Ms. Brenna Feirer Profile

Brenna Feirer

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Proceedings Article | 18 May 2020 Paper

Acoustic emission damage evaluation of rolling element bearings for shipboard machinery

Brenna Feirer, Paul Ziehl, Rafal Anay, Mahmoud Bayat, Bin Zhang, Eugene Golda

Proceedings Volume 11380, 113801P (2020) https://doi.org/10.1117/12.2563819

KEYWORDS: Acoustic emission, Sensors, Continuous wavelet transforms, Analytical research, Failure analysis, Pattern recognition, Data acquisition

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Rolling element bearings perform an essential role in most rotating machinery. Bearing fault diagnosis and prognosis can detect degradation to bearing performance, preventing the costs of unexpeceted system failure. Acoustic Emission (AE) introduces high sensitivity, early and rapid detection of cracking, and real time monitoring that can provide an alarm once cracking is noticed. This paper discusses the nondestructive monitoring of crack growth in rolling element bearings in a marine environment and the determination of acoustic emission parameters which indicate crack initiation and propagation. The paper’s intellectual merit lies in the signal alarm developed from an AE data pattern recognition method, and the specially made rotating machinery test bed that simulates a bearing used on board a ship. Four rolling element bearings were tested in the test bed at various loads and rotation cycles. All AE data was clustered using k-means unsupervised method, and the lowest correlated features were selected for pattern recognition. Useful AE parameters for classifying crack initiation and propagation were determined. Acoustic emission proved to be suitable for remote monitoring of bearing degradation. With the use of signal alarms based upon the clustering method and parameters discussed, one can be notified when a crack has been initiated and is propagating. This will allow the user to avoid a costly unexpected system failure and plan to perform a less costly bearing replacement.

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