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Course Detail

Course Name Machine Learning Based Condition Monitoring
Course Code 19MEE331
Program B. Tech. in Mechanical Engineering
Year Taught 2019


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.

Objectives and Outcomes

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

CO1 3 3 1 3
CO2 3 3 1 3 3
CO3 3 3 3 2 1 1 1 1 1 1 3 1
CO4 3 3 3 3 3 1 1 1 1 1 1 3

Textbook / References


  • Clarence 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


  • 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

Assessment Internal External
Periodical 1 (P1) 15
Periodical 2 (P2) 15
*Continuous Assessment (CA) 20
End Semester 50
*CA – Can be Quizzes, Assignment, Projects, and Reports.

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