Detecting and identifying artificial acoustic emission signals in an industrial fatigue environment

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.