A Deep Neural Network Based Model for a Kind of Magnetorheological Dampers

In this paper, a deep neural network based model for a set of small-scale magnetorheological dampers (MRD) is developed where relevant parameters that have a physical meaning are inputs to the model. An experimental platform and a 3D-printing rapid prototyping facility provided a set of different conditions including MRD filled with two different MR fluids, which were used to train a Deep Neural Network (DNN), which is the core of the proposed model. Testing results indicate the model could forecast the hysteretic response of magnetorheological dampers for different load conditions and various physical configurations.

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

[2]  N. Belfiore,et al.  A review on parametric dynamic models of magnetorheological dampers and their characterization methods , 2018 .

[3]  Pravin M. Singru,et al.  Development of an Experimental Model for a Magnetorheological Damper Using Artificial Neural Networks (Levenberg-Marquardt Algorithm) , 2016 .

[4]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[5]  Hong Chen,et al.  A New Delay-Compensation Scheme for Networked Control Systems in Controller Area Networks , 2018, IEEE Transactions on Industrial Electronics.

[6]  Siti Anom Ahmad,et al.  Self-Calibration Algorithm for a Pressure Sensor with a Real-Time Approach Based on an Artificial Neural Network , 2018, Sensors.

[7]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..

[8]  Qiang Zhou,et al.  Intelligent control for braking-induced longitudinal vibration responses of floating-type railway bridges , 2009 .

[9]  Nikolaos Doulamis,et al.  Deep Learning for Computer Vision: A Brief Review , 2018, Comput. Intell. Neurosci..

[10]  Jong-Myon Kim,et al.  A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis , 2017, Sensors.

[11]  Tudor Sireteanu,et al.  Semi-active Suspension Control: Improved Vehicle Ride and Road Friendliness , 2008 .

[12]  Song Han,et al.  Learning both Weights and Connections for Efficient Neural Network , 2015, NIPS.

[13]  S. K. Mangal,et al.  Geometric parameter optimization of magneto-rheological damper using design of experiment technique , 2015, International Journal of Mechanical and Materials Engineering.

[14]  Yurong Liu,et al.  A survey of deep neural network architectures and their applications , 2017, Neurocomputing.

[15]  C. Willmott,et al.  Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance , 2005 .

[16]  Ubaidillah,et al.  Characterization and modeling of a new magnetorheological damper with meandering type valve using neuro-fuzzy , 2017 .

[17]  Wei Zhu,et al.  An efficient parameters identification method of normalized Bouc-Wen model for MR damper , 2019, Journal of Sound and Vibration.

[18]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[19]  H. Mekki,et al.  Artificial neural network-based modelling and fault detection of partial shaded photovoltaic modules , 2016, Simul. Model. Pract. Theory.

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

[21]  Matthew D. Zeiler.  ADADELTA: An Adaptive Learning Rate Method , 2012, ArXiv.

[22]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[23]  Jungwhee Lee,et al.  Structural Damage Detection in the Frequency Domain using Neural Networks , 2007 .

[24]  Y. Peng,et al.  Full-Scale Simulations of Magnetorheological Damper for Implementation of Semi-Actively Structural Control , 2018, Journal of Mechanics.

[25]  Vasile Marinca,et al.  Application of the optimal homotopy asymptotic method to nonlinear Bingham fluid dampers , 2015 .

[26]  Sebastian Ruder,et al.  An overview of gradient descent optimization algorithms , 2016, Vestnik komp'iuternykh i informatsionnykh tekhnologii.

[27]  S. M. Dumne,et al.  Seismic response analysis of adjacent buildings connected with MR dampers , 2010 .

[28]  Jeong-Hoi Koo,et al.  A comprehensive analysis of the response time of MR dampers , 2006 .

[29]  K. Gnana Sheela,et al.  Review on Methods to Fix Number of Hidden Neurons in Neural Networks , 2013 .

[30]  O. V. Stryk,et al.  Modelling and simulation of electro- and magnetorheological fluid dampers , 2002 .

[31]  Jianchun Li,et al.  Nonparametric modeling of magnetorheological elastomer base isolator based on artificial neural network optimized by ant colony algorithm , 2015 .

[32]  Honghai Liu,et al.  State of the Art in Vehicle Active Suspension Adaptive Control Systems Based on Intelligent Methodologies , 2008, IEEE Transactions on Intelligent Transportation Systems.

[33]  Jie Li,et al.  Parameter identification of modified Bouc–Wen model and analysis of size effect of magnetorheological dampers , 2017 .

[34]  Kalyanmoy Deb,et al.  A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications , 2017, IEEE Transactions on Evolutionary Computation.

[35]  Seung-Bok Choi,et al.  Non-dimensional analysis and design of a magnetorheological damper , 2005 .

[36]  Hui Pang,et al.  Variable universe fuzzy control for vehicle semi-active suspension system with MR damper combining fuzzy neural network and particle swarm optimization , 2018, Neurocomputing.

[37]  Seung-Ik Lee,et al.  A hysteresis model for the field-dependent damping force of a magnetorheological damper , 2001 .

[38]  S. Olutunde Oyadiji,et al.  Design, modelling and testing of magnetorheological (MR) dampers using analytical flow solutions , 2008 .

[39]  Miao Yu,et al.  Comparative research on semi-active control strategies for magneto-rheological suspension , 2010 .

[40]  M. Cheng,et al.  Design, Analysis, and Experimental Evaluation of a Magnetorheological Damper With Meandering Magnetic Circuit , 2018, IEEE Transactions on Magnetics.

[41]  Yoshua Bengio,et al.  Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..

[42]  F. Windmeijer,et al.  R-Squared Measures for Count Data Regression Models With Applications to Health-Care Utilization , 1996 .

[43]  Heidar Ali Talebi,et al.  Application of RBF neural networks and unscented transformation in probabilistic power-flow of microgrids including correlated wind/PV units and plug-in hybrid electric vehicles , 2017, Simul. Model. Pract. Theory.

[44]  F. Gordaninejad,et al.  A Low Force Magneto-rheological (MR) Fluid Damper: Design, Fabrication and Characterization , 2007 .

[45]  Fengjun Yan,et al.  Hydrothermal Aging Factor Estimation for Two-Cell Diesel-Engine SCR Systems via a Dual Time-Scale Unscented Kalman Filter , 2020, IEEE Transactions on Industrial Electronics.

[46]  Ruben Morales-Menendez,et al.  Magnetorheological damper—an experimental study , 2012 .

[47]  Tahsin Engin,et al.  Optimal design of MR damper via finite element analyses of fluid dynamic and magnetic field , 2012 .

[48]  Markus Schweizer,et al.  Semi Active Suspension Control Design For Vehicles , 2016 .

[49]  Sami F. Masri,et al.  Modeling the oscillatory dynamic behaviour of electrorheological materials in shear , 1992 .

[50]  Ubaidillah,et al.  Modelling, characterisation and force tracking control of a magnetorheological damper under harmonic excitation , 2011, Int. J. Model. Identif. Control..

[51]  María Jesús López Boada,et al.  Real-Time Vehicle Roll Angle Estimation Based on Neural Networks in IoT Low-Cost Devices , 2018, Sensors.

[52]  Wei-Hsin Liao,et al.  Optimal design of a magnetorheological damper used in smart prosthetic knees , 2017 .

[53]  Amy Loutfi,et al.  A review of unsupervised feature learning and deep learning for time-series modeling , 2014, Pattern Recognit. Lett..

[54]  Jun Wang,et al.  An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition , 2018, Neurocomputing.

[55]  Léon Bottou,et al.  Large-Scale Machine Learning with Stochastic Gradient Descent , 2010, COMPSTAT.

[56]  Michael S. Lew,et al.  Deep learning for visual understanding: A review , 2016, Neurocomputing.

[57]  Devdutt Singh.  Passenger body vibration control in active quarter car model using ANFIS based super twisting sliding mode controller , 2018, Simul. Model. Pract. Theory.