TIEE3037 Syllabus - Machine Monitoring System - 2022 Regulation Anna University
TIEE3037 Syllabus - Machine Monitoring System - 2022 Regulation Anna University
TIEE3037 |
MACHINE MONITORING SYSTEM |
L T P C |
|---|
3003
COURSE OBJECTIVES:
• To make the students familiarize with the concept of condition-based maintenance for effective utilization of machines.
• To Impart the knowledge of artificial intelligence for machinery fault diagnosis.
• To give basic knowledge on vibration monitoring.
• To study the machinery vibrations using signal processing techniques.
• To provide knowledge on FMECA.
• To Impart the knowledge of artificial intelligence for machinery fault diagnosis.
• To give basic knowledge on vibration monitoring.
• To study the machinery vibrations using signal processing techniques.
• To provide knowledge on FMECA.
UNIT I |
INTRODUCTION TO MACHINE CONDITION MONITORING |
(7+2 SKILL) 9 |
|---|
Machinery condition monitoring - Present status - Fault prognosis - Future needs.
UNIT II |
MACHINERY MAINTENANCE |
(7+2 SKILL) 9 |
|---|
Maintenance strategies – Reactive, Preventive, and Predictive – Benefits of planned maintenance – Bath tub curve – Failure Modes Effects and Criticality Analysis (FMECA).
UNIT III |
INTRODUCTION TO MACHINERY VIBRATION AND MONITORING |
(7+2 SKILL) 9 |
|---|
Characteristics of Vibration systems – Mode shapes & operational deflection shapes – Experimental modal analysis – Principles of vibration monitoring – Machinery faults diagnosed by vibration analysis.
UNIT IV |
SIGNAL PROCESSING IN MACHINERY MONITORING |
(7+2 SKILL) 9 |
|---|
FFT analysis – Time domain analysis – Time-frequency analysis – Signal filtering – Cepstrum analysis – Health condition of compressor & engine.
UNIT V |
MACHINE LEARNING FOR CONDITION MONITORING |
(7+2 SKILL) 9 |
|---|
Machine Learning: Feature extraction and feature selection methods – Feature reduction – Classification techniques – Case studies of condition monitoring in Nuclear plant components, Distillation column.
TOTAL: 45 PERIODS
COURSE OUTCOMES:
CO1 Ability to identify the faults in machinery L1.
CO2 Choose the proper maintenance strategies and condition monitoring techniques for identification of failure in a machine L3.
CO3 Construct a classifier model for machine learning based fault diagnosis L5.
CO4 Predict the faulty component in a machine by analyzing the acquired vibration signals L2.
CO5 Ability to analyze & build a model using modern tools L4.
CO2 Choose the proper maintenance strategies and condition monitoring techniques for identification of failure in a machine L3.
CO3 Construct a classifier model for machine learning based fault diagnosis L5.
CO4 Predict the faulty component in a machine by analyzing the acquired vibration signals L2.
CO5 Ability to analyze & build a model using modern tools L4.
TEXT BOOKS:
1. Cornelius SchefferandPareshGirdhar, “Practical Machinery Vibration Analysis and Predictive Maintenance”, Elsevier, 2004, 1st Edition.
2. A. R. Mohanty, “Machinery Condition Monitoring: Principles and Practices” , CRC Press, Taylor & Francis, 1st Edition, 2017.
2. A. R. Mohanty, “Machinery Condition Monitoring: Principles and Practices” , CRC Press, Taylor & Francis, 1st Edition, 2017.
REFERENCES:
1. Stephen Marsland, Machine Learning: An Algorithmic Perspective, 2nd Edition, 2014, CRC, Press.
2. Collacot, “Mechanical Fault Diagnosis and Condition Monitoring”, Chapman- Hall, 1st Edition, 2011.
3. Davies, “Handbook of Condition Monitoring – Techniques and Methodology”, Springer, 1st Edition, 2011.
4. Ian H. Witten, Eibe Frank, Mark A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, Elsevier, 3rd Edition 2011.
5. Ferdinand van der Heijden, Robert Duin, Dick de Ridder, David M. J. Tax, Classification, Parameter Estimation and State Estimation: An Engineering Approach Using MATLAB, John Wiley & Sons, 2nd Edition, 2017.
2. Collacot, “Mechanical Fault Diagnosis and Condition Monitoring”, Chapman- Hall, 1st Edition, 2011.
3. Davies, “Handbook of Condition Monitoring – Techniques and Methodology”, Springer, 1st Edition, 2011.
4. Ian H. Witten, Eibe Frank, Mark A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, Elsevier, 3rd Edition 2011.
5. Ferdinand van der Heijden, Robert Duin, Dick de Ridder, David M. J. Tax, Classification, Parameter Estimation and State Estimation: An Engineering Approach Using MATLAB, John Wiley & Sons, 2nd Edition, 2017.
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