Intelligent Predictive Maintenance and Remote Monitoring Framework for Industrial Equipment Based on Mixed Reality
暂无分享,去创建一个
Dimitris Mourtzis | John Angelopoulos | Nikos Panopoulos | D. Mourtzis | J. Angelopoulos | N. Panopoulos
[1] Theodoros H. Loutas,et al. Remaining Useful Life Estimation in Rolling Bearings Utilizing Data-Driven Probabilistic E-Support Vectors Regression , 2013, IEEE Transactions on Reliability.
[2] Bin Liang,et al. Remaining useful life prediction of aircraft engine based on degradation pattern learning , 2017, Reliab. Eng. Syst. Saf..
[3] Dimitris Mourtzis,et al. An Augmented Reality Collaborative Product Design Cloud-Based Platform in the Context of Learning Factory , 2020 .
[4] Yi Shang,et al. Deep learning for prognostics and health management: State of the art, challenges, and opportunities , 2020 .
[5] Wei Zhang,et al. Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction , 2019, Reliab. Eng. Syst. Saf..
[6] Yaguo Lei. Remaining useful life prediction , 2017 .
[7] Jan Zenisek,et al. Data-Driven Maintenance: Combining Predictive Maintenance and Mixed Reality-supported Remote Assistance , 2020 .
[8] Dimitris Mourtzis,et al. Augmented Reality based Visualization of CAM Instructions towards Industry 4.0 paradigm: a CNC Bending Machine case study , 2018 .
[9] Monica Bordegoni,et al. Supporting Remote Maintenance in Industry 4.0 through Augmented Reality , 2017 .
[10] Tariq Masood,et al. Augmented reality in support of intelligent manufacturing - A systematic literature review , 2020, Comput. Ind. Eng..
[11] Dimitris Mourtzis,et al. Cloud-Based Augmented Reality Remote Maintenance Through Shop-Floor Monitoring: A Product-Service System Approach , 2017 .
[12] Christopher Leckie,et al. High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning , 2016, Pattern Recognit..
[13] Al-Sakib Khan Pathan,et al. Data Analytics : Concepts, Techniques, and Applications , 2018 .
[14] Helge Janicke,et al. SCADA security in the light of Cyber-Warfare , 2012, Comput. Secur..
[15] Huibin Sun,et al. Enhancing cutting tool sustainability based on remaining useful life prediction , 2020 .
[16] Fang Ruiming,et al. Identifying early defects of wind turbine based on SCADA data and dynamical network marker , 2020 .
[17] Yong Li,et al. A generalized remaining useful life prediction method for complex systems based on composite health indicator , 2021, Reliab. Eng. Syst. Saf..
[18] Dimitris Mourtzis,et al. A Framework for Automatic Generation of Augmented Reality Maintenance & Repair Instructions based on Convolutional Neural Networks , 2020 .
[19] Brian A. Weiss,et al. A review of diagnostic and prognostic capabilities and best practices for manufacturing , 2019, J. Intell. Manuf..
[20] Xiang Li,et al. Remaining useful life estimation in prognostics using deep convolution neural networks , 2018, Reliab. Eng. Syst. Saf..
[21] Rok Vrabič,et al. Multi-objective adjustment of remaining useful life predictions based on reinforcement learning , 2020 .
[22] John Ahmet Erkoyuncu,et al. A systematic review of augmented reality applications in maintenance , 2018 .
[23] R. J. Mitchell,et al. How Computers Process Data , 1995 .
[24] Xiaofeng Hu,et al. Remaining useful life prediction based on health index similarity , 2019, Reliab. Eng. Syst. Saf..
[25] Dimitris Mourtzis,et al. Simulation in the design and operation of manufacturing systems: state of the art and new trends , 2019, Int. J. Prod. Res..
[26] Oladimeji Farri,et al. Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification , 2019, Artif. Intell. Medicine.
[27] Hang Wang,et al. Remaining useful life prediction techniques for electric valves based on convolution auto encoder and long short term memory. , 2020, ISA transactions.
[28] Sikai Zhang,et al. SCADA-data-based wind turbine fault detection: A dynamic model sensor method , 2020 .
[29] Feng Yang,et al. Remaining useful life prediction of induction motors using nonlinear degradation of health index , 2021 .
[30] Åsa Fast-Berglund,et al. Testing and validating Extended Reality (xR) technologies in manufacturing , 2018 .
[31] Qun He,et al. Spatio-temporal fusion neural network for multi-class fault diagnosis of wind turbines based on SCADA data , 2020 .
[32] Dimitris Mourtzis,et al. A Cloud-based Approach for Maintenance of Machine Tools and Equipment Based on Shop-floor Monitoring☆ , 2016 .
[33] Qian Liu,et al. Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process , 2019, Reliab. Eng. Syst. Saf..
[34] Paola Fantini,et al. Placing the operator at the centre of Industry 4.0 design: Modelling and assessing human activities within cyber-physical systems , 2018, Comput. Ind. Eng..
[35] Dimitris Mourtzis,et al. Real-Time Remote Maintenance Support Based on Augmented Reality (AR) , 2020, Applied Sciences.
[36] Yaguo Lei,et al. Machinery health prognostics: A systematic review from data acquisition to RUL prediction , 2018 .
[37] Andrew Kusiak,et al. Data-driven smart manufacturing , 2018, Journal of Manufacturing Systems.
[38] Zhengmin Kong,et al. Convolution and Long Short-Term Memory Hybrid Deep Neural Networks for Remaining Useful Life Prognostics , 2019, Applied Sciences.
[39] Wolfgang Vorraber,et al. Assessing augmented reality in production: remote-assisted maintenance with HoloLens , 2020 .