Comprehensive Course Structure for Industrial Maintenance
| Semester | Course Code | Course Title | Credit (L-T-P-C) | Pre-requisite |
|---|---|---|---|---|
| 1 | EME-101 | Engineering Mathematics I | 3-1-0-4 | - |
| 1 | EME-102 | Engineering Physics | 3-1-0-4 | - |
| 1 | EME-103 | Engineering Chemistry | 3-1-0-4 | - |
| 1 | EME-104 | Engineering Drawing | 2-1-0-3 | - |
| 1 | EME-105 | Basic Electrical Engineering | 3-1-0-4 | - |
| 1 | EME-106 | Workshop Practice | 2-0-4-3 | - |
| 2 | EME-201 | Engineering Mathematics II | 3-1-0-4 | EME-101 |
| 2 | EME-202 | Mechanics of Solids | 3-1-0-4 | EME-101 |
| 2 | EME-203 | Thermodynamics | 3-1-0-4 | EME-102 |
| 2 | EME-204 | Materials Science | 3-1-0-4 | EME-103 |
| 2 | EME-205 | Fluid Mechanics | 3-1-0-4 | EME-101 |
| 2 | EME-206 | Electrical Circuits and Networks | 3-1-0-4 | EME-105 |
| 3 | EME-301 | Mechanical Measurements and Instrumentation | 3-1-0-4 | EME-202, EME-205 |
| 3 | EME-302 | Mechanical Engineering Materials | 3-1-0-4 | EME-204 |
| 3 | EME-303 | Manufacturing Processes | 3-1-0-4 | EME-202, EME-204 |
| 3 | EME-304 | Machine Design I | 3-1-0-4 | EME-202, EME-205 |
| 3 | EME-305 | Industrial Safety | 3-1-0-4 | EME-201, EME-202 |
| 3 | EME-306 | Electrical Machines and Drives | 3-1-0-4 | EME-206 |
| 4 | EME-401 | Maintenance Planning and Scheduling | 3-1-0-4 | EME-301, EME-305 |
| 4 | EME-402 | Predictive Maintenance Techniques | 3-1-0-4 | EME-301, EME-306 |
| 4 | EME-403 | Industrial Automation Systems | 3-1-0-4 | EME-306 |
| 4 | EME-404 | Quality Control and Assurance | 3-1-0-4 | EME-303, EME-305 |
| 4 | EME-405 | Advanced Manufacturing Processes | 3-1-0-4 | EME-303 |
| 4 | EME-406 | Computer Aided Design and Manufacturing | 3-1-0-4 | EME-301, EME-304 |
| 5 | EME-501 | Condition Monitoring Techniques | 3-1-0-4 | EME-402, EME-403 |
| 5 | EME-502 | Renewable Energy Systems Maintenance | 3-1-0-4 | EME-403, EME-406 |
| 5 | EME-503 | Industrial Robotics and Control | 3-1-0-4 | EME-403, EME-406 |
| 5 | EME-504 | Data Analytics for Predictive Maintenance | 3-1-0-4 | EME-402, EME-406 |
| 5 | EME-505 | Supply Chain Management in Maintenance | 3-1-0-4 | EME-401, EME-404 |
| 5 | EME-506 | Project Management and Risk Analysis | 3-1-0-4 | EME-401, EME-404 |
| 6 | EME-601 | Advanced Maintenance Engineering | 3-1-0-4 | EME-501, EME-502 |
| 6 | EME-602 | Smart Manufacturing Systems | 3-1-0-4 | EME-503, EME-504 |
| 6 | EME-603 | Energy Efficiency in Industrial Operations | 3-1-0-4 | EME-502, EME-504 |
| 6 | EME-604 | Sustainable Maintenance Practices | 3-1-0-4 | EME-502, EME-503 |
| 6 | EME-605 | Human Factors in Industrial Maintenance | 3-1-0-4 | EME-505, EME-506 |
| 6 | EME-606 | Industrial Internship Program | 2-0-8-6 | All previous semesters |
| 7 | EME-701 | Capstone Project I | 3-1-0-4 | EME-601, EME-602 |
| 7 | EME-702 | Capstone Project II | 3-1-0-4 | EME-701 |
| 7 | EME-703 | Research Methodology | 3-1-0-4 | EME-602, EME-603 |
| 7 | EME-704 | Advanced Topics in Maintenance Engineering | 3-1-0-4 | EME-601, EME-602 |
| 7 | EME-705 | Entrepreneurship in Industrial Maintenance | 3-1-0-4 | EME-605, EME-606 |
| 7 | EME-706 | Final Year Project | 2-0-12-8 | All previous semesters |
| 8 | EME-801 | Final Project Presentation and Viva | 2-0-0-2 | EME-706 |
Detailed Course Descriptions for Departmental Electives
The department offers a wide array of advanced elective courses designed to provide students with specialized knowledge and skills in various domains of industrial maintenance. These courses are tailored to meet the evolving demands of industry and prepare students for leadership roles in their chosen fields.
1. Condition Monitoring Techniques
This course focuses on modern techniques used to monitor the health and performance of industrial equipment. Students learn about vibration analysis, thermal imaging, oil analysis, ultrasonic testing, and other non-destructive methods. The course combines theoretical concepts with practical applications using industry-standard tools and software. Learning objectives include identifying failure modes, implementing monitoring strategies, and interpreting diagnostic data for predictive maintenance.
2. Renewable Energy Systems Maintenance
This elective introduces students to the maintenance of renewable energy systems such as solar panels, wind turbines, hydroelectric generators, and geothermal systems. The course covers installation procedures, operational principles, troubleshooting techniques, and safety protocols specific to renewable energy infrastructure. Students gain hands-on experience with real-world systems and learn about emerging technologies in sustainable energy production.
3. Industrial Robotics and Control
This advanced course explores the design, programming, and maintenance of industrial robotic systems used in manufacturing environments. Topics include robot kinematics, control algorithms, sensor integration, safety standards, and automation planning. Students work with programmable robots and develop skills in motion control, path planning, and system integration. The course prepares graduates for roles in robotics maintenance and automation engineering.
4. Data Analytics for Predictive Maintenance
This course teaches students how to apply data analytics and machine learning techniques to predict equipment failures and optimize maintenance schedules. Students learn about data preprocessing, feature extraction, model selection, and performance evaluation. The curriculum includes practical sessions on tools like Python, R, TensorFlow, and scikit-learn, along with case studies from real industrial environments.
5. Supply Chain Management in Maintenance
This elective examines the role of supply chain logistics in industrial maintenance operations. Students learn about procurement strategies, inventory management, vendor evaluation, and cost optimization techniques. The course explores how effective supply chain practices can reduce downtime, improve asset reliability, and enhance overall operational efficiency.
6. Project Management and Risk Analysis
This course equips students with project management skills essential for large-scale maintenance initiatives. Topics include project planning, resource allocation, risk assessment, quality control, and stakeholder communication. Students engage in simulations and case studies to understand the complexities of managing industrial projects and learn best practices from industry experts.
7. Advanced Maintenance Engineering
This advanced course delves into cutting-edge maintenance methodologies and technologies that are transforming industrial operations. It covers topics such as digital twin technology, augmented reality in maintenance, blockchain for asset tracking, and artificial intelligence applications in predictive maintenance. Students explore emerging trends and develop critical thinking skills to adapt to future industry changes.
8. Smart Manufacturing Systems
This course explores the integration of IoT, AI, and automation in manufacturing environments. Students learn about smart sensors, edge computing, real-time data analytics, and industrial communication protocols. The course emphasizes how these technologies can be leveraged to enhance maintenance processes and achieve operational excellence.
9. Energy Efficiency in Industrial Operations
This elective focuses on strategies for improving energy efficiency in industrial settings. Students study energy auditing techniques, heat recovery systems, motor efficiency optimization, and power quality management. The course includes field visits to industrial facilities where students observe and analyze actual energy consumption patterns.
10. Sustainable Maintenance Practices
This course addresses the growing importance of sustainability in maintenance operations. Topics include eco-friendly lubricants, waste reduction strategies, circular economy principles, and environmental compliance requirements. Students learn how to implement sustainable practices that minimize environmental impact while maintaining operational effectiveness.
11. Human Factors in Industrial Maintenance
This course examines the role of human behavior and psychology in maintenance operations. Students explore topics such as cognitive biases, decision-making under stress, team dynamics, and safety culture development. The course emphasizes how understanding human factors can lead to improved maintenance outcomes and workplace safety.
Project-Based Learning Philosophy
The department strongly believes that project-based learning is the most effective way to prepare students for real-world challenges in industrial maintenance. Our approach integrates academic knowledge with practical application, enabling students to develop both technical competence and professional skills.
Mandatory Mini-Projects
Throughout the program, students undertake mandatory mini-projects that reinforce core concepts learned in class. These projects are typically completed in teams of 3-5 members and involve designing, implementing, and evaluating solutions to specific maintenance problems. Each project is supervised by a faculty member with expertise in the relevant domain.
Final-Year Thesis/Capstone Project
The final-year capstone project represents the culmination of students' learning experience. Students select projects based on their interests and career goals, often in collaboration with industry partners or research laboratories. The project involves extensive research, design work, prototype development, testing, and documentation.
Project Selection Process
Students begin selecting their final-year projects in the sixth semester by submitting proposals to faculty mentors. Projects are assigned based on faculty expertise, student interests, industry relevance, and resource availability. The selection process includes an evaluation of proposal quality, feasibility assessment, and mentor-student matching.
Evaluation Criteria
Projects are evaluated using a comprehensive rubric that considers technical merit, innovation, presentation quality, teamwork, and adherence to deadlines. Students must present their work to a panel of faculty members and industry experts, demonstrating their ability to communicate complex ideas effectively.