Ultrasonic acoustic health monitoring of ball bearings using neural network pattern classification of power spectral density

Proceedings of the ASME Joint Rail Conference 2010, JRC2010

Volumn 2, Issue , 2010, Pages 255-265

  Kirchner, William ^a   Southward, Steve ^a   Ahmadian, Mehdi ^a  

^a VIRGINIA POLYTECHNIC INSTITUTE AND STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACOUSTIC EMISSION SIGNAL; ARTIFICIAL NEURAL NETWORK; ARTIFICIAL TRAINING; EXPERIMENTAL DATA; FAILURE CLASSIFICATION; FREQUENCY RANGES; GENERIC APPROACH; HEALTH MONITORING; LOW SIGNAL-TO-NOISE RATIO; MULTICLASS CLASSIFICATION PROBLEMS; NETWORK PATTERN; NON-CONTACT; OPERATING CONDITION; RAIL INDUSTRY; STATISTICAL PROPERTIES; TRAINING DATA; ULTRASONIC FREQUENCY; ULTRASONIC RANGE;

ACOUSTIC EMISSION TESTING; ACOUSTIC EMISSIONS; BALL BEARINGS; ENGINES; HEALTH; MONITORING; POWER SPECTRAL DENSITY; SIGNAL TO NOISE RATIO; TIME SERIES ANALYSIS; ULTRASONICS;

NEURAL NETWORKS;

EID: 78049252430     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1115/JRC2010-36240     Document Type: Conference Paper

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