A review of deep learning in the study of materials degradation

Deep learning is revolutionising the way that many industries operate, providing a powerful method to interpret large quantities of data automatically and relatively quickly. Deterioration is often multi-factorial and difficult to model deterministically due to limits in measurability, or unknown variables. Deploying deep learning tools to the field of materials degradation should be a natural fit. In this paper, we review the current research into deep learning for detection, modelling and planning for material deterioration. Driving such research are factors such as budget reductions, increasing safety and increasing detection reliability. Based on the available literature, researchers are making headway, but several challenges remain, not least of which is the development of large training data sets and the computational intensity of many of these deep learning models.

[1]  Steven Verstockt,et al.  Convolutional Neural Network Based Fault Detection for Rotating Machinery , 2016 .

[2]  Alberto Ortiz,et al.  Visual Inspection of Vessels by Means of a Micro-Aerial Vehicle: An Artificial Neural Network Approach for Corrosion Detection , 2015, ROBOT.

[3]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[4]  Xiang Li,et al.  Remaining useful life estimation in prognostics using deep convolution neural networks , 2018, Reliab. Eng. Syst. Saf..

[5]  Robert X. Gao,et al.  Deep learning and its applications to machine health monitoring , 2019, Mechanical Systems and Signal Processing.

[6]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[7]  Laurence T. Yang,et al.  A survey on deep learning for big data , 2018, Inf. Fusion.

[8]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[9]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

[10]  Min Meng,et al.  Ultrasonic signal classification and imaging system for composite materials via deep convolutional neural networks , 2017, Neurocomputing.

[11]  Jannik Fritsch,et al.  A new performance measure and evaluation benchmark for road detection algorithms , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[12]  Ngai-Man Cheung,et al.  Aircraft Fuselage Defect Detection using Deep Neural Networks , 2017, ArXiv.

[13]  Xiaoli Li,et al.  Deep Convolutional Neural Network Based Regression Approach for Estimation of Remaining Useful Life , 2016, DASFAA.

[14]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[15]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[16]  D. Dimiduk,et al.  Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering , 2018, Integrating Materials and Manufacturing Innovation.

[17]  Ye Wei,et al.  Automatic Detection of Welding Defects using Deep Neural Network , 2018 .

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

[19]  Kunihiko Fukushima,et al.  Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position , 1982, Pattern Recognit..

[20]  Hod Lipson,et al.  Understanding Neural Networks Through Deep Visualization , 2015, ArXiv.

[21]  Yoshua Bengio,et al.  Deep Learning of Representations for Unsupervised and Transfer Learning , 2011, ICML Unsupervised and Transfer Learning.

[22]  S. Gin,et al.  Material degradation foreseen in the very long term: the case of glasses and ferrous metals , 2017, npj Materials Degradation.

[23]  David Tuck,et al.  The identification of pitting and crevice corrosion using a neural network , 1993, Proceedings 1993 The First New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems.

[24]  Vijay Kumar,et al.  U-Net for MAV-based Penstock Inspection: an Investigation of Focal Loss in Multi-class Segmentation for Corrosion Identification , 2018, ArXiv.

[25]  Liang Guo,et al.  A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines , 2018, Neurocomputing.

[26]  Jingwen Tian,et al.  Corrosion Detection System for Oil Pipelines Based on Multi-sensor Data Fusion by Improved Simulated Annealing Neural Network , 2006, 2006 International Conference on Communication Technology.

[27]  Ruqiang Yan,et al.  Machine health monitoring with LSTM networks , 2016, 2016 10th International Conference on Sensing Technology (ICST).

[28]  Hadi Meidani,et al.  Deep Learning for Accelerated Seismic Reliability Analysis of Transportation Networks , 2017, Comput. Aided Civ. Infrastructure Eng..

[29]  W. P. Winfree,et al.  Neural Network Based Processing of Thermal NDE Data for Corrosion Detection , 1993 .

[30]  Li Lin,et al.  Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network , 2016, 2016 IEEE International Conference on Aircraft Utility Systems (AUS).

[31]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[32]  Luca Petricca,et al.  CORROSION DETECTION USING A.I. : A COMPARISON OF STANDARD COMPUTER VISION TECHNIQUES AND DEEP LEARNING MODEL , 2016 .

[33]  Ruqiang Yan,et al.  Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks , 2017, Sensors.

[34]  Yang Liu,et al.  Automated Pixel‐Level Pavement Crack Detection on 3D Asphalt Surfaces Using a Deep‐Learning Network , 2017, Comput. Aided Civ. Infrastructure Eng..

[35]  Rama Chellappa,et al.  Deep Multitask Learning for Railway Track Inspection , 2015, IEEE Transactions on Intelligent Transportation Systems.

[36]  Mohammad R. Jahanshahi,et al.  Evaluation of deep learning approaches based on convolutional neural networks for corrosion detection , 2018 .

[37]  Kay Chen Tan,et al.  Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[38]  Brian B. Avants,et al.  The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) , 2015, IEEE Transactions on Medical Imaging.

[39]  Zhaozheng Hu,et al.  Grid-based pavement crack analysis using deep learning , 2017, 2017 4th International Conference on Transportation Information and Safety (ICTIS).

[40]  Rama Chellappa,et al.  Material classification and semantic segmentation of railway track images with deep convolutional neural networks , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[41]  Zhi Qiang Cai,et al.  AI-facilitated coating corrosion assessment system for productivity enhancement , 2018, 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA).

[42]  Yi Zhang,et al.  Using Machine Learning for Scientific Discovery in Electronic Quantum Matter Visualization Experiments , 2018 .

[43]  Benjamin Schrauwen,et al.  Defect Detection in Reinforced Concrete Using Random Neural Architectures , 2014, Comput. Aided Civ. Infrastructure Eng..

[44]  Jürgen Schmidhuber,et al.  Learning to Forget: Continual Prediction with LSTM , 2000, Neural Computation.

[45]  Yi-Zhou Lin,et al.  Structural Damage Detection with Automatic Feature‐Extraction through Deep Learning , 2017, Comput. Aided Civ. Infrastructure Eng..

[46]  Oral Büyüköztürk,et al.  Autonomous Structural Visual Inspection Using Region‐Based Deep Learning for Detecting Multiple Damage Types , 2018, Comput. Aided Civ. Infrastructure Eng..

[47]  Geoffrey E. Hinton,et al.  Learning representations by back-propagation errors, nature , 1986 .

[48]  P. Mahadevan,et al.  An overview , 2007, Journal of Biosciences.

[49]  J. A. Hernandez-Perez,et al.  Artificial Intelligence for the Assessment on the Corrosion Conditions Diagnosis of Transmission Line Tower Foundations , 2012 .

[50]  Ming Zhao,et al.  A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox , 2017 .

[51]  Octavian Postolache,et al.  Detection and characterization of defects using GMR probes and artificial neural networks , 2011, Comput. Stand. Interfaces.

[52]  L. Sadowski.  Non-destructive investigation of corrosion current density in steel reinforced concrete by artificial neural networks , 2013 .

[53]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[54]  Rui Xiong,et al.  A LSTM-RNN method for the lithuim-ion battery remaining useful life prediction , 2017, 2017 Prognostics and System Health Management Conference (PHM-Harbin).

[55]  Yun-Kyu An,et al.  Deep learning-based concrete crack detection using hybrid images , 2018, Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[56]  Juan de Lara,et al.  Supporting user-oriented analysis for multi-view domain-specific visual languages , 2009, Inf. Softw. Technol..

[57]  Li Lin,et al.  Remaining useful life estimation of engineered systems using vanilla LSTM neural networks , 2018, Neurocomputing.

[58]  Rajat Raina,et al.  Large-scale deep unsupervised learning using graphics processors , 2009, ICML '09.

[59]  Jürgen Schmidhuber,et al.  Learning to forget: continual prediction with LSTM , 1999 .