LEARNING OUTCOMES
At the end of the course you will be able to:
- Explain the necessity of diagnostics and condition monitoring of electrical devices
- List the different faults that occur in electrical machines
- List different methods to diagnosis faults in electrical machines
- Use machine learning algorithm for fault detection
- Use numirical softwrae to model some faults in the electrical machines
Credits: 5
Schedule: 27.02.2025 - 15.05.2025
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Anouar Belahcen, Ahmed Hemeida
Contact information for the course (applies in this implementation): General information Welcome to the Diagnosis and condition monitoring of electrical machines D- course! In this course, you will explore the factors influencing the health of electrical machines and the various faulty conditions they may experience. You will also learn to characterize these faults. By analyzing machine signals in both time and frequency domains, you will develop the ability to detect and diagnose faults. Additionally, we will cover essential methods related to fault diagnosis. The course content includes: • What is condition monitoring? • Why condition monitoring is necessary? • Why fault occurs in electric machines? • Different types of faults • Effect of faults on the performance of electric machines • Modeling of electric machines under faulty conditions • Measurements of faults • Fault detection methods • Machine learning algorithms for faults detection The last lecture is a guest lecture from a well-known industry (TBA) in the sector who will show how condition monitoring is leveraged in industrial processes. Intended learning outcomes After this course, you will be able to: • Understand condition monitoring through practical examples and its significance in electric machinery. • Identify the appropriate signal types for diagnosing specific faults. • Use COMSOL to diagnose various faults of different severities in electrical machines. • Learn how condition monitoring is applied in industrial processes. Teaching sessions in Spring 2025 Completion of the course To complete the course and gain 5 ECTS credits, you need to: Participate actively in all the lectures and exercise sessions (30% of the full grade), Have the maximum of one justified absence (we expect you to inform the assistants about your absence before the course), Complete and submit the project (70% of the grade). The course will be graded between 0-5. Course teaching assistants: Nada El Bouharrouti, nada.elbouharrouti@aalto.fi Mahetab Alam, mahetab.alam@aalto.fi
CEFR level (valid for whole curriculum period):
Language of instruction and studies (applies in this implementation):
Teaching language: English. Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Content
valid for whole curriculum period:
• What is condition monitoring?
• Why condition monitoring is necessary?
• Why fault occurs in electric machines?
• Different types of faults
• Effect of faults on the performance of electric machines
• Modeling of electric machines under faulty conditions
• Measurements of faults
• Fault detection methods
• Machine learning algorithms for faults detection
Assessment Methods and Criteria
valid for whole curriculum period:
• Two assignments (contribute to full final grade)
• Attendance is mandatory
Workload
valid for whole curriculum period:
Lectures and self studies
DETAILS
Study Material
valid for whole curriculum period:
Lecture slides, course handouts
"Fault Diagnosis, Prognosis, and Reliability for Electrical Machines and Drives", Elias G. Strangas et al. 2022, John Wiley & Sons, New Jersey.
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language: English
Teaching Period: 2024-2025 Spring IV - V
2025-2026 No teachingRegistration:
SISU