Random forest regression for predicting an anomalous condition on a UR10 cobot end-effector from purposeful failure data
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[1] Nam H. Kim,et al. Options for Prognostics Methods: A review of data-driven and physics-based prognostics , 2013 .
[2] Walter Sextro,et al. Condition Monitoring of Bearing Damage in Electromechanical Drive Systems by Using Motor Current Signals of Electric Motors: A Benchmark Data Set for Data-Driven Classification , 2016, PHM Society European Conference.
[3] André Carvalho Bittencourt,et al. A Data-driven Method for Monitoring Systems that Operate Repetitively -Applications to Wear Monitoring in an Industrial Robot Joint1 , 2012 .
[4] Puqiang Zhang,et al. Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery , 2014 .
[5] P. D. McFadden,et al. Model for the vibration produced by a single point defect in a rolling element bearing , 1984 .
[6] MajidiCarmel,et al. Soft Robotics: A Perspective—Current Trends and Prospects for the Future , 2014 .
[7] Yi-Chi Wang,et al. Application of reinforcement learning for agent-based production scheduling , 2005, Eng. Appl. Artif. Intell..
[8] Narrendar RaviChandran,et al. Genetic algorithms and unsupervised machine learning for predicting robotic manipulation failures for force-sensitive tasks , 2018, 2018 4th International Conference on Control, Automation and Robotics (ICCAR).
[9] Yuyun Zeng,et al. Real-time condition monitoring and fault detection of components based on machine-learning reconstruction model , 2019, Renewable Energy.
[10] Lubna Mahmoud Abu Zohair. Prediction of Student’s performance by modelling small dataset size , 2019 .
[11] Klaus-Dieter Thoben,et al. Machine learning in manufacturing: advantages, challenges, and applications , 2016 .
[12] Jovica V. Milanovic,et al. Estimation of Cost of Downtime of Industrial Process Due to Voltage Sags , 2011 .
[13] Yusri Yusof,et al. Machine monitoring system: a decade in review , 2020, The International Journal of Advanced Manufacturing Technology.
[14] Roger V. Bostelman,et al. Literature Review of Mobile Robots for Manufacturing , 2015 .
[15] Bernd Kuhlenkötter,et al. Systematic review on machine learning (ML) methods for manufacturing processes – Identifying artificial intelligence (AI) methods for field application , 2020 .
[16] Derya Birant,et al. Machine learning and data mining in manufacturing , 2021, Expert Syst. Appl..
[17] J. R. Llata,et al. Working Together: A Review on Safe Human-Robot Collaboration in Industrial Environments , 2017, IEEE Access.
[18] Guangjun Liu,et al. Power efficiency estimation based health monitoring and fault detection of modular and reconfigurable robot , 2008, 2008 IEEE International Conference on Automation and Logistics.
[19] Robert B. Randall,et al. Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study , 2015 .
[20] Jay Lee,et al. A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems , 2015 .
[21] László Monostori,et al. Lead time prediction using machine learning algorithms: A case study by a semiconductor manufacturer , 2018 .
[22] Amanpreet Singh,et al. A review of supervised machine learning algorithms , 2016, 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom).
[23] Brian A. Weiss,et al. A review of diagnostic and prognostic capabilities and best practices for manufacturing , 2019, J. Intell. Manuf..
[24] Hai Qiu,et al. Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics , 2006 .
[25] J. Geoffrey Chase,et al. Human-Robot Collaboration: A Literature Review and Augmented Reality Approach in Design , 2008 .
[26] Daming Lin,et al. A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .
[27] John T. Renwick,et al. Vibration Analysis---A Proven Technique as a Predictive Maintenance Tool , 1985, IEEE Transactions on Industry Applications.
[28] Michael Lütjen,et al. Anomaly detection with convolutional neural networks for industrial surface inspection , 2019, Procedia CIRP.