Hydrogen Cylinder Acoustic Emission Testing and Data Evaluation with Supervised Pattern Recognition September 2002, Vol. 7 No. 09:

A. N. Tsimogiannis, V. N. Nikolaidis, A. A. Anastasopoulos


Acoustic Emission (AE) has been extensively applied to vessel testing during pressurization, and has proven to be a fast and economic method for structural integrity assessment. High-pressure hydrogen cylinders are commonly tested with AE in-service, pressurizing with product, to verify fitness for service and to detect developing flaws at early stages. Testing large numbers of such cylinders and the presence of extraneous noise due to filling/pressurization processes and other sources can make AE data analysis difficult and time consuming. This paper presents work carried out for the analysis of hydrogen cylinder AE data, utilizing Supervised Pattern Recognition (SPR) techniques from modern AE software, in order to discriminate developing crack data from other data (e.g. noise) in an effort to automate the analysis process for large scale testing and increase confidence of the results. This work has led to the successful creation of a classifier to automate the discrimination between AE data from developing cracks and other indications, as diagnosed by follow-up NDT for verification, leading to minimum data analysis time.