Abstract
This paper proposes an innovative method for identifying the locations of multiple simultaneous acoustic emission (AE) events in plate-like structures from the view of image processing. By using a linear lead zirconium titanate (PZT) sensor array to record the AE wave signals, a reverse-time frequency-wavenumber (f-k) migration is employed to produce images displaying the locations of AE sources by back-propagating the AE waves. Lamb wave theory is included in the f-k migration to consider the dispersive property of the AE waves. Since the exact occurrence time of the AE events is usually unknown when recording the AE wave signals, a heuristic artificial bee colony (ABC) algorithm combined with an optimal criterion using minimum Shannon entropy is used to find the image with the identified AE source locations and occurrence time that mostly approximate the actual ones. Experimental studies on an aluminum plate with AE events simulated by PZT actuators are performed to validate the applicability and effectiveness of the proposed optimal image-based AE source identification method.
Highlights
With the development of novel sensing technology, there is increasing needs to develop in-situ structural health monitoring system (SHMS) to monitor the integrity of critical structures such as airplanes and detect their damages as early as possible to prevent catastrophic failure
Results have demonstrated that artificial neural network (ANN) and support vector machine (SVM) are promising ways to localize acoustic emission (AE) sources; they require a large amount of data sets for training, impeding their applications in practical use
This study proposes a new method for identifying the locations of AE sources in plate-like structure from the view of image processing
Summary
With the development of novel sensing technology, there is increasing needs to develop in-situ structural health monitoring system (SHMS) to monitor the integrity of critical structures such as airplanes and detect their damages as early as possible to prevent catastrophic failure. For a specific SHMS, depending on its configuration there are two kinds of working modes: active monitoring and passive monitoring [1,2]. Active monitoring uses actuators to excite diagnostic signals into the structure and uses sensors to measure the responses to infer damage states, while passive monitoring uses only sensors to record structural responses or signals released by damages. This study considers passive monitoring of acoustic emission (AE) waves for plate-like structures to determine the locations of their sources where possible damages may exist. Traditional AE source localization methods are based on signal processing. Most of these localization algorithms developed utilize time information extracted from the AE wave signals recorded by different sensors. With the development of time-frequency signal processing, Sensors 2018, 18, 631; doi:10.3390/s18020631 www.mdpi.com/journal/sensors
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