Acoustic emission (AE) health monitoring of diaphragm type couplings using neural network analysis

Valery F. Godinez-Azcuaga; Fong Shu; Richard D. Finlayson D.V.M.; Bruce O'Donnell

doi:10.1117/12.601464

9 May 2005 Acoustic emission (AE) health monitoring of diaphragm type couplings using neural network analysis

Valery F. Godinez-Azcuaga, Fong Shu, Richard D. Finlayson D.V.M., Bruce O'Donnell

Proceedings Volume 5770, Advanced Sensor Technologies for Nondestructive Evaluation and Structural Health Monitoring; (2005) https://doi.org/10.1117/12.601464
Event: Nondestructive Evaluation for Health Monitoring and Diagnostics, 2005, San Diego, CA, United States

Abstract

This paper presents the latest results obtained from Acoustic Emission (AE) monitoring and detection of cracks and/or damage in diaphragm couplings, which are used in some aircraft and engine drive systems. Early detection of mechanical failure in aircraft drive train components is a key safety and economical issue with both military and civil sectors of aviation. One of these components is the diaphragm-type coupling, which has been evaluated as the ideal drive coupling for many application requirements such as high speed, high torque, and non-lubrication. Its flexible axial and angular displacement capabilities have made it indispensable for aircraft drive systems. However, diaphragm-type couplings may develop cracks during their operation. The ability to monitor, detect, identify, and isolate coupling cracks on an operational aircraft system is required in order to provide sufficient advance warning to preclude catastrophic failure. It is known that metallic structures generate characteristic Acoustic Emission (AE) during crack growth/propagation cycles. This phenomenon makes AE very attractive among various monitoring techniques for fault detection in diaphragm-type couplings. However, commercially available systems capable of automatic discrimination between signals from crack growth and normal mechanical noise are not readily available. Positive classification of signals requires experienced personnel and post-test data analysis, which tend to be a time-consuming, laborious, and expensive process. With further development of automated classifiers, AE can become a fully autonomous fault detection technique requiring no human intervention after implementation. AE has the potential to be fully integrated with automated query and response mechanisms for system/process monitoring and control.

Citation Download Citation

Valery F. Godinez-Azcuaga, Fong Shu, Richard D. Finlayson D.V.M., and Bruce O'Donnell "Acoustic emission (AE) health monitoring of diaphragm type couplings using neural network analysis", Proc. SPIE 5770, Advanced Sensor Technologies for Nondestructive Evaluation and Structural Health Monitoring, (9 May 2005); https://doi.org/10.1117/12.601464

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KEYWORDS

Metals

Sensors

Acoustic emission

Signal processing

Software development

Neural networks

Picture Archiving and Communication System

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