NDLI: Ultrasonic Acoustic Health Monitoring of Ball Bearings Using Neural Network Pattern Classification of Power Spectral Density

Content Provider	The American Society of Mechanical Engineers (ASME) Digital Collection
Author	Kirchner, William Southward, Steve Ahmadian, Mehdi
Copyright Year	2010
Abstract	This work presents a generic passive non-contact based acoustic health monitoring approach using ultrasonic acoustic emissions (UAE) to facilitate classification of bearing health via neural networks. This generic approach is applied to classifying the operating condition of conventional ball bearings. The acoustic emission signals used in this study are in the ultrasonic range (20–120 kHz), which is significantly higher than the majority of the research in this area thus far. A direct benefit of working in this frequency range is the inherent directionality of microphones capable of measurement in this range, which becomes particularly useful when operating in environments with low signal-to-noise ratios that are common in the rail industry. Using the UAE power spectrum signature, it is possible to pose the health monitoring problem as a multi-class classification problem, and make use of a multi-layer artificial neural network (ANN) to classify the UAE signature. One major problem limiting the usefulness of ANN’s for failure classification is the need for large quantities of training data. This becomes a particularly important issue when considering applications involving higher value components such as the turbo mechanisms and traction motors on diesel locomotives. Artificial training data, based on the statistical properties of a significantly smaller experimental data set is created to train the artificial neural network. The combination of the artificial training methods and ultrasonic frequency range being used results in an approach generic enough to suggest that this particular method is applicable to a variety of systems and components where persistent UAE exist.
Sponsorship	Rail Transportation Division
Starting Page	255
Ending Page	265
Page Count	11
File Format	PDF
ISBN	9780791849071
DOI	10.1115/JRC2010-36240
e-ISBN	9780791838679
Volume Number	2010 Joint Rail Conference, Volume 2
Conference Proceedings	2010 Joint Rail Conference
Language	English
Publisher Date	2010-04-27
Publisher Place	Urbana, Illinois, USA
Access Restriction	Subscribed
Subject Keyword	Trains Ball bearings Bearings Locomotives Artificial neural networks Spectral energy distribution Motors Acoustics Traction Microphones Rails Diesel Turbochargers Signals Failure Acoustic emissions Signal to noise ratio
Content Type	Text
Resource Type	Article

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