Course Structure and Core Curriculum
The Industrial Maintenance program at Phonics Group Of Institutions is structured over eight semesters, with a blend of foundational sciences, engineering fundamentals, departmental electives, and specialized courses tailored to meet industry demands. Each semester carries a credit load designed to ensure comprehensive understanding while maintaining flexibility for specialization.
| Semester | Course Code | Course Title | Credit (L-T-P-C) | Prerequisite |
|---|---|---|---|---|
| I | ENG101 | Engineering Mathematics I | 3-0-0-3 | - |
| I | PHY101 | Physics for Engineers | 3-0-0-3 | - |
| I | CHM101 | Chemistry | 3-0-0-3 | - |
| I | MEC101 | Basic Electrical Engineering | 3-0-0-3 | - |
| I | CSE101 | Introduction to Industrial Systems | 2-0-0-2 | - |
| I | MAT101 | Engineering Drawing | 2-0-0-2 | - |
| I | LAB101 | Basic Electrical Lab | 0-0-3-1 | - |
| II | ENG201 | Engineering Mathematics II | 3-0-0-3 | ENG101 |
| II | MEC201 | Mechanics of Materials | 3-0-0-3 | - |
| II | CSE201 | Control Systems | 3-0-0-3 | - |
| II | MAT201 | Fluid Mechanics | 3-0-0-3 | - |
| II | LAB201 | Thermodynamics Lab | 0-0-3-1 | - |
| III | MAT301 | Mechanical Design | 3-0-0-3 | MEC201 |
| III | MEC301 | Industrial Automation | 3-0-0-3 | - |
| III | CSE301 | Data Analytics for Maintenance | 3-0-0-3 | ENG201 |
| III | LAB301 | PLC Programming Lab | 0-0-3-1 | - |
| IV | MAT401 | Reliability Engineering | 3-0-0-3 | MAT301 |
| IV | MEC401 | Maintenance Engineering | 3-0-0-3 | - |
| IV | CSE401 | AI for Predictive Maintenance | 3-0-0-3 | CSE301 |
| IV | LAB401 | Vibration Analysis Lab | 0-0-3-1 | - |
| V | MAT501 | Asset Management | 3-0-0-3 | MAT401 |
| V | MEC501 | Energy Systems Maintenance | 3-0-0-3 | - |
| V | CSE501 | Cybersecurity for Industrial Systems | 3-0-0-3 | CSE401 |
| V | LAB501 | Industrial Robotics Lab | 0-0-3-1 | - |
| VI | MAT601 | Safety Management Systems | 3-0-0-3 | MAT501 |
| VI | MEC601 | Power Generation Maintenance | 3-0-0-3 | - |
| VI | CSE601 | Quality Assurance & Process Optimization | 3-0-0-3 | CSE501 |
| VI | LAB601 | Condition Monitoring Lab | 0-0-3-1 | - |
| VII | MEC701 | Capstone Project I | 2-0-0-2 | - |
| VII | CSE701 | Research Methodology | 2-0-0-2 | - |
| VIII | MEC801 | Capstone Project II | 4-0-0-4 | - |
| VIII | CSE801 | Thesis Writing | 2-0-0-2 | - |
Advanced Departmental Electives
Data Analytics for Maintenance: This course delves into statistical methods and data visualization tools used in industrial maintenance. Students learn how to extract meaningful insights from operational data to improve maintenance planning and reduce downtime.
AI for Predictive Maintenance: Designed for students interested in leveraging artificial intelligence, this course covers neural networks, regression models, and classification algorithms applied to fault prediction in industrial machinery.
Cybersecurity for Industrial Systems: With the increasing digitization of industrial environments, cybersecurity becomes paramount. This course explores network protocols, intrusion detection systems, and secure communication practices tailored for industrial settings.
Energy Systems Maintenance: Focused on renewable and traditional energy sectors, this elective teaches students how to maintain power plants, solar installations, wind turbines, and other energy-generating equipment.
Quality Assurance & Process Optimization: This course introduces Six Sigma methodologies, Lean principles, and statistical process control techniques to ensure high-quality maintenance practices.
Asset Management: Students explore lifecycle costing, depreciation models, and asset performance tracking systems used by large industrial organizations to optimize capital investments.
Safety Management Systems: A comprehensive look at hazard identification, risk assessment, and regulatory compliance in industrial environments. The course includes case studies from chemical and manufacturing plants.
Power Generation Maintenance: Tailored for students interested in working in power sectors, this course covers steam turbines, gas turbines, boiler systems, and electrical power distribution infrastructure.
Industrial Robotics: Students gain hands-on experience with robotic arms, sensors, and automation software used in modern maintenance operations. The course includes programming and simulation labs.
Condition Monitoring Techniques: This elective teaches students to use advanced diagnostic tools like thermal imaging, ultrasonic testing, and vibration analysis for early fault detection.
Maintenance Engineering Principles: An overview of various maintenance strategies including corrective, preventive, and predictive approaches. Students analyze real-world case studies from different industries.
Reliability Engineering: Focuses on reliability modeling, failure analysis, and system optimization techniques to enhance equipment lifespan and operational efficiency.
Industrial Automation: Introduces programmable logic controllers (PLCs), SCADA systems, and industrial communication protocols used in automated maintenance processes.
Machine Design: Teaches students how to design and select mechanical components for various industrial applications, considering factors like stress, strain, and fatigue.
Control Systems: Covers feedback control theory, transfer functions, and system stability analysis essential for understanding automated maintenance systems.
Project-Based Learning Philosophy
Our program emphasizes project-based learning as a means of integrating theoretical knowledge with practical application. Projects are designed to mirror real-world challenges faced by industrial organizations, encouraging students to think critically and innovate creatively.
Mini-projects begin in the third year, where students work in small teams on specific aspects of maintenance engineering. These projects often involve collaboration with industry partners and require students to present findings at departmental symposiums.
The final-year capstone project represents the culmination of a student's academic journey. Students select topics aligned with their interests and mentorship from faculty members. Projects are evaluated based on innovation, feasibility, impact, and presentation quality.
Faculty mentors are chosen based on expertise in the relevant domain, ensuring that students receive guidance from experienced professionals who understand both industry needs and academic rigor.
Students also have the opportunity to participate in national competitions such as the National Institute of Industrial Engineering (NITIE) projects, where their solutions are judged by industry experts and academics alike.