Machine Condition Monitoring - MECH9223
Faculty: Faculty of Engineering
School: School of Mechanical and Manufacturing Engineering
Course Outline: http://www.mech.unsw.edu.au/
Campus: Sydney
Career: Postgraduate
Units of Credit: 6
EFTSL: 0.12500 (more info)
Indicative Contact Hours per Week: 3
CSS Contribution Charge: 2 (more info)
Tuition Fee: See Tuition Fee Schedule
Further Information: See Class Timetable
View course information for previous years.
Description
Pre requisite for this course is MMMAN2300
This course is intended to provide the necessary tools and basic knowledge in the field of monitoring the health of rotating and reciprocating machines, primarily through vibration analysis. Machine condition monitoring (MCM) plays a key role in the maintenance of machinery and consequently has the potential to minimise maintenance costs and increase the reliability and safety of machines.
The first part of the course explores different maintenance practices and introduces a number of related concepts, including techniques for MCM, such as vibration analysis, lubricant analysis, thermography, ultrasound, current and voltage, acoustic emissions, etc. Of these, vibrations are the most powerful indicator of internal condition, and the course concentrates mainly on this area. A number of signal processing techniques (FFT analysis, time synchronous averaging, cepstrum analysis, demodulation, time-frequency presentations such wavelets etc.) are then introduced and ultimately applied to diagnose faults in specific components of machines, e.g. gears, bearings, fans, pumps, engines, compressors, etc.
The concepts introduced in the course are reinforced through a practical MATLAB-based project on diagnosing gear or bearing faults from actual machine operating data.
This course is intended to provide the necessary tools and basic knowledge in the field of monitoring the health of rotating and reciprocating machines, primarily through vibration analysis. Machine condition monitoring (MCM) plays a key role in the maintenance of machinery and consequently has the potential to minimise maintenance costs and increase the reliability and safety of machines.
The first part of the course explores different maintenance practices and introduces a number of related concepts, including techniques for MCM, such as vibration analysis, lubricant analysis, thermography, ultrasound, current and voltage, acoustic emissions, etc. Of these, vibrations are the most powerful indicator of internal condition, and the course concentrates mainly on this area. A number of signal processing techniques (FFT analysis, time synchronous averaging, cepstrum analysis, demodulation, time-frequency presentations such wavelets etc.) are then introduced and ultimately applied to diagnose faults in specific components of machines, e.g. gears, bearings, fans, pumps, engines, compressors, etc.
The concepts introduced in the course are reinforced through a practical MATLAB-based project on diagnosing gear or bearing faults from actual machine operating data.
Study Levels
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