A novel digital twin model for dynamical updating and real-time mapping of local defect extension in rolling bearings

[1]  Xin Lu,et al.  A review for control theory and condition monitoring on construction robots , 2023, J. Field Robotics.

[2]  Kecheng Zhang,et al.  A two-stage sound-vibration signal fusion method for weak fault detection in rolling bearing systems , 2022, Mechanical Systems and Signal Processing.

[3]  Xueguan Song,et al.  Building a Trustworthy Product-Level Shape-Performance Integrated Digital Twin With Multifidelity Surrogate Model , 2021, Journal of Mechanical Design.

[4]  Xin Chen,et al.  Digital twins-based smart manufacturing system design in Industry 4.0: A review , 2021, Journal of Manufacturing Systems.

[5]  Wei Sun,et al.  Designing a Shape–Performance Integrated Digital Twin Based on Multiple Models and Dynamic Data: A Boom Crane Example , 2021 .

[6]  Fei Tao,et al.  Digital twin enhanced fault prediction for the autoclave with insufficient data , 2021, Journal of Manufacturing Systems.

[7]  Yi Qin,et al.  Data-Model Combined Driven Digital Twin of Life-Cycle Rolling Bearing , 2021, IEEE Transactions on Industrial Informatics.

[8]  Xiaochen Zhang,et al.  A fault diagnosis method based on improved convolutional neural network for bearings under variable working conditions , 2021 .

[9]  Huaitao Shi,et al.  Investigation of the orbit-spinning behaviors of the outer ring in a full ceramic ball bearing-steel pedestal system in wide temperature ranges , 2021 .

[10]  Mohamed Haddar,et al.  Digital twin-driven machine learning: ball bearings fault severity classification , 2020 .

[11]  Yi Qin,et al.  Multiple-degree-of-freedom dynamic model of rolling bearing with a localized surface defect , 2020 .

[12]  Konstantinos Gryllias,et al.  Domain Adaptation Digital Twin for Rolling Element Bearing Prognostics , 2020 .

[13]  TG Ritto,et al.  Digital twin, physics-based model, and machine learning applied to damage detection in structures , 2020, Mechanical Systems and Signal Processing.

[14]  X. Bai,et al.  Model-based uneven loading condition monitoring of full ceramic ball bearings in starved lubrication , 2020 .

[15]  Yaguo Lei,et al.  A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings , 2020, IEEE Transactions on Reliability.

[16]  Sankaran Mahadevan,et al.  Digital twin approach for damage-tolerant mission planning under uncertainty , 2020 .

[17]  Joaquim R. R. A. Martins,et al.  A Python surrogate modeling framework with derivatives , 2019, Adv. Eng. Softw..

[18]  He Zhang,et al.  Digital Twin in Industry: State-of-the-Art , 2019, IEEE Transactions on Industrial Informatics.

[19]  Thomas R. Kurfess,et al.  Signal processing techniques for rolling element bearing spall size estimation , 2019, Mechanical Systems and Signal Processing.

[20]  Ahmad Forouzantabar,et al.  Rolling bearing fault detection of electric motor using time domain and frequency domain features extraction and ANFIS , 2019, IET Electric Power Applications.

[21]  Robert X. Gao,et al.  Digital Twin for rotating machinery fault diagnosis in smart manufacturing , 2018, Int. J. Prod. Res..

[22]  Renata Klein,et al.  A physics-based algorithm for the estimation of bearing spall width using vibrations , 2018 .

[23]  Wei Chen,et al.  Dynamic Analysis of a Cylindrical Roller Bearing With Time-Varying Localized Defects on Raceways , 2015 .

[24]  Idriss El-Thalji,et al.  Dynamic modelling of wear evolution in rolling bearings , 2015 .

[25]  Xuezhi Zhao,et al.  Selection of effective singular values using difference spectrum and its application to fault diagnosis of headstock , 2011 .

[26]  Robert B. Randall,et al.  Vibration response of spalled rolling element bearings: Observations, simulations and signal processing techniques to track the spall size , 2011 .

[27]  Jun Gao,et al.  Local Outlier Detection Based on Kernel Regression , 2010, 2010 20th International Conference on Pattern Recognition.

[28]  Michael W. Grieves.  Product lifecycle management: the new paradigm for enterprises , 2005 .

[29]  P. D. McFadden,et al.  Model for the vibration produced by a single point defect in a rolling element bearing , 1984 .

[30]  Siliang Lu,et al.  Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning , 2021, Reliab. Eng. Syst. Saf..

[31]  Andrew Y. C. Nee,et al.  Digital twin driven prognostics and health management for complex equipment , 2018 .

[32]  S. P. Harsha,et al.  Failure Evaluation of Ball Bearing for Prognostics , 2016 .