Characterization of fretting related acoustic emission signals

Md. Tariqul Alam; Mannur Sundaresan

doi:10.1117/12.847786

8 April 2010 Characterization of fretting related acoustic emission signals

Md. Tariqul Alam, Mannur Sundaresan

Proceedings Volume 7650, Health Monitoring of Structural and Biological Systems 2010; 76500J (2010) https://doi.org/10.1117/12.847786
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2010, San Diego, California, United States

Abstract

The acoustic emission (AE) technique is a promising tool for monitoring the integrity of structural members while they are in service. The major obstacle in deploying this technique is the presence of noise from extraneous sources that generate false positives. Identification and separation of noise from crack related signals are of interest. Friction induced AE is a prominent source of noise in structural members. When a structural member having riveted or bolted joints is subjected to cyclic loading, the mating parts of the surfaces experience very small relative motion that results in a localized rubbing process usually termed as "fretting". The fretting process is a prolific source of AE signals. As signals from crack growth as well as fretting emanates from the same region in riveted joints, it is difficult to discriminate crack related AE from fretting related AE, unless the distinct characteristics of the two signals are well understood. However, fretting related AE signals are also of noteworthy interest in tribological applications as they contain significant information about the surface conditions. To understand friction induced AE signals, numerically simulated fretting signals are analyzed. Greenwood and Williamson's multiple asperity contact model is used to generate fretting signals numerically. The results are also compared with experimentally obtained fretting signals.

Citation Download Citation

Md. Tariqul Alam and Mannur Sundaresan "Characterization of fretting related acoustic emission signals", Proc. SPIE 7650, Health Monitoring of Structural and Biological Systems 2010, 76500J (8 April 2010); https://doi.org/10.1117/12.847786

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