Review of Vibration-Based Structural Health Monitoring Using Deep Learning

[1]  Mia Loccufier,et al.  Crack identification method in beam-like structures using changes in experimentally measured frequencies and Particle Swarm Optimization , 2018 .

[2]  Hyunseok Oh,et al.  Scalable and Unsupervised Feature Engineering Using Vibration-Imaging and Deep Learning for Rotor System Diagnosis , 2018, IEEE Transactions on Industrial Electronics.

[3]  Guoxin Zhang,et al.  Coal-Rock Recognition in Top Coal Caving Using Bimodal Deep Learning and Hilbert-Huang Transform , 2017 .

[4]  Bo Tang,et al.  Intelligent Fault Diagnosis of the High-Speed Train With Big Data Based on Deep Neural Networks , 2017, IEEE Transactions on Industrial Informatics.

[5]  Diego Cabrera,et al.  Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning , 2016, Sensors.

[6]  Raimondo Betti,et al.  A structural health monitoring strategy using cepstral features , 2014 .

[7]  Amiya R Mohanty,et al.  Technical Note: Gearbox Health Monitoring through Multiresolution Fourier Transform of Vibration and Current Signals , 2006 .

[8]  Wang,et al.  Fault Diagnosis of Rolling Bearing Based on Multiscale Intrinsic Mode Function Permutation Entropy and a Stacked Sparse Denoising Autoencoder , 2019, Applied Sciences.

[9]  Anindya Ghoshal,et al.  Damage detection using finite element and laser operational deflection shapes , 2002 .

[10]  Yang Wang,et al.  A clustering approach for structural health monitoring on bridges , 2016 .

[11]  Leonard Ziemiański,et al.  Neural networks in mechanics of structures and materials – new results and prospects of applications , 2001 .

[12]  Bin Li,et al.  Early Fault Detection of Machine Tools Based on Deep Learning and Dynamic Identification , 2019, IEEE Transactions on Industrial Electronics.

[13]  Yun Zhang,et al.  Analysis of Feature Extracting Ability for Cutting State Monitoring Using Deep Belief Networks , 2015 .

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

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

[16]  I T Joliffe,et al.  Principal component analysis and exploratory factor analysis , 1992, Statistical methods in medical research.

[17]  P. Konar,et al.  Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs) , 2011, Appl. Soft Comput..

[18]  Shuai Yang,et al.  Spur Gear Fault Diagnosis Using a Multilayer Gated Recurrent Unit Approach With Vibration Signal , 2019, IEEE Access.

[19]  Amiya R Mohanty,et al.  Vibration and current transient monitoring for gearbox fault detection using multiresolution Fourier transform , 2008 .

[20]  Ibrahim Esat,et al.  ARTIFICIAL NEURAL NETWORK BASED FAULT DIAGNOSTICS OF ROTATING MACHINERY USING WAVELET TRANSFORMS AS A PREPROCESSOR , 1997 .

[21]  Moncef Gabbouj,et al.  Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks , 2017 .

[22]  Hani G. Melhem,et al.  DAMAGE DETECTION IN CONCRETE BY FOURIER AND WAVELET ANALYSES , 2003 .

[23]  Hui Li,et al.  Gear fault detection and diagnosis under speed-up condition based on order cepstrum and radial basis function neural network , 2009 .

[24]  V. G. Idichandy,et al.  ART-based multiple neural networks for monitoring offshore platforms , 1996 .

[25]  Darian M. Onchis,et al.  A deep learning approach to condition monitoring of cantilever beams via time-frequency extended signatures , 2019, Comput. Ind..

[26]  Samir Khatir,et al.  Structural health monitoring using modal strain energy damage indicator coupled with teaching-learning-based optimization algorithm and isogoemetric analysis , 2019, Journal of Sound and Vibration.

[27]  David He,et al.  Using Deep Learning-Based Approach to Predict Remaining Useful Life of Rotating Components , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[28]  Wei Zhang,et al.  A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals , 2017, Sensors.

[29]  M. Lemistre,et al.  Structural health monitoring system based on diffracted Lamb wave analysis by multiresolution processing , 2001 .

[30]  Darryll J. Pines,et al.  Structural health monitoring using empirical mode decomposition and the Hilbert phase , 2006 .

[31]  Mohammad Noori,et al.  Wavelet-Based Approach for Structural Damage Detection , 2000 .

[32]  Robert B. Randall,et al.  Damage identification based on response-only measurements using cepstrum analysis and artificial neural networks , 2014 .

[33]  Arturo Garcia-Perez,et al.  MUSIC‐ANN Analysis for Locating Structural Damages in a Truss‐Type Structure by Means of Vibrations , 2012, Comput. Aided Civ. Infrastructure Eng..

[34]  Konstantinos C. Gryllias,et al.  Rolling element bearing fault detection in industrial environments based on a K-means clustering approach , 2011, Expert Syst. Appl..

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

[36]  Pratyay Konar,et al.  Tri-Axial Vibration Analysis Using Data Mining for Multi Class Fault Diagnosis in Induction Motor , 2015, MIKE.

[37]  Weihua Li,et al.  Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network , 2017, IEEE Transactions on Instrumentation and Measurement.

[38]  Robert B. Randall,et al.  Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study , 2015 .

[39]  Jiangtao Wen,et al.  Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning , 2018, IEEE Transactions on Instrumentation and Measurement.

[40]  Girish Kumar Singh,et al.  Vibration signal analysis using wavelet transform for isolation and identification of electrical faults in induction machine , 2004 .

[41]  Soo-Chul Lim,et al.  Structural vibration-based classification and prediction of delamination in smart composite laminates using deep learning neural network , 2019, Composites Part B: Engineering.

[42]  Cajetan M. Akujuobi,et al.  An approach to vibration analysis using wavelets in an application of aircraft health monitoring , 2007 .

[43]  Xinqing Wang,et al.  A hydraulic fault diagnosis method based on sliding-window spectrum feature and deep belief network , 2017 .

[44]  Issam Abu-Mahfouz,et al.  Drilling wear detection and classification using vibration signals and artificial neural network , 2003 .

[45]  Filipe Magalhães,et al.  Vibration based structural health monitoring of an arch bridge: From automated OMA to damage detection , 2012 .

[46]  Pieter Abbeel,et al.  Autonomous Helicopter Aerobatics through Apprenticeship Learning , 2010, Int. J. Robotics Res..

[47]  Ruoyu Li,et al.  Rotational Machine Health Monitoring and Fault Detection Using EMD-Based Acoustic Emission Feature Quantification , 2012, IEEE Transactions on Instrumentation and Measurement.

[48]  Kil To Chong,et al.  Induction Machine Condition Monitoring Using Neural Network Modeling , 2007, IEEE Transactions on Industrial Electronics.

[49]  Roger Serra,et al.  Damage detection and localization in composite beam structures based on vibration analysis , 2016 .

[50]  K. I. Ramachandran,et al.  A comparative study on classification of features by SVM and PSVM extracted using Morlet wavelet for fault diagnosis of spur bevel gear box , 2008, Expert Syst. Appl..

[51]  Jun He,et al.  Unsupervised Fault Diagnosis of a Gear Transmission Chain Using a Deep Belief Network , 2017, Sensors.

[52]  Liang Gao,et al.  A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method , 2018, IEEE Transactions on Industrial Electronics.

[53]  Constantinos Soutis,et al.  Damage detection in composite materials using frequency response methods , 2002 .

[54]  Magd Abdel Wahab,et al.  Damage detection in CFRP composite beams based on vibration analysis using proper orthogonal decomposition method with radial basis functions and cuckoo search algorithm , 2018 .

[55]  Mehrisadat Makki Alamdari,et al.  A spectral-based clustering for structural health monitoring of the Sydney Harbour Bridge , 2017 .

[56]  A. K. Wadhwani,et al.  Application of ANN, Fuzzy Logic and Wavelet Transform in machine fault diagnosis using vibration signal analysis , 2010 .

[57]  Achmad Zubaydi,et al.  Damage identification in a ship’s structure using neural networks , 2002 .

[58]  Xiangrong Liu,et al.  One-Dimensional CNN-Based Intelligent Recognition of Vibrations in Pipeline Monitoring With DAS , 2019, Journal of Lightwave Technology.

[59]  Maria Q. Feng,et al.  DAMAGE ASSESSMENT OF JACKETED RC COLUMNS USING VIBRATION TESTS , 1999 .

[60]  Daniel J. Inman,et al.  Electro-Mechanical Impedance-Based Wireless Structural Health Monitoring Using PCA-Data Compression and k-means Clustering Algorithms , 2008 .

[61]  Zhishen Wu,et al.  Deep Learning-Based Damage, Load and Support Identification for a Composite Pipeline by Extracting Modal Macro Strains from Dynamic Excitations , 2018, Applied Sciences.

[62]  Chao Liu,et al.  A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults , 2019, Knowl. Based Syst..

[63]  Yang Zhao,et al.  Vibration signal analysis and fault diagnosis of bogies of the high-speed train based on deep neural networks , 2017 .

[64]  Enrico Zio,et al.  Artificial intelligence for fault diagnosis of rotating machinery: A review , 2018, Mechanical Systems and Signal Processing.

[65]  Junhong Park,et al.  Determination of Clamping Force Using Bolt Vibration Responses during the Tightening Process , 2019, Applied Sciences.

[66]  J. Rafiee,et al.  INTELLIGENT CONDITION MONITORING OF A GEARBOX USING ARTIFICIAL NEURAL NETWORK , 2007 .

[67]  K. Worden,et al.  The application of machine learning to structural health monitoring , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

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

[69]  Yun-Lai Zhou,et al.  Multiple damage detection in composite beams using Particle Swarm Optimization and Genetic Algorithm , 2017 .

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

[71]  Samir Khatir,et al.  A computational approach for crack identification in plate structures using XFEM, XIGA, PSO and Jaya algorithm , 2019, Theoretical and Applied Fracture Mechanics.

[72]  A. C. Neves,et al.  Structural health monitoring of bridges: a model-free ANN-based approach to damage detection , 2017, Journal of Civil Structural Health Monitoring.

[73]  Noureddine Zerhouni,et al.  Enabling Health Monitoring Approach Based on Vibration Data for Accurate Prognostics , 2015, IEEE Transactions on Industrial Electronics.

[74]  Xuepeng Chang,et al.  Event-Triggered Adaptive Control for Uncertain Constrained Nonlinear Systems With Its Application , 2020, IEEE Transactions on Industrial Informatics.

[75]  M. Satyam,et al.  Cepstrum Analysis -An Advanced Technique in Vibration Analysis of Defects in Rotating Machinery , 1994 .

[76]  Qiang Miao,et al.  Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators , 2018, IEEE Access.

[77]  Fenghua Wang,et al.  Fault Diagnosis of On-Load Tap-Changer in Converter Transformer Based on Time–Frequency Vibration Analysis , 2016, IEEE Transactions on Industrial Electronics.

[78]  Michael Unser,et al.  Deep Convolutional Neural Network for Inverse Problems in Imaging , 2016, IEEE Transactions on Image Processing.

[79]  Hongmei Liu,et al.  Rolling Bearing Fault Diagnosis Based on STFT-Deep Learning and Sound Signals , 2016 .

[80]  Haidong Shao,et al.  A novel deep autoencoder feature learning method for rotating machinery fault diagnosis , 2017 .

[81]  Jiong Tang,et al.  Preprocessing-Free Gear Fault Diagnosis Using Small Datasets With Deep Convolutional Neural Network-Based Transfer Learning , 2017, IEEE Access.

[82]  Teik C. Lim,et al.  Sound quality prediction of vehicle interior noise using deep belief networks , 2016 .

[83]  Yang Yu,et al.  An architecture of deep learning network based on ensemble empirical mode decomposition in precise identification of bearing vibration signal , 2019, Journal of Mechanical Science and Technology.

[84]  Wei-Xin Ren,et al.  EMD-based stochastic subspace identification of structures from operational vibration measurements , 2005 .

[85]  Samir Khatir,et al.  Damage assessment in structures using combination of a modified Cornwell indicator and genetic algorithm , 2018, Engineering Structures.

[86]  Noureddine Zerhouni,et al.  Bearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression , 2015, IEEE Transactions on Instrumentation and Measurement.

[87]  Chen Lu,et al.  Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification , 2017, Signal Process..

[88]  Robert X. Gao,et al.  Hilbert–Huang Transform-Based Vibration Signal Analysis for Machine Health Monitoring , 2006, IEEE Transactions on Instrumentation and Measurement.

[89]  Robert X. Gao,et al.  PCA-based feature selection scheme for machine defect classification , 2004, IEEE Transactions on Instrumentation and Measurement.

[90]  Robert X. Gao,et al.  Non-stationary signal processing for bearing health monitoring , 2006, Int. J. Manuf. Res..

[91]  S. K. Tso,et al.  Impact-acoustics-based health monitoring of tile-wall bonding integrity using principal component analysis , 2006 .

[92]  Jozue Vieira Filho,et al.  A New Structural Health Monitoring Strategy Based on PZT Sensors and Convolutional Neural Network , 2018, Sensors.

[93]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[94]  Samir Khatir,et al.  Fast simulations for solving fracture mechanics inverse problems using POD-RBF XIGA and Jaya algorithm , 2019, Engineering Fracture Mechanics.

[95]  D. H. Wang,et al.  Health Monitoring and Diagnosis for Flexible Structures with PVDF Piezoelectric Film Sensor Array , 2000 .

[96]  Limin Sun,et al.  Structural health monitoring by using a sparse coding-based deep learning algorithm with wireless sensor networks , 2014, Personal and Ubiquitous Computing.

[97]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[98]  Magd Abdel Wahab,et al.  A damage identification technique for beam-like and truss structures based on FRF and Bat Algorithm , 2018, Comptes Rendus Mécanique.