Abstract
The acoustic emission (AE) technique has become a well-established method of monitoring structural health over recent years. The sensing and analysis of elastic AE waves, which have involved piezoelectric wafer active sensors (PWAS) and time domain and frequency domain analysis, has proven to be effective in yielding fatigue crack-related information. However, not much research has been performed regarding (i) the correlation between the fatigue crack length and AE signal signatures and (ii) artificial intelligence (AI) methodologies to automate the AE waveform analysis. In this paper, this crack length correlation is investigated along with the development of a novel AE signal analysis technique via AI. A finite element model (FEM) study was first performed to understand the effects of fatigue crack length on the resulting AE waveforms and a fatigue experiment was performed to capture experimental AE waveforms. Finally, this database of experimental AE waveforms was used with a convolutional neural network to build a system capable of performing automated classification and prediction of the length of a fatigue crack that excited respective AE signals. AE signals captured during a fatigue crack growth experiment were found to match closely with the FEM simulations. This novel AI system proved to be effective at predicting the crack length of an AE signal at an accuracy of 98.4%. This novel AI-enabled AE signal analysis technique will provide a crucial step forward in the development of a comprehensive structural health monitoring (SHM) system.
Highlights
For engineering structures in service, safety and trustworthiness are of the utmost concern
We propose the potential to develop an artificial intelligence-related signal analysis system that could discern the acoustic emission (AE) wave information and predict the length of the crack from which it originates
The network can be used to build a comprehensive artificial intelligence (AI) system for monitoring fatigue-prone areas in thin metallic sheets. This is accomplished by utilizing the capabilities of this convolutional neural network (CNN) to filter out crack-related vs. noise signals, determine the crack length of a given AE crack-related signal picked up by the monitoring system, and give a prognosis on the remaining useful life of the metallic component
Summary
For engineering structures in service, safety and trustworthiness are of the utmost concern. In the numerous different operation modes that engineering structures are exposed to, there exist various types of possible failure mechanisms. The number of fatigue-prone structures in current and future engineering applications continues to grow, yielding the demand for a robust and efficient method of monitoring their structural integrity during operational use. The structural health monitoring (SHM) field is an emerging methodology that is used as a technique for detecting damages in structures, such as fatigue damage [1,2,3]. The SHM technique/process, in general, can be described in four levels. These levels explain how, from start to finish, an SHM system can yield useful information [5].
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
