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Fees
₹1,20,000
Placement
93.0%
Avg Package
₹4,50,000
Highest Package
₹8,50,000
Fees
₹1,20,000
Placement
93.0%
Avg Package
₹4,50,000
Highest Package
₹8,50,000
Seats
180
Students
200
Seats
180
Students
200
| Semester | Course Code | Full Title | Credit (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | MAT-101 | Engineering Mathematics I | 3-1-0-4 | - |
| 1 | PHY-101 | Physics for Engineering | 3-1-0-4 | - |
| 1 | CHE-101 | Chemistry for Engineers | 3-1-0-4 | - |
| 1 | BME-101 | Basic Mechanical Engineering | 3-1-0-4 | - |
| 1 | EEE-101 | Electrical Technology | 3-1-0-4 | - |
| 1 | ENG-101 | English for Communication | 2-0-0-2 | - |
| 1 | LIT-101 | Introduction to Information Technology | 2-0-2-3 | - |
| 1 | MAT-102 | Engineering Mathematics II | 3-1-0-4 | MAT-101 |
| 2 | MAT-201 | Advanced Engineering Mathematics | 3-1-0-4 | MAT-102 |
| 2 | MEC-201 | Mechanics of Materials | 3-1-0-4 | BME-101 |
| 2 | ELE-201 | Electrical Circuits and Machines | 3-1-0-4 | EEE-101 |
| 2 | MEC-202 | Thermodynamics | 3-1-0-4 | BME-101 |
| 2 | MAT-202 | Probability and Statistics | 3-1-0-4 | MAT-102 |
| 2 | PRO-201 | Production Technology | 3-1-0-4 | BME-101 |
| 2 | LIT-201 | Computer Programming and Applications | 2-0-2-3 | LIT-101 |
| 3 | MAT-301 | Numerical Methods and Optimization | 3-1-0-4 | MAT-202 |
| 3 | MED-301 | Mechanical Engineering Materials | 3-1-0-4 | BME-101 |
| 3 | ELE-301 | Electronic Devices and Circuits | 3-1-0-4 | ELE-201 |
| 3 | MED-302 | Mechanical Systems Design | 3-1-0-4 | MED-301 |
| 3 | IND-301 | Industrial Instrumentation and Control | 3-1-0-4 | ELE-201 |
| 3 | MAT-302 | Operations Research | 3-1-0-4 | MAT-202 |
| 3 | DEE-301 | Departmental Elective I | 3-1-0-4 | - |
| 4 | MED-401 | Reliability Engineering | 3-1-0-4 | MED-302 |
| 4 | ELE-401 | Power Systems and Drives | 3-1-0-4 | ELE-201 |
| 4 | MED-402 | Failure Analysis and Root Cause Investigation | 3-1-0-4 | MED-301 |
| 4 | IND-401 | Industrial Automation and PLCs | 3-1-0-4 | IND-301 |
| 4 | MAT-401 | Data Analytics for Engineering | 3-1-0-4 | MAT-202 |
| 4 | DEE-401 | Departmental Elective II | 3-1-0-4 | - |
| 5 | MED-501 | Predictive Maintenance Techniques | 3-1-0-4 | MED-401 |
| 5 | IND-501 | Asset Management and Lifecycle Planning | 3-1-0-4 | IND-301 |
| 5 | MED-502 | Advanced Maintenance Diagnostics | 3-1-0-4 | MED-402 |
| 5 | IND-502 | Computer-Aided Maintenance Systems (CMMS) | 3-1-0-4 | IND-401 |
| 5 | MAT-501 | Machine Learning for Engineering | 3-1-0-4 | MAT-401 |
| 5 | DEE-501 | Departmental Elective III | 3-1-0-4 | - |
| 6 | MED-601 | Industrial Safety and Risk Management | 3-1-0-4 | MED-502 |
| 6 | IND-601 | Sustainable Manufacturing Practices | 3-1-0-4 | IND-501 |
| 6 | MED-602 | Advanced Materials for Maintenance | 3-1-0-4 | MED-502 |
| 6 | IND-602 | Digital Twins and Simulation Tools | 3-1-0-4 | IND-502 |
| 6 | MAT-601 | Big Data Analytics in Maintenance | 3-1-0-4 | MAT-501 |
| 6 | DEE-601 | Departmental Elective IV | 3-1-0-4 | - |
| 7 | IND-701 | Capstone Project I | 4-0-2-6 | All previous semesters |
| 7 | MED-701 | Research Methodology and Ethics | 3-1-0-4 | - |
| 7 | DEE-701 | Industry Internship | 2-0-0-2 | - |
| 8 | IND-801 | Capstone Project II | 4-0-2-6 | IND-701 |
| 8 | MED-801 | Final Thesis and Presentation | 3-0-0-3 | IND-701 |
| 8 | DEE-801 | Advanced Elective V | 3-1-0-4 | - |
The curriculum is designed to progressively build upon foundational knowledge while introducing students to emerging technologies and industry practices. The project-based learning approach ensures that theoretical concepts are applied in real-world scenarios, fostering critical thinking and problem-solving skills.
Predictive Maintenance Techniques: This course explores the use of data analytics, machine learning algorithms, and sensor technologies to predict equipment failures. Students learn how to implement predictive models for various industrial systems and interpret real-time data streams.
Industrial Automation and PLCs: Designed to equip students with hands-on experience in programming and configuring programmable logic controllers (PLCs). The course covers ladder logic, HMI design, and integration of automation systems into existing maintenance frameworks.
Asset Management and Lifecycle Planning: Focuses on strategic approaches to managing industrial assets from acquisition through decommissioning. Topics include cost-benefit analysis, risk assessment, and lifecycle optimization strategies for maximum return on investment.
Advanced Maintenance Diagnostics: Introduces advanced diagnostic techniques used in modern maintenance environments. Students learn how to troubleshoot complex systems using specialized tools and software, and develop skills in root cause analysis.
Digital Twins and Simulation Tools: Explores the concept of digital twins and their application in modeling real-world assets. Students gain experience with simulation software to test maintenance strategies without disrupting actual operations.
Big Data Analytics in Maintenance: Teaches students how to collect, process, and analyze large volumes of maintenance data using modern tools like Python, R, and SQL. The course emphasizes extracting actionable insights for performance improvement.
Sustainable Manufacturing Practices: Examines sustainable approaches to industrial maintenance that reduce environmental impact while maintaining operational efficiency. Topics include waste minimization, energy conservation, and circular economy principles.
Advanced Materials for Maintenance: Provides an overview of advanced materials used in industrial applications, including composites, ceramics, and smart coatings. Students learn about material selection criteria and their implications for maintenance strategies.
Industrial Safety and Risk Management: Covers safety protocols, hazard identification, and risk mitigation techniques specific to industrial environments. Students are trained in emergency response planning and regulatory compliance.
Mechanical Systems Design: Focuses on the principles of mechanical system design with an emphasis on maintainability and reliability. Students learn how to design systems that minimize downtime and maximize operational lifespan.
The department places a strong emphasis on project-based learning, believing that real-world experience is essential for developing competent engineers. Students begin working on mini-projects in their third year, tackling challenges posed by industry partners or academic faculty. These projects are evaluated based on innovation, technical execution, teamwork, and presentation quality.
The final-year capstone project is a comprehensive endeavor where students select a topic related to industrial maintenance and work under the supervision of a faculty mentor. The project involves extensive research, design, implementation, and documentation phases. Students present their findings to a panel of experts from academia and industry, ensuring that they are well-prepared for professional environments.
Faculty mentors are selected based on their expertise in specific areas of industrial maintenance. They guide students through the entire project lifecycle, providing technical support, feedback, and career advice. The selection process ensures that each student is matched with a mentor whose interests align with their chosen topic.
