The development of automated pattern recognition and statistical feature isolation techniques for the diagnosis of reciprocating machinery faults using acoustic emission

Mechanical Systems and Signal Processing

Volumn 17, Issue 4, 2003, Pages 805-823

  El Ghamry, M H ^a   Reuben, R L ^a   Steel, J A ^a  

^a HERIOT WATT UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ACOUSTIC EMISSIONS; FAILURE ANALYSIS; MECHANICAL ENGINEERING; PATTERN RECOGNITION; SENSORS; STATISTICAL METHODS; TIME DOMAIN ANALYSIS;

AUTOMATED PATTERN RECOGNITION; RECIPROCATING MACHINERY; STATISTICAL FEATURE ISOLATION;

MACHINERY;

EID: 0038298894     PISSN: 08883270     EISSN: None     Source Type: Journal    
DOI: 10.1006/mssp.2002.1473     Document Type: Article

References (10)

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10. M. H. El-Ghamry 2000 Ph.D. Thesis, Heriot Watt University, Edinburgh. Performance and condition monitoring of reciprocating machines using acoustic emission.