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| ME4000 | Predictive Maintenance Technigues | 3+0+0 | ECTS:6 | | Year / Semester | Spring Semester | | Level of Course | First Cycle | | Status | Elective | | Department | DEPARTMENT of MECHANICAL ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Dr. Öğr. Üyesi Yunus Emre KARABACAK | | Co-Lecturer | - | | Language of instruction | | | Professional practise ( internship ) | None | | | | The aim of the course: | | The aim of this course is to teach the fundamentals of predictive maintenance, provide information on techniques necessary for detecting and preventing machine faults, and introduce practical applications. |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | Predictive Maintenance Fundamentals: Students will demonstrate a comprehensive understanding of the principles underlying predictive maintenance, including its significance, methodologies, and implementation strategies. | 4,5 | 1,6, | | LO - 2 : | Machine Fault Detection and Diagnosis: Students will be able to identify various types of machine faults, apply appropriate diagnostic techniques, and interpret diagnostic results to effectively troubleshoot machinery issues. | 4,5 | 1,6, | | LO - 3 : | Preventive Techniques Application: Students will learn to apply preventive maintenance techniques effectively to mitigate potential machine failures, ensuring optimal equipment performance and longevity. | 4,5 | 1,6, | | LO - 4 : | Practical Application Proficiency: Students will develop hands-on proficiency in utilizing data collection systems, signal processing tools, and other predictive maintenance methods such as ultrasonics and infrared thermography to analyze machine vibrations, and detect faults. | 4,5 | 1,6, | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), LO : Learning Outcome | | |
| Fundamentals of predictive maintenance. Fundamentals of vibration. Data collection systems. Signal processing and applications. Machine fault detection with vibration analysis. Preventing fauts caused by vibration. Oil and particle analysis. Other predictive maintenance methods: Ultrasound, Infrared thermography. Practical applications |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Definition and importance of predictive maintenance, its history and development, basic principles, benefits, and application areas. | | | Week 2 | Definition and importance of predictive maintenance, its history and development, basic principles, benefits, and application areas. | | | Week 3 | Classification and examples of machine faults, fundamental techniques and equipment used for fault detection, diagnosis process and methods, interpretation of diagnostic results, and troubleshooting machine issues. | | | Week 4 | Classification and examples of machine faults, fundamental techniques and equipment used for fault detection, diagnosis process and methods, interpretation of diagnostic results, and troubleshooting machine issues. | | | Week 5 | Importance and principles of preventive maintenance, planning and implementation of preventive maintenance, maintenance strategies, and best practices. | | | Week 6 | Importance and principles of preventive maintenance, planning and implementation of preventive maintenance, maintenance strategies, and best practices. | | | Week 7 | Utilization of data collection systems, fundamentals of signal processing tools, practical applications of predictive maintenance methods such as ultrasonics and infrared thermography. | | | Week 8 | Utilization of data collection systems, fundamentals of signal processing tools, practical applications of predictive maintenance methods such as ultrasonics and infrared thermography. | | | Week 9 | Midterm exam | | | Week 10 | Basic principles of vibration, vibration analysis methods, detection and diagnosis of machine faults through vibration analysis. | | | Week 11 | Basic principles of vibration, vibration analysis methods, detection and diagnosis of machine faults through vibration analysis. | | | Week 12 | Importance of oil analysis, methods of particle analysis, role of oil and particle analysis in machine maintenance. | | | Week 13 | Introduction and applications of other advanced predictive maintenance methods such as ultrasonic inspection, infrared thermography, and others. | | | Week 14 | Introduction and applications of other advanced predictive maintenance methods such as ultrasonic inspection, infrared thermography, and others. | | | Week 15 | Presenting a practical project where students apply predictive maintenance methods to detect and resolve machine faults, thereby enhancing their skills in practical application | | | Week 16 | End of term exam | | | |
| 1 | Randall, R. B. 2021; Vibration-based condition monitoring: industrial, automotive and aerospace applications, John Wiley & Sons. | | | 2 | Mohanty, A. R. 2014; Machinery condition monitoring: Principles and practices, CRC Press. | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 09 | 17/04/2024 | 2,00 | 25 | | Homework/Assignment/Term-paper | 15 | 28/05/2024 | | 25 | | End-of-term exam | 16 | 04/06/2024 | 2,00 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 3 | 14 | 42 | | Arasınav için hazırlık | 2 | 5 | 10 | | Arasınav | 2 | 1 | 2 | | Ödev | 2 | 12 | 24 | | Dönem sonu sınavı için hazırlık | 2 | 5 | 10 | | Dönem sonu sınavı | 2 | 1 | 2 | | Total work load | | | 90 |
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