Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Detecting and Identifying Artificial Acoustic Emission Signals in an Industrial Fatigue Environment

Hensman, J., Pullin, Rhys, Eaton, Mark Jonathan, Worden, K., Holford, Karen Margaret and Evans, Samuel Lewin 2009. Detecting and Identifying Artificial Acoustic Emission Signals in an Industrial Fatigue Environment. Measurement Science and Technology 20 (4) , 045101. 10.1088/0957-0233/20/4/045101

Full text not available from this repository.

Abstract

This paper details progress in the application of a methodology for acoustic emission (AE) detection and interpretation for the monitoring of fatigue fractures in large-scale industrial environments. The approach makes use of a number of novel signal processing techniques. An online radius-based clustering algorithm (ORACAL) is used to identify clusters of data, both in the spatial domain (locating AE sources) and in the feature domain (identifying candidate fracture processes). The paper proposes a new approach to the identification of AE waveforms produced by crack propagation; rather than seeking to identify the waveform features characteristic of a fracture event, the new method looks for specific patterns of clustering in the feature space. The approach is validated by a full-scale experiment. An artificial acoustic emission source, representative of a fatigue fracture, was injected into a test of a substantial landing gear component. A commercial AE monitoring system was then used to successfully locate and identify the source in a blind test using the new signal processing methodology. The method was successful on two of three experiments performed and the position of the artificial source was determined accurately; further analysis shows that the unsuccessful test appears to have occurred due to incorrect mounting of the artificial source.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Centre for Advanced Manufacturing Systems At Cardiff (CAMSAC)
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Publisher: IOP Publishing
ISSN: 0957-0233
Last Modified: 09 Jan 2018 10:38
URI: http://orca.cf.ac.uk/id/eprint/5427

Citation Data

Cited 13 times in Google Scholar. View in Google Scholar

Cited 17 times in Scopus. View in Scopus. Powered By Scopus® Data

Cited 3 times in Web of Science. View in Web of Science.

Actions (repository staff only)

Edit Item Edit Item