Course

Unit 1

Basic Concepts: Machinery failures, basic maintenance strategies, factors influencing maintenance strategies, machine condition monitoring, transducer selection and location, PC interfacing and virtual instrumentation. Vibration signatures of faults in rotating machines; detection and diagnosis of faults.

Unit 2

Instrumentation and Signal Processing: Types of sensors in condition monitoring: vibration, sound, acoustic emission, temperature, ultrasonic and infra-red sensors – Signal processing: basic signal and systems concepts, time domain analysis, frequency domain analysis, time-frequency analysis and wavelets.

Unit 3

Machine Learning: Feature extraction and feature selection methods, feature reduction using PCA – discriminate functions and decision boundaries, decision trees, maximum likelihood and nearest neighbor classification – Bayesian theory, neural networks and support vector machines in classification Application and case studies of condition monitoring: Bearings, gear boxes, centrifugal pumps, turbines and tool wear monitoring.

Course Objectives

This course is expected to enable the student to:

• Familiarize with the concept of condition-based maintenance for effective utilization of machines
• Impart knowledge of artificial intelligence for machinery fault diagnosis

Course Outcomes

• CO1: Select the proper maintenance strategies and condition monitoring techniques for identification of failure in a machine.
• CO2: Acquire and Process sound and vibration signals in a dynamic mechanical system
• CO3: Predict the faulty component in a machine by analyzing the acquired vibration signals
• CO4: Build a classifier model for machine learning based fault diagnosis of rotating machines

CO – PO Mapping

Textbook(s)

• Clarence W.de Silva “Vibration Monitoring, Testing and Instrumentation (Mechanical and Aeropace Engineering Series)”, CRC Press, Taylor & Francis, 2007.
• A. R. Mohanty, “Machinery Condition Monitoring: Principles and Practices” , CRC Press, Taylor & Francis, 2015

Reference(s)

• Collacot, “Mechanical Fault Diagnosis and Condition Monitoring”, Chapman- Hall, 1987.
• Davies, “Handbook of Condition Monitoring – Techniques and Methodology”, Springer, 1998.
• Cornelius SchefferandPareshGirdhar, “Practical Machinery Vibration Analysis and Predictive Maintenance”, Elsevier, 2004.
• K.P.Soman, ShyamDiwakar and V.Ajay, “Data Mining: Theory and Practice” PHI Learning Pvt. Ltd., 2006.
• Duda, R.O., Peter, Hart, E., and Stork, D.E., “Pattern Classification”, 2e, Wiley India, 2007.

Evaluation Pattern