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Thursday, May 16 • 11:00am - 11:30am
Bearing Faults Detection and Identification Using Relational Data Clustering with Composite Differential Evolution Optimization

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Bearing faults in machinery are among the most critical faults that require attention by maintenance personnel at early stages of fault initiation. In many cases it is difficult to directly and accurately identify the fault type and its extent under varying operating conditions. This work demonstrates a novel procedure for bearing fault detection and identification in an experimental set-up. Three seeded faults in the rotating machinery supported by the test roller bearing include inner race fault, outer race fault and one roller fault. The rotor is run at different speeds and with different levels of rotating mass unbalance. Accelerometer based vibration signals are analyzed for the different bearing faults’ signatures using statistical moments, frequency spectra and wavelet coefficients. These are then used as features to create a data-partition using relational data clustering. The composite differential evolution technique is proposed for optimizing the clustering parameters. The objective is to correlate bearing faults to the extracted vibration features. The results of this analysis will be extended for applications in real time bearing condition monitoring system.


Issam Abu-Mahfouz

Professor of Mechanical Engineering, Penn State University Harrisburg

Thursday May 16, 2019 11:00am - 11:30am
Freedom Ballroom I