Curriculum Overview
The Industrial Maintenance program at Government Polytechnic Tanakpur is structured over 8 semesters, providing a comprehensive and progressive learning experience. The curriculum is designed to ensure students develop both theoretical knowledge and practical skills essential for success in industrial maintenance environments.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | IM-101 | Engineering Mathematics I | 3-1-0-4 | - |
| 1 | IM-102 | Applied Physics | 3-1-0-4 | - |
| 1 | IM-103 | Chemistry for Engineers | 3-1-0-4 | - |
| 1 | IM-104 | Computer Programming | 2-1-0-3 | - |
| 1 | IM-105 | Basic Electrical Engineering | 3-1-0-4 | - |
| 1 | IM-106 | Engineering Drawing & Graphics | 2-1-0-3 | - |
| 1 | IM-107 | Workshop Practice | 0-0-2-2 | - |
| 1 | IM-108 | Introduction to Industrial Maintenance | 2-0-0-2 | - |
| 2 | IM-201 | Engineering Mathematics II | 3-1-0-4 | IM-101 |
| 2 | IM-202 | Mechanics of Materials | 3-1-0-4 | - |
| 2 | IM-203 | Thermodynamics | 3-1-0-4 | - |
| 2 | IM-204 | Electrical Machines & Circuits | 3-1-0-4 | - |
| 2 | IM-205 | Fluid Mechanics | 3-1-0-4 | - |
| 2 | IM-206 | Computer Applications in Engineering | 2-1-0-3 | - |
| 2 | IM-207 | Workshop Practice II | 0-0-2-2 | - |
| 3 | IM-301 | Applied Mechanics | 3-1-0-4 | IM-202 |
| 3 | IM-302 | Maintenance Planning & Management | 3-1-0-4 | - |
| 3 | IM-303 | Industrial Safety & Health | 2-1-0-3 | - |
| 3 | IM-304 | Process Control & Instrumentation | 3-1-0-4 | - |
| 3 | IM-305 | Reliability Engineering | 2-1-0-3 | - |
| 3 | IM-306 | Data Structures & Algorithms | 2-1-0-3 | - |
| 3 | IM-307 | Mini Project I | 0-0-4-4 | - |
| 4 | IM-401 | Mechatronics & Automation | 3-1-0-4 | - |
| 4 | IM-402 | Advanced Maintenance Techniques | 3-1-0-4 | - |
| 4 | IM-403 | Digital Systems & Microprocessors | 3-1-0-4 | - |
| 4 | IM-404 | Industrial Electronics | 3-1-0-4 | - |
| 4 | IM-405 | Energy Systems Maintenance | 3-1-0-4 | - |
| 4 | IM-406 | Project Management & Economics | 2-1-0-3 | - |
| 4 | IM-407 | Mini Project II | 0-0-4-4 | - |
| 5 | IM-501 | Predictive Maintenance Systems | 3-1-0-4 | - |
| 5 | IM-502 | Industrial Robotics | 3-1-0-4 | - |
| 5 | IM-503 | AI for Maintenance Applications | 3-1-0-4 | - |
| 5 | IM-504 | Cybersecurity in Industrial Systems | 3-1-0-4 | - |
| 5 | IM-505 | Sustainable Maintenance Practices | 2-1-0-3 | - |
| 5 | IM-506 | Quality Assurance & Reliability | 3-1-0-4 | - |
| 5 | IM-507 | Mini Project III | 0-0-4-4 | - |
| 6 | IM-601 | Smart Manufacturing Technologies | 3-1-0-4 | - |
| 6 | IM-602 | Industrial Internet of Things (IoT) | 3-1-0-4 | - |
| 6 | IM-603 | Advanced Process Control | 3-1-0-4 | - |
| 6 | IM-604 | Industrial Data Analytics | 3-1-0-4 | - |
| 6 | IM-605 | Capstone Project - I | 0-0-8-8 | - |
| 6 | IM-606 | Professional Ethics & Communication | 2-1-0-3 | - |
| 7 | IM-701 | Capstone Project - II | 0-0-8-8 | - |
| 7 | IM-702 | Internship & Industry Exposure | 0-0-8-8 | - |
| 7 | IM-703 | Advanced Topics in Maintenance Engineering | 3-1-0-4 | - |
| 7 | IM-704 | Research Methodology & Thesis Writing | 2-1-0-3 | - |
| 7 | IM-705 | Elective Course I | 3-1-0-4 | - |
| 7 | IM-706 | Elective Course II | 3-1-0-4 | - |
| 8 | IM-801 | Final Year Project | 0-0-12-12 | - |
| 8 | IM-802 | Industry Internship & Thesis Defense | 0-0-12-12 | - |
| 8 | IM-803 | Elective Course III | 3-1-0-4 | - |
| 8 | IM-804 | Elective Course IV | 3-1-0-4 | - |
Advanced Departmental Electives
The program offers a wide range of advanced departmental electives designed to deepen students' understanding and specialization in various aspects of industrial maintenance. These courses are taught by experienced faculty members who bring both academic expertise and industry experience to the classroom.
One such course is Predictive Maintenance Systems, which introduces students to advanced techniques for predicting equipment failures before they occur. This course covers statistical methods, machine learning algorithms, and sensor technologies used in predictive maintenance. Students learn how to interpret data from various sources and apply them to develop maintenance strategies that minimize downtime and maximize asset lifespan.
Another important elective is Industrial Robotics, which explores the design, programming, and application of robotic systems in industrial settings. Students gain hands-on experience with industrial robots, learning to program them for tasks such as assembly line operations, inspection, and maintenance. This course also covers robot safety protocols, integration challenges, and collaborative robotics.
The AI for Maintenance Applications course provides an in-depth look at how artificial intelligence can be applied to enhance maintenance operations. Topics include neural networks, deep learning models, computer vision, and natural language processing. Students work on real-world projects using AI tools to optimize maintenance processes and reduce operational costs.
The Cybersecurity in Industrial Systems course addresses the growing need for security in industrial environments. It covers network security, risk assessment, incident response, and compliance with industry standards. Students learn how to protect critical infrastructure from cyber threats while maintaining operational integrity and efficiency.
Sustainable Maintenance Practices introduces students to environmental considerations in maintenance activities. The course emphasizes resource optimization, waste reduction, and life-cycle assessment techniques. It covers green maintenance policies, eco-friendly lubricants, and energy-efficient practices that minimize the ecological footprint of industrial operations.
The Quality Assurance & Reliability elective delves into the principles and practices of ensuring consistent quality in maintenance activities. Students learn about quality management systems, Six Sigma methodologies, fault tree analysis, and reliability-centered maintenance (RCM) approaches. This course prepares students to implement quality control measures that meet industry standards and regulatory requirements.
Additional electives include Smart Manufacturing Technologies, which explores Industry 4.0 frameworks, digital twins, cyber-physical systems, and intelligent manufacturing processes; Industrial Internet of Things (IoT), which focuses on sensor networks, data communication protocols, and smart infrastructure design; and Advanced Process Control, which covers control theory, system identification, and optimization techniques for complex industrial processes.
Project-Based Learning Philosophy
The department places a strong emphasis on project-based learning as a core component of the educational experience. This approach is designed to bridge the gap between theoretical knowledge and practical application, ensuring that students are well-prepared for real-world challenges in their careers.
The mini-projects are introduced in the third year and continue through the final year. These projects are carefully structured to allow students to apply the concepts learned in class to actual industrial scenarios. Each project is assigned a faculty mentor who guides the student throughout the process, providing technical support and ensuring academic rigor.
Mini Projects I (Semester 3) focuses on fundamental problem-solving skills using basic tools and techniques. Students work individually or in small groups to tackle problems related to mechanical systems, electrical circuits, or basic maintenance practices. The projects are evaluated based on technical accuracy, creativity, and presentation quality.
Mini Projects II (Semester 4) builds upon the first project by introducing more complex challenges involving automation, data analysis, or process control. Students often collaborate with industry partners to ensure that their solutions are relevant to current market needs. The emphasis is on developing critical thinking skills and demonstrating proficiency in using advanced tools and methodologies.
Mini Projects III (Semester 5) requires students to work on interdisciplinary projects that combine multiple areas of knowledge. These projects often involve working with real datasets, conducting experiments, and presenting findings to peers and faculty members. The goal is to develop a deeper understanding of how different engineering disciplines interact in industrial settings.
The final-year capstone project represents the culmination of the student's learning journey. Students select a topic that aligns with their interests and career goals, often in collaboration with industry partners or research labs. The project involves extensive research, data collection, analysis, and development of a comprehensive solution or innovation. Faculty mentors provide guidance throughout the process, helping students refine their ideas and ensure quality outcomes.
The evaluation criteria for all projects include technical depth, originality, clarity of presentation, teamwork, and adherence to industry standards. Students are encouraged to present their work at conferences, competitions, or exhibitions, providing them with opportunities to network and gain recognition for their efforts. This approach not only enhances learning but also builds confidence and prepares students for professional environments where problem-solving and innovation are highly valued.