Comprehensive Course Listing Across All 8 Semesters
| Semester | Course Code | Full Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| 1 | IM-101 | Engineering Mathematics I | 3-1-0-4 | - |
| 1 | IM-102 | Applied Physics | 3-1-0-4 | - |
| 1 | IM-103 | Basic Electrical Engineering | 3-1-0-4 | - |
| 1 | IM-104 | Engineering Graphics | 2-1-0-3 | - |
| 1 | IM-105 | Workshop Practice | 0-2-0-2 | - |
| 1 | IM-106 | Introduction to Industrial Maintenance | 2-0-0-2 | - |
| 2 | IM-201 | Engineering Mathematics II | 3-1-0-4 | IM-101 |
| 2 | IM-202 | Chemistry | 3-1-0-4 | - |
| 2 | IM-203 | Electrical Machines | 3-1-0-4 | IM-103 |
| 2 | IM-204 | Mechanics of Materials | 3-1-0-4 | - |
| 2 | IM-205 | Computer Programming | 3-1-0-4 | - |
| 2 | IM-206 | Industrial Safety & Health | 2-0-0-2 | - |
| 3 | IM-301 | Engineering Mathematics III | 3-1-0-4 | IM-201 |
| 3 | IM-302 | Fluid Mechanics | 3-1-0-4 | IM-204 |
| 3 | IM-303 | Mechanics of Machines | 3-1-0-4 | IM-204 |
| 3 | IM-304 | Thermodynamics | 3-1-0-4 | - |
| 3 | IM-305 | Control Systems | 3-1-0-4 | - |
| 3 | IM-306 | Materials Science & Metallurgy | 3-1-0-4 | - |
| 4 | IM-401 | Engineering Mathematics IV | 3-1-0-4 | IM-301 |
| 4 | IM-402 | Industrial Automation | 3-1-0-4 | IM-305 |
| 4 | IM-403 | Maintenance Engineering | 3-1-0-4 | - |
| 4 | IM-404 | Computer Aided Design & Drafting | 2-1-0-3 | IM-205 |
| 4 | IM-405 | Industrial Electronics | 3-1-0-4 | IM-203 |
| 4 | IM-406 | Quality Management | 2-0-0-2 | - |
| 5 | IM-501 | Advanced Control Systems | 3-1-0-4 | IM-401 |
| 5 | IM-502 | Predictive Maintenance | 3-1-0-4 | IM-403 |
| 5 | IM-503 | Robotics & Automation | 3-1-0-4 | IM-402 |
| 5 | IM-504 | Asset Management | 3-1-0-4 | - |
| 5 | IM-505 | Data Analytics for Maintenance | 3-1-0-4 | IM-401 |
| 5 | IM-506 | Industrial Cybersecurity | 3-1-0-4 | - |
| 6 | IM-601 | Power Plant Engineering | 3-1-0-4 | IM-304 |
| 6 | IM-602 | Sustainable Maintenance Practices | 3-1-0-4 | - |
| 6 | IM-603 | Industrial Safety & Risk Assessment | 3-1-0-4 | IM-206 |
| 6 | IM-604 | Project Management | 3-1-0-4 | - |
| 6 | IM-605 | Research Methodology | 2-1-0-3 | - |
| 6 | IM-606 | Internship I | 0-0-2-2 | - |
| 7 | IM-701 | Advanced Maintenance Technologies | 3-1-0-4 | - |
| 7 | IM-702 | Industrial IoT & Smart Systems | 3-1-0-4 | - |
| 7 | IM-703 | Human Factors in Maintenance | 2-1-0-3 | - |
| 7 | IM-704 | Final Year Project I | 0-0-4-6 | - |
| 7 | IM-705 | Maintenance Optimization Techniques | 3-1-0-4 | - |
| 7 | IM-706 | Entrepreneurship & Innovation | 2-1-0-3 | - |
| 8 | IM-801 | Final Year Project II | 0-0-6-8 | - |
| 8 | IM-802 | Capstone Presentation | 0-0-2-2 | - |
| 8 | IM-803 | Internship II | 0-0-4-4 | - |
Detailed Course Descriptions for Advanced Departmental Electives
Predictive Maintenance: This course focuses on using advanced analytics, machine learning algorithms, and sensor technologies to predict equipment failures before they occur. Students will learn about condition monitoring systems, fault diagnosis techniques, and data-driven decision-making frameworks that are essential for modern maintenance strategies.
Industrial Robotics & Automation: Designed for students interested in robotics integration within industrial environments, this course covers robot programming, PLC interfacing, motion control systems, and collaborative automation technologies. It includes hands-on lab sessions with industrial-grade robots and simulation software.
Data Analytics for Maintenance: This elective teaches students how to analyze large volumes of maintenance data using statistical methods, Python libraries, and visualization tools. Topics include time series forecasting, root cause analysis, and performance optimization techniques based on historical maintenance records.
Asset Management: Focused on optimizing asset lifecycle costs through strategic planning, risk assessment, and performance metrics evaluation. Students will explore various asset management methodologies such as Total Cost of Ownership (TCO), Life Cycle Assessment (LCA), and Return on Investment (ROI) calculations.
Industrial Cybersecurity: As industries become increasingly digital, cybersecurity becomes critical for protecting sensitive data and maintaining operational integrity. This course covers network security protocols, threat identification, incident response strategies, and compliance with international standards like ISO/IEC 27001 and NIST.
Sustainable Maintenance Practices: Emphasizing environmental responsibility, this course explores green maintenance solutions such as waste minimization, energy-efficient operations, and sustainable material usage. It also covers lifecycle assessment methods for evaluating the environmental impact of maintenance activities.
Advanced Control Systems: Building upon foundational knowledge in control theory, this advanced elective delves into nonlinear systems, robust control design, and adaptive control algorithms. Students will gain proficiency in MATLAB/Simulink for simulation and real-time control system implementation.
Maintenance Optimization Techniques: This course introduces optimization techniques specifically tailored for maintenance scheduling and resource allocation problems. It covers linear programming, integer programming, heuristic methods, and metaheuristics such as genetic algorithms and particle swarm optimization applied to maintenance contexts.
Industrial IoT & Smart Systems: Exploring the integration of Internet of Things (IoT) technologies into industrial maintenance environments, this course discusses sensor networks, cloud computing platforms, edge analytics, and real-time monitoring systems. Students will work on practical projects involving embedded devices and data transmission protocols.
Human Factors in Maintenance: Recognizing that human error is a significant contributor to industrial incidents, this course examines cognitive biases, ergonomics, training effectiveness, and communication strategies within maintenance teams. It includes workshops on safety culture development and team dynamics improvement.
Project-Based Learning Philosophy
The department believes in experiential learning as the cornerstone of academic excellence. Our approach to project-based learning is structured around three distinct phases:
- Problem Identification & Analysis: Students begin by identifying real-world problems within their chosen specialization area. They conduct literature reviews, gather data from industry sources, and define clear objectives for their projects.
- Design & Implementation: In this phase, students develop conceptual designs, create prototypes, perform simulations, and implement solutions using available tools and technologies. Regular mentorship sessions ensure progress alignment with project goals.
- Evaluation & Presentation: The final stage involves evaluating the effectiveness of implemented solutions through testing, analysis, and feedback collection. Students present their findings to faculty panels and industry experts, refining their communication and presentation skills.
Mini-projects are assigned during semesters 3 and 5, each lasting approximately 6 weeks and carrying a credit value of 2 credits. These projects allow students to explore specific aspects of industrial maintenance in depth while building teamwork and project management capabilities.
The final-year thesis/capstone project spans the entire eighth semester and carries 8 credits. Students are encouraged to collaborate with industry partners or faculty members on original research topics that contribute to current knowledge in the field. The project must include a literature review, methodology, experimental design, data analysis, and a comprehensive report.
Faculty mentors play a crucial role in guiding students throughout their academic journey. Each student is paired with a mentor based on their interests and career aspirations. Mentors provide regular feedback, suggest resources, facilitate networking opportunities, and support professional development activities.