sustainable manufacturing practices.
Research Methodology provides students with the tools needed to conduct independent research. The course covers literature review, hypothesis formulation, experimental design, data analysis, and scientific writing.
Capstone Project I and II are extended projects that allow students to apply their knowledge in solving complex engineering problems. These projects involve extensive research, design, implementation, and documentation phases.
Project Selection Process
Students begin selecting their capstone project topics during the sixth semester. The selection process involves submitting a proposal outlining the problem statement, objectives, methodology, and expected outcomes. Faculty mentors are assigned based on availability and expertise matching.
The evaluation criteria for projects include innovation, technical depth, feasibility, impact, and presentation quality. Regular meetings with mentors ensure that projects stay on track and meet academic standards.