(Agarwal et al. 2021). Various studies have demonstrated the value of transfer learning in enhancing neural networks for acoustic emission source localization and SHM, such as Chen et al. (2021), who proposed an acoustic-homologous transfer learning approach for rail condition evaluation, and Hasan et al. (2019), who utilized transfer learning for reliable bearing fault diagnosis under variable speed conditions. Deep learning and transfer learning methods have shown great potential in improving acoustic emission source local- ization efficiency (Sun 2020 Bengio 2012). Ismail-Fawaz et al. (2022) presented a deep learning approach for time series classification using hand-crafted convolution filters, further enhancing AE capabilities. Ismail Fawaz et al. (2018) explored transfer learning for time series classification, while Zhang et al. (2017) studied the learnability of fully connected neural networks. Weiss et al. (2016) provided a survey of transfer learning, and Bengio (2012) emphasized the importance of deep learning representations for unsupervised and transfer learning. Though significant advancements have been made in applying deep learning and transfer learning to acoustic emission source localization, continued development and opti- mization of these methodologies are essential for addressing inherent challenges and maximizing their potential in SHM (Bengio 2012 Sun 2020). In this study, our principal innovation lies in the success- ful implementation of transfer learning through the pretrain- ing of six deep learning models on a large simulated acoustic emission dataset. This enabled the localization of acoustic emission sources using a single sensor. We pretrained con- volutional neural network (CNN), fully convolutional neural network (FCNN), Encoder, ResNet, Inception, and Multi-layer Perceptron (MLP) models using data from finite element method (FEM) simulations of acoustic emission impulses. Through transfer learning, we fine-tuned the pretrained models on the experimental dataset, improving their perfor- mance while reducing the number of experiments needed. Our results show that the pretrained models generalized well to variations in acoustic emission signals and could be applied to different model architectures and datasets. Overall, our research highlights the potential of deep learning techniques, particularly transfer learning, for improving the accuracy and efficiency of acoustic emission source localization. These findings can significantly benefit the development of reliable and cost-effective SHM strategies and are readily applicable to other nondestructive evaluation problems. This paper is organized into four main sections. The first section provides an overview of the laboratory experiments conducted utilizing pencil lead break (PLB) and impact tests at nine distinct positions. In the next section, to aid under- standing, data visualization is furnished through raw waveform plots of both simulated and real-life experimental data derived from impact and PLB testing. Additionally, a 2D t-SNE plot is provided to better illustrate the clustering structure of signals originating from nine distinct locations or classes. The third section introduces six distinct deep learning models, including our own, which were designed through the iterative fine-tuning of layers with unique training parameters. The architectural details of both the classifier and the transfer elements of our model are thoroughly analyzed in this section. The final section presents the results obtained by training these fine- tuned models using tenfold cross-validation. To give a compre- hensive view of the models’ performance, the mean loss and range of loss for each classifier, as well as for the impact and PLB tests, are plotted. The efficacy of each fine-tuned model is further evaluated by computing and representing key metrics such as precision, recall, and accuracy in a box plot format. Methods and Experiments The primary objective of the conducted experiments was to scrutinize the effectiveness of the suggested source localization techniques, utilizing a singular AE sensor, on an aluminum plate. As represented in Figure 1, the experimental setup com- prised a sensor, constituted by two frail fiber Bragg gratings (FBGs), forming a low-finesse Fabry-Perot interferometer (FPI) on a coiled single-mode fiber. This arrangement facilitated the detection of ultrasound on a solid surface. The setup employed a narrow-linewidth diode laser with wavelength tunability, designed to direct light toward the FBG-FPI sensor via a circu- lator developed in Karim et al. (2021). Before reaching the sensor, the light was passed through a three-paddle polarization controller, which facilitated manual adjustments to the laser polarization. The light reflected from the sensor was then directed to a photodetector (PD) through the same circulator. To obtain acoustic emission signals of higher quality, the output from the PD was amplified and filtered using a 50–500 kHz band-pass filter. Additionally, noise removal techniques, such as adaptive filtering, were employed to reduce any extraneous signals present during data collec- tion. It is selected based on its ability to effectively remove noise while preserving the signal of interest. The filtered and noise-free AE signals were subsequently utilized to train and test the deep learning models for source localization. Acoustic emission is a physical occurrence linked to stress waves, initiated by the abrupt liberation of elastic energy during the formation of cracks or damages within materials. AE signals can be captured and logged by attaching AE sensors to the sample surface. The AE monitoring process involves the collec- tion and analysis of these signals to assess the condition of the object under study. The Hsu-Nielsen PLB test, a widely accepted artificial method for acoustic emission signal generation (Sause 2011), was used in this study. It involves breaking pencil leads on a surface with an affixed AE sensor. For this study, PLB tests were conducted on a 2.54 mm thick aluminum plate measuring 0.30 × 0.30 m. The plate was partitioned into nine distinct loca- tions as delineated in Figure 2. Each of the nine representative points, denoted by a red dot, underwent the PLB test 10 times, using a 2H mechanical pencil with a 0.5 mm diameter lead. Furthermore, impact-like signals were gathered by dropping steel balls (4.7 mm diameter) from a height of 25 mm at the same AE sensor location illustrated in Figure 2. The ME |AI/ML 72 M A T E R I A L S E V A L U A T I O N • J U L Y 2 0 2 3 2307 ME July dup.indd 72 6/19/23 3:41 PM

equipment and settings for this experiment mirrored those utilized for the PLB tests. The recorded signals were distin- guished and examined for acoustic emission source identifi- cation and localization, using these procedures. The experi- mental setup facilitated the collection of precise and accurate data, thereby enabling the evaluation of the proposed method’s efficacy in acoustic emission source localization. Numerical Modeling Assisted Data Augmentation This study utilizes a 3D computational model for the test specimen to enable an enhanced characterization of acoustic emission impulses, as inspired by Cuadra et al. (2015). The approach hinges on the implementation of pretrained deep learning models, which harness data from FEM-simulated acoustic emission impulses derived from impact-type and PLB tests (Hamstad 2007). The creation of pretraining data via these simulated AE signals propels advancements in acoustic emission source localization within the specimen. This model offers several benefits, such as reducing computational demands and enhancing the performance of AE monitoring systems in real-world scenarios. The accurate characterization of acoustic emission impulses is a vital prerequisite for devel- oping effective signal-processing algorithms. Our proposal presents a robust methodology to pretrain deep learning models using data procured from acoustic emission impulse simulations. The PLB source was strategically positioned in the out-of-plane direction at a predefined location on the plate, with the sensor situated an inch from the right and upper FO sensor is flexibly mounted to the sample surface AE source FBG-FPI sensor Laser source Circulator Laser Polarization controller DC1 AC1 Photodetector Phase modulator LPF (25 kHz) Amp 40 dB BPF (50–500 kHz) Oscilloscope z y x Figure 1. Schematic of novel fiber-optic coil-based acoustic emission sensing and monitoring system. Lead Mechanical pencil Specimen Specimen 45º Steel ball Sensor Zone 1 Zone 2 Zone 3 Zone 4 Zone 5 Zone 6 Zone 7 Zone 8 Zone 9 Figure 2. Experimental setup for acoustic emission monitoring: (a) pencil lead break (PLB) and (b) impact tests conducted on an aluminum plate (c) that is segregated into nine identified zones. This setup assists the localization of acoustic emission sources. J U L Y 2 0 2 3 • M A T E R I A L S E V A L U A T I O N 73 2307 ME July dup.indd 73 6/19/23 3:41 PM Oscilloscope/DA