Single-Sensor Acoustic Emission Source Localization in Plate-Like Structures Using Deep Learning

This paper introduces two deep learning approaches to localize acoustic emissions (AE) sources within metallic plates with geometric features, such as rivet-connected stiffeners. In particular, a stack of autoencoders and a convolutional neural network are used. The idea is to leverage the reflection and reverberation patterns of AE waveforms as well as their dispersive and multimodal characteristics to localize their sources with only one sensor. Specifically, this paper divides the structure into multiple zones and finds the zone in which each source occurs. To train, validate, and test the deep learning networks, fatigue cracks were experimentally simulated by Hsu–Nielsen pencil lead break tests. The pencil lead breaks were carried out on the surface and at the edges of the plate. The results show that both deep learning networks can learn to map AE signals to their sources. These results demonstrate that the reverberation patterns of AE sources contain pertinent information to the location of their sources.

[1]  Yoshua Bengio,et al.  Deep Sparse Rectifier Neural Networks , 2011, AISTATS.

[2]  S. H. Carpenter,et al.  A waveform investigation of the acoustic emission generated during the deformation and cracking of 7075 aluminum , 1995 .

[3]  Oral Büyüköztürk,et al.  Conditional classifiers and boosted conditional Gaussian mixture model for novelty detection , 2018, Pattern Recognit..

[4]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[5]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[6]  Sheng-Wei Chi,et al.  Wavelet based harmonics decomposition of ultrasonic signal in assessment of plastic strain in aluminum , 2017 .

[7]  Emmanuel Moulin,et al.  Source Localisation in a Reverberant Plate Using Average Coda Properties and Early Signal Strength , 2014 .

[8]  S. Salamone,et al.  Damage localization in metallic plate structures using edge-reflected lamb waves , 2016 .

[9]  Rhys Pullin,et al.  Acoustic emission source location in complex structures using full automatic delta T mapping technique , 2016 .

[10]  Martin Fodslette Meiller.  A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning , 1993 .

[11]  Christopher Niezrecki,et al.  Vibration-Based Damage Detection in Wind Turbine Blades using Phase-Based Motion Estimation and Motion Magnification , 2018, ArXiv.

[12]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[13]  Yoshua Bengio,et al.  Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.

[14]  Saman Farhangdoust,et al.  Interaction of Higher Modes in Nonlinear Free Vibration of Stiffened Rectangular Plates , 2017 .

[15]  Arvin Ebrahimkhanlou,et al.  Single-sensor acoustic emission source localization in plate-like structures: a deep learning approach , 2018, Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[16]  Alexander Mordvintsev,et al.  Inceptionism: Going Deeper into Neural Networks , 2015 .

[17]  Salvatore Salamone,et al.  Simultaneous Localization and Classification of Acoustic Emission Sources in Plates using a Guided Wave-Based Sparse Reconstruction , 2017 .

[18]  Salvatore Salamone,et al.  A probabilistic framework for single-sensor acoustic emission source localization in thin metallic plates , 2017 .

[19]  Zhu Mao,et al.  Structural operating deflection shape estimation via a hybrid computer-vision algorithm , 2018, Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[20]  W. Lance Richards,et al.  Large Scale Applications Using FBG Sensors: Determination of In-Flight Loads and Shape of a Composite Aircraft Wing , 2016 .

[21]  Victor Giurgiutiu,et al.  Guided wave crack detection and size estimation in stiffened structures , 2018, Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[22]  Christopher Niezrecki,et al.  Mode extraction on wind turbine blades via phase-based video motion estimation , 2017, Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[23]  S. Salamone,et al.  Acoustic emission source localization in thin metallic plates: A single‐sensor approach based on multimodal edge reflections , 2017, Ultrasonics.

[24]  Tribikram Kundu,et al.  A new wave front shape‐based approach for acoustic source localization in an anisotropic plate without knowing its material properties , 2018, Ultrasonics.

[25]  Salvatore Salamone,et al.  A guided ultrasonic imaging approach in isotropic plate structures using edge reflections , 2016, SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[26]  Tribikram Kundu,et al.  Acoustic source localization. , 2014, Ultrasonics.

[27]  Martin J. Wainwright,et al.  Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions , 2011, ICML.

[28]  Jiao Jingpin,et al.  Acoustic emission source location methods using mode and frequency analysis , 2008 .

[29]  Xin Zhou,et al.  Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data , 2016 .

[30]  Salvatore Salamone,et al.  Probabilistic location estimation of acoustic emission sources in isotropic plates with one sensor , 2017, Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[31]  John M. Gary,et al.  Reflections of AE Waves in Finite Plates: Finite Element Modeling and Experimental Measurements , 1999 .

[32]  Teruo Kishi,et al.  Two-dimensional AE source location with two sensors in thin CFRP plates , 2001 .

[33]  Salvatore Salamone,et al.  A Deep Learning Approach for Single-sensor Acoustic Emission Source Localization in Plate-like Structures , 2017 .

[34]  Miao He,et al.  Deep Learning Based Approach for Bearing Fault Diagnosis , 2017, IEEE Transactions on Industry Applications.

[35]  John M. Gary,et al.  Effects of Lateral Plate Dimensions on Acoustic Emission Signals From Dipole Sources , 2001 .

[36]  Diego Cabrera,et al.  Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals , 2016 .

[37]  J Dual,et al.  One sensor acoustic emission localization in plates. , 2016, Ultrasonics.

[38]  Marvin A. Hamstad,et al.  Practical Aspects of Acoustic Emission Source Location by a Wavelet Transform | NIST , 2003 .

[39]  Kevin P. Murphy,et al.  Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.

[40]  T. Kundu,et al.  Point of impact prediction in isotropic and anisotropic plates from the acoustic emission data. , 2007, The Journal of the Acoustical Society of America.

[41]  T Kundu,et al.  A two-step hybrid technique for accurately localizing acoustic source in anisotropic structures without knowing their material properties. , 2015, Ultrasonics.

[42]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[43]  Karen Margaret Holford,et al.  Acoustic Emission Source Location , 1999 .

[44]  Z. Sharif-Khodaei,et al.  Determination of impact location on composite stiffened panels , 2012 .