Evaluation of acoustic emission signals during monitoring of thick-wall vessels operating at elevated temperatures


EWGAE 2004, 26th European Conference on AE testing, September 15-17, 2004 Berlin

Anastasopoulos, A., A., and Apostolos Tsimogiannis


Acoustic Emission testing of thick wall vessels, operating at elevated temperatures is discussed and pattern recognition methodologies for AE data evaluation are presented. Two different types of testing procedures are addressed: Cool Down monitoring and semi-continuous periodic monitoring. In both types of tests, temperature variation is the driving force of AE as opposed to traditional AE testing where controlled pressure variation is used as AE stimulus.

Representative examples of reactors cool down testing as well as in-process vessel monitoring are given. AE activity as a function of temperature and pressure variation is discussed. In addition to the real-time limited criteria application, unsupervised pattern recognition is applied as a post-processing tool for multidimensional sorting, noise discrimination, characterizing defects and/or damage. On the other hand, Supervised Pattern Recognition is used for data classification in repetitive critical tests, leading to an objective quantitative comparison between repeated tests. Results show that damage sustained by the equipment can be described by the plotting the cumulative energy of AE, from critical signal classes, versus temperature. Overall, the proposed methodology can reduce the complexity of AE tests in many cases leading to higher efficiency. The possibility for real time signals classification, during permanent AE installations and continuous monitoring is discussed.