A Novel Acoustic Emission Sources Localization and Identification Method in Metallic Plates Based on Stacked Denoising Autoencoders

Abstract

Nowadays, deep learning could be an alternative approach to crack characterization. However, to the best of the authors' knowledge, little research exists on a deep learning-based characterization of fatigue-related AE sources occurring in plate-like structures. Consequently, this paper introduces a stacked denoising autoencoders (SDAE)-based framework to localize acoustic emission (AE) sources in common and complex metallic panels. The experimental specimen are respectively a Q235B steel plate and a 316L stainless steel containing a laser cladding layer. Specifically, SDAE is pre-trained and utilized to localize AE sources that are simulated by using the classical pencil lead break (PLB) approach. Meanwhile, the number of layers and hidden nodes of SDAE used for coordinate-based location is optimized according to a Bayesian Information Criteria (BIC) approach. To validate the proposed network and simplify the analysis, experiments are carried out on the surface of plate-like structures, which only one sensor is applied. After identifying AE sources that occur near laser cladding layers, the proposed approach classifies them into four source-to-laser cladding layer distance categories. Particularly, a tenfold cross-validation method is utilized to improve the accuracy of localization in this paper. Moreover, the effectiveness analysis to the number of sensors and comparison with conventional machine learning methods, including support vector machine (SVM) and artificial neural network (ANN), are also evaluated. In order to validate the performance of the proposed approach in terms of coordinate-based source localization. Ultimately, the results demonstrate that 100% accuracy for zonal localization, and the root mean squared (RMS) localization errors of two metallic panels are 38 mm (1.5") and 48 mm (1.9"), respectively. Additionally, in comparison with conventional machine learning approaches (i.e. SVM and ANN) which the RMS errors were 78 mm (2.5") and 67 mm (2.1"), respectively, the coordinates-based localization accuracy is significantly improved using the proposed approach. The results demonstrate the proposed approach is effective in AE-based structural health monitoring of plate-like structures with singlesensor. INDEX TERMS acoustic emission (AE); sources localization and identification; stacked denoising autoencoders (SDAE); metallic steel plates; structural health monitoring

Damage Degree Assessment of Steel Products Based on the Statistical Processing of Acoustic Emission Signal Flow

“…As an example, two close time points τ = 16.5 s and τ = 18.4 s were chosen. The energy values of the AE signal leading edge Ef were calculated by the formula: , (5) where uμV -AE signal amplitude (µV), τf -AE signal rise time (μs).…”

“…These characteristics of AE signals reflect the energy and temporal parameters of the possible destruction [4]. As a result of the AE data processing, the identification, localization and damage degree assessment of propagating defects are carried out [5][6][7][8][9][10].…”

“…For a more accurate damage degree assessment, a comprehensive analysis of AE data is usually performed. Thus, complex AE parameters were applied in [5,[11][12][13][14]. For instance, the ratio of the rise time to the maximum amplitude of the AE signal, the ratio of the spikes number to the AE signal duration, as well as the slope of the linear function approximating the shape of the AE signal amplitude distribution were used.…”

State-of-the-Art Review on the Acoustic Emission Source Localization Techniques

“…The accuracy of the localization results obtained from the proposed method was significantly improved. Yang and Xu [130] presented a pre-trained stacked denoising autoencoders (SDAE)-based framework to localize acoustic emission (AE) sources in common and complex metallic panels. Bayesian information criteria (BIC) approach was used to optimize the number of layers and hidden nodes of SDAE used for coordinate-based location.…”

Machine Learning for Touch Localization on Ultrasonic Wave Touchscreen

“…The accuracy of this method however remains sensitive to noise and degrades to 57 % when the Signal-to-Noise Ratio (SNR) drops by 30 dB. An auto-encoder-based approach for acoustic emission sources localization is studied in [36]. The RMS localization errors are 38 mm and 48 mm (for two metallic panels), which represents an improvement in comparison with SVM and ANN (78 mm and 67 mm, respectively).…”