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Wednesday, May 15 • 11:00am - 11:30am
CANCELLED An Octave Analysis Approach Based on Vibrational Data for Early Detection of Muliple Faults in Rolling Bearing

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The mechanical industry is focusing on modern maintenance techniques to minimize the downtime so that the production can be increased. The fourth industrial revolution also known as Industry 4.0 is aiming to provide fully autonomous systems including condition monitoring fault diagnostics techniques. In order to implement the Industry 4.0, the maintenance department needs automatic condition monitoring and fault diagnostics techniques. In this study, octave analysis has been proposed for early detection of bearing faults. In order to show the effectiveness of the proposed methodology, detailed experimentations have been performed on a Machinery Fault Simulator (MFS). The octave analysis has been implemented on the vibration data acquired through the accelerometers placed on the inboard and outboard bearing housing. The vibration data of a healthy bearing has been compared with a faulty bearing at four different rotational speeds under two operating conditions: no load applied and a constant load applied near outboard bearing housing. The standard deviation has also been calculated for healthy and faulty bearings. The octave analysis has high peaks for all rotational speeds and both operating conditions. The values of standard deviation of faulty bearings were found to be high as compared to a healthy bearing. This research article proposes a simple approach that does not require signal processing. The results proved that octave analysis along with standard deviation can be used for early detection of rolling bearing faults.
Keywords: Condition Monitoring, Fault Detection, Industry 4.0, Octave Analysis, Rolling Bearings, Vibrations.


Naqash Azeem

Northwestern Polytechnical University

Wednesday May 15, 2019 11:00am - 11:30am
Freedom Ballroom I