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:

1. 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.
2. 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.
3. 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.