Comprehensive Course Structure
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| Semester I | EN101 | Engineering Mathematics I | 3-1-0-4 | - |
| PH101 | Physics for Engineers | 3-1-0-4 | - | |
| CH101 | Chemistry for Engineers | 3-1-0-4 | - | |
| EC101 | Electrical Engineering Fundamentals | 3-1-0-4 | - | |
| ME101 | Mechanics of Solids | 3-1-0-4 | - | |
| CS101 | Computer Programming | 3-1-0-4 | - | |
| EP101 | Engineering Drawing and Graphics | 2-1-0-3 | - | |
| EG101 | Introduction to Engineering | 2-0-0-2 | - | |
| ES101 | Environmental Science and Sustainability | 3-0-0-3 | - | |
| EN102 | Engineering Mathematics II | 3-1-0-4 | EN101 | |
| PH102 | Thermodynamics and Heat Transfer | 3-1-0-4 | PH101 | |
| EC102 | Electronics Circuits | 3-1-0-4 | EC101 | |
| ME102 | Fluid Mechanics and Hydraulic Machines | 3-1-0-4 | ME101 | |
| Semester II | EN201 | Probability and Statistics | 3-1-0-4 | EN102 |
| PH201 | Electromagnetic Fields and Waves | 3-1-0-4 | PH102 | |
| CH201 | Chemical Engineering Principles | 3-1-0-4 | CH101 | |
| EC201 | Digital Electronics and Logic Design | 3-1-0-4 | EC102 | |
| ME201 | Mechanical Measurements and Instrumentation | 3-1-0-4 | ME102 | |
| CS201 | Data Structures and Algorithms | 3-1-0-4 | CS101 | |
| EP201 | Engineering Economics and Management | 3-0-0-3 | - | |
| EG201 | Professional Ethics and Values | 2-0-0-2 | - | |
| EN202 | Linear Algebra and Differential Equations | 3-1-0-4 | EN201 | |
| PH202 | Optics and Modern Physics | 3-1-0-4 | PH201 | |
| EC202 | Analog Electronics | 3-1-0-4 | EC201 | |
| ME202 | Mechanical Design and Drafting | 3-1-0-4 | ME201 | |
| EN203 | Complex Variables and Transforms | 3-1-0-4 | EN202 | |
| Semester III | ME301 | Mechanics of Materials | 3-1-0-4 | ME202 |
| EC301 | Signals and Systems | 3-1-0-4 | EC202 | |
| CS301 | Database Management Systems | 3-1-0-4 | CS201 | |
| EN301 | Control Systems Engineering | 3-1-0-4 | EN203 | |
| PH301 | Quantum Physics and Applications | 3-1-0-4 | PH202 | |
| ME302 | Thermal Engineering | 3-1-0-4 | ME202 | |
| EC302 | Communication Systems | 3-1-0-4 | EC301 | |
| CS302 | Operating Systems | 3-1-0-4 | CS301 | |
| EN302 | Digital Signal Processing | 3-1-0-4 | EN301 | |
| PH302 | Nuclear Physics and Applications | 3-1-0-4 | PH301 | |
| ME303 | Mechanical Vibrations | 3-1-0-4 | ME302 | |
| EC303 | Microprocessors and Microcontrollers | 3-1-0-4 | EC302 | |
| CS303 | Software Engineering | 3-1-0-4 | CS302 | |
| Semester IV | ME401 | Design of Machine Elements | 3-1-0-4 | ME303 |
| EC401 | Computer Networks | 3-1-0-4 | EC303 | |
| CS401 | Artificial Intelligence | 3-1-0-4 | CS303 | |
| EN401 | Process Control Systems | 3-1-0-4 | EN302 | |
| PH401 | Advanced Physics Concepts | 3-1-0-4 | PH302 | |
| ME402 | Industrial Safety and Risk Management | 3-1-0-4 | ME303 | |
| EC402 | Embedded Systems | 3-1-0-4 | EC401 | |
| CS402 | Machine Learning | 3-1-0-4 | CS401 | |
| EN402 | Industrial Automation | 3-1-0-4 | EN401 | |
| PH402 | Optical and Quantum Technologies | 3-1-0-4 | PH401 | |
| ME403 | Maintenance Engineering | 3-1-0-4 | ME402 | |
| EC403 | Power Electronics and Drives | 3-1-0-4 | EC402 | |
| CS403 | Big Data Analytics | 3-1-0-4 | CS402 | |
| Semester V | ME501 | Predictive Maintenance using AI/ML | 3-1-0-4 | ME403 |
| EC501 | Industrial IoT and Sensor Networks | 3-1-0-4 | EC403 | |
| CS501 | Advanced Data Analytics for Maintenance | 3-1-0-4 | CS403 | |
| EN501 | Smart Manufacturing Systems | 3-1-0-4 | EN402 | |
| PH501 | Renewable Energy Technologies | 3-1-0-4 | PH402 | |
| ME502 | Energy Auditing and Management | 3-1-0-4 | ME403 | |
| EC502 | Robotics and Automation | 3-1-0-4 | EC501 | |
| CS502 | Cloud Computing for Industrial Applications | 3-1-0-4 | CS501 | |
| EN502 | Digital Twin Technology | 3-1-0-4 | EN501 | |
| PH502 | Sustainable Energy Practices | 3-1-0-4 | PH501 | |
| ME503 | Advanced Maintenance Techniques | 3-1-0-4 | ME502 | |
| EC503 | Industrial Automation and Control Systems | 3-1-0-4 | EC502 | |
| CS503 | Blockchain Applications in Industry | 3-1-0-4 | CS502 | |
| Semester VI | ME601 | Advanced Predictive Modeling for Maintenance | 3-1-0-4 | ME503 |
| EC601 | Edge Computing in Industrial Environments | 3-1-0-4 | EC503 | |
| CS601 | Machine Learning for Industrial Applications | 3-1-0-4 | CS503 | |
| EN601 | Industry 4.0 Integration | 3-1-0-4 | EN502 | |
| PH601 | Quantum Technologies in Industry | 3-1-0-4 | PH502 | |
| ME602 | Industrial Safety and Compliance | 3-1-0-4 | ME503 | |
| EC602 | Advanced Control Systems for Manufacturing | 3-1-0-4 | EC601 | |
| CS602 | Data Science and Analytics for Maintenance | 3-1-0-4 | CS601 | |
| EN602 | Cybersecurity in Industrial Environments | 3-1-0-4 | EN601 | |
| PH602 | Nanotechnology and Its Applications | 3-1-0-4 | PH601 | |
| ME603 | Maintenance Optimization and Cost Analysis | 3-1-0-4 | ME602 | |
| EC603 | Advanced IoT Implementations | 3-1-0-4 | EC602 | |
| CS603 | Software Engineering for Smart Systems | 3-1-0-4 | CS602 | |
| Semester VII | ME701 | Capstone Project - Industrial Maintenance | 3-0-0-6 | - |
| EC701 | Research Methodology and Project Planning | 2-0-0-3 | - | |
| CS701 | Capstone Thesis Writing | 2-0-0-3 | - | |
| EN701 | Final Year Project - Industry Collaboration | 4-0-0-8 | - | |
| PH701 | Capstone Research Paper Presentation | 2-0-0-3 | - | |
| ME702 | Mini Project - Maintenance Innovation | 2-0-0-4 | - | |
| EC702 | Project Supervision and Evaluation | 1-0-0-2 | - | |
| Semester VIII | ME801 | Internship and Practical Exposure | 0-0-6-12 | - |
| EC801 | Capstone Presentation and Defense | 2-0-0-4 | - | |
| CS801 | Advanced Capstone Research | 3-0-0-6 | - | |
| EN801 | Final Project Implementation | 4-0-0-8 | - | |
| PH801 | Research Synthesis and Publication | 2-0-0-3 | - | |
| ME802 | Industry Project Finalization | 3-0-0-6 | - | |
| EC802 | Final Review and Grading | 1-0-0-2 | - |
Detailed Description of Advanced Departmental Electives
Departmental electives form a crucial part of the Industrial Maintenance program, offering students specialized knowledge and advanced skills in emerging fields. These courses are designed to deepen understanding and enhance career prospects by aligning with current industry trends and demands.
Predictive Maintenance using AI/ML
This course explores how machine learning algorithms can be applied to predict equipment failures before they occur. Students learn about data collection, preprocessing, feature extraction, and model selection techniques tailored for industrial environments. Topics include regression analysis, classification models, neural networks, deep learning architectures, and time series forecasting methods.
Industrial IoT and Sensor Networks
This course introduces students to the architecture and implementation of Internet of Things (IoT) solutions in industrial settings. It covers sensor technologies, communication protocols, network topologies, edge computing, data fusion techniques, and real-time monitoring systems. Students also learn about security challenges and best practices for deploying IoT infrastructure in manufacturing environments.
Advanced Data Analytics for Maintenance
Building upon foundational analytics concepts, this course focuses on advanced statistical methods and tools used in industrial maintenance optimization. It includes exploratory data analysis, hypothesis testing, regression modeling, clustering algorithms, decision trees, and ensemble methods. The course emphasizes practical applications using industry-standard software like Python, R, and MATLAB.
Smart Manufacturing Systems
This elective delves into the integration of digital technologies in manufacturing processes. It covers topics such as automation systems, robotic process automation (RPA), cyber-physical systems, smart factories, digital twin modeling, and Industry 4.0 principles. Students gain hands-on experience with simulation tools and real-world case studies from leading manufacturers.
Renewable Energy Technologies
The course explores various renewable energy sources and their applications in industrial maintenance contexts. It covers solar power systems, wind turbines, hydroelectric plants, and biomass technologies. Students learn about energy storage solutions, grid integration challenges, maintenance practices for renewable assets, and environmental impact assessments.
Energy Auditing and Management
This course provides a comprehensive overview of energy auditing techniques and management strategies in industrial settings. It includes energy consumption analysis, benchmarking methods, energy efficiency improvements, carbon footprint reduction, and sustainability reporting frameworks. Students are trained to conduct audits using industry-standard tools and interpret results for strategic decision-making.
Robotics and Automation
This course covers the design, implementation, and maintenance of robotic systems in industrial environments. It includes robot kinematics, control systems, programming languages, sensor integration, machine vision, and collaborative robotics (cobots). Students work on hands-on projects involving industrial robots and automation solutions.
Digital Twin Technology
Students learn how to create virtual replicas of physical assets using digital twin technology. The course covers modeling techniques, simulation environments, real-time data integration, predictive analytics, and visualization tools. It emphasizes practical applications in manufacturing, energy, transportation, and other sectors.
Cybersecurity in Industrial Environments
This course addresses the growing threat landscape in industrial cybersecurity. It covers network security, endpoint protection, intrusion detection systems, vulnerability assessments, risk management, compliance standards (e.g., NIST, ISO 27001), and incident response procedures. Students are exposed to real-world scenarios through simulations and case studies.
Industry 4.0 Integration
This course explores how Industry 4.0 technologies such as artificial intelligence, IoT, robotics, and big data analytics can be integrated into traditional manufacturing processes. It includes topics like smart production lines, predictive maintenance systems, supply chain digitization, digital transformation strategies, and future trends in industrial innovation.
Project-Based Learning Philosophy
The department strongly believes in project-based learning as a means to develop critical thinking, problem-solving, and collaborative skills among students. The curriculum includes mandatory mini-projects in earlier semesters and a final-year capstone project that integrates all learned concepts.
Mini Projects
Mini projects are assigned during the second and third years to provide practical exposure to real-world problems. These projects typically last 3-4 months and involve small teams of 2-4 students working under faculty supervision. The evaluation criteria include project proposal, implementation, documentation, presentation, and peer review.
Final-Year Capstone Project
The final-year capstone project is a significant component of the program that allows students to demonstrate their mastery of industrial maintenance concepts. Students choose projects aligned with their interests or industry needs and work closely with faculty mentors throughout the process. The project involves research, design, prototyping, testing, documentation, and presentation components.
Project Selection Process
Students can select projects based on various categories including:
- Research-based projects aligned with faculty expertise
- Industry-sponsored projects addressing real challenges
- Innovation and entrepreneurship projects
- Interdisciplinary collaborative projects
The selection process involves proposal submissions, mentor matching, and approval by the departmental committee. Faculty members play a crucial role in guiding students through each phase of their project journey.