PTCIC338 Machine Monitoring System Syllabus:

PTCIC338 Machine Monitoring System Syllabus – Anna University Part time Regulation 2023

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.

UNIT I INTRODUCTION TO MACHINE CONDITION MONITORING

Machinery condition monitoring – Present status – Fault prognosis – Future needs.

UNIT II MACHINERY MAINTENANCE

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

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

FFT analysis – Time domain analysis – Time-frequency analysis – Signal filtering – Cepstrum analysis – Health condition of compressor & engine.

UNIT V MACHINE LEARNING FOR CONDITION MONITORING

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
SKILL DEVELOPMENT ACTIVITIES (Group Seminar/Mini Project/Assignment/ Content Preparation / Quiz/ Surprise Test / Solving GATE questions/ etc)

1 Survey of critical machinery that requires monitoring system.
2 Exposure to practical machinery vibration & monitoring system presently in use.
3 Carryout FMECA using software.
4 Analyze the health condition of any machinery.

COURSE OUTCOMES:

CO1 Ability to identify the faults in machinery
CO2 Choose the proper maintenance strategies and condition monitoring techniques for identification of failure in a machine
CO3 Construct a classifier model for machine learning based fault diagnosis
CO4 Predict the faulty component in a machine by analyzing the acquired vibration signals
CO5 Ability to analyze & build a model using modern tools

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.

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.