Development of compact acoustic emission system for structural health monitoring

Monitoring the condition and damage of industrial structures and equipment in a working condition is important to ensure proactive maintenance and prevent unexpected failures. Acoustic Emission (AE) is one of the widely used non-destructive testing (NDT) methods for online detection of sensitive damage areas with non-invasive techniques. However, traditional AE systems are restricted in their mobility, localized mounting, and energy efficiency (especially for certain applications). Here, we propose the development of a single-channel, localized long-term AE monitoring system with a compact size and low resource requirements. This AE system was designed and developed with ease of deployment and low power consumption especially, focusing on single-channel monitoring.

The proposed system can be utilized to process AE waveforms via a specific embedded microcontroller, extract the AE parameters, and store them for offline analysis using customized software. In addition, it also combines machine-learning-based signal typing and severity classification to AE parameters, which can help to interpret the collected data and plan for functional maintenance. Experimental of material damages under loading, which are metal and plastic types have been studied by using our AE system. The results of using our AE system show effective detection and classification of damage. Key AE parameters were successfully extracted and analyzed, revealing distinct patterns correlated with progressive damage stages. Finally, the proposed AE system confirms its effectiveness in continuous monitoring applications with easy operation and installation. Moreover, it shows compact size and power saving compared with traditional multi-channel AE devices while retaining reliable damage detection.