Acceso abierto

Model run monitoring and parameter modification methods

  
03 sept 2024

Cite
Descargar portada

Li, J. Q., Yu, F. R., Deng, G., Luo, C., Ming, Z., & Yan, Q. (2017). Industrial internet: A survey on the enabling technologies, applications, and challenges. IEEE Communications Surveys & Tutorials, 19(3), 1504-1526. Search in Google Scholar

Jiang, Y., Yin, S., Dong, J., & Kaynak, O. (2020). A review on soft sensors for monitoring, control, and optimization of industrial processes. IEEE Sensors Journal, 21(11), 12868-12881. Search in Google Scholar

Ge, Z., Song, Z., Ding, S. X., & Huang, B. (2017). Data mining and analytics in the process industry: The role of machine learning. Ieee Access, 5, 20590-20616. Search in Google Scholar

Grasso, M., & Colosimo, B. M. (2017). Process defects and in situ monitoring methods in metal powder bed fusion: a review. Measurement Science and Technology, 28(4), 044005. Search in Google Scholar

Cawley, P. (2018). Structural health monitoring: Closing the gap between research and industrial deployment. Structural health monitoring, 17(5), 1225-1244. Search in Google Scholar

Bibi, F., Guillaume, C., Gontard, N., & Sorli, B. (2017). A review: RFID technology having sensing aptitudes for food industry and their contribution to tracking and monitoring of food products. Trends in Food Science & Technology, 62, 91-103. Search in Google Scholar

Yazdi, M., Korhan, O., & Daneshvar, S. (2018). Application of fuzzy fault tree analysis based on modified fuzzy AHP and fuzzy TOPSIS for fire and explosion in the process industry. International journal of occupational safety and ergonomics. Search in Google Scholar

Khan, W. Z., Rehman, M. H., Zangoti, H. M., Afzal, M. K., Armi, N., & Salah, K. (2020). Industrial internet of things: Recent advances, enabling technologies and open challenges. Computers & electrical engineering, 81, 106522. Search in Google Scholar

Delli, U., & Chang, S. (2018). Automated process monitoring in 3D printing using supervised machine learning. Procedia Manufacturing, 26, 865-870. Search in Google Scholar

Lu, Y., Xu, X., & Wang, L. (2020). Smart manufacturing process and system automation–a critical review of the standards and envisioned scenarios. Journal of Manufacturing Systems, 56, 312-325. Search in Google Scholar

Bottani, E., & Vignali, G. (2019). Augmented reality technology in the manufacturing industry: A review of the last decade. Iise Transactions, 51(3), 284-310. Search in Google Scholar

Zhou, Y., & Xue, W. (2018). Review of tool condition monitoring methods in milling processes. The International Journal of Advanced Manufacturing Technology, 96, 2509-2523. Search in Google Scholar

Dabbaghjamanesh, M., Kavousi-Fard, A., & Mehraeen, S. (2018). Effective scheduling of reconfigurable microgrids with dynamic thermal line rating. IEEE Transactions on Industrial Electronics, 66(2), 1552-1564. Search in Google Scholar

Zhang, W., Li, C., Peng, G., Chen, Y., & Zhang, Z. (2018). A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load. Mechanical systems and signal processing, 100, 439-453. Search in Google Scholar

Diez-Olivan, A., Del Ser, J., Galar, D., & Sierra, B. (2019). Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0. Information Fusion, 50, 92-111. Search in Google Scholar

Reis, M. S., & Gins, G. (2017). Industrial process monitoring in the big data/industry 4.0 era: From detection, to diagnosis, to prognosis. Processes, 5(3), 35. Search in Google Scholar

Wu, D., Liu, S., Zhang, L., Terpenny, J., Gao, R. X., Kurfess, T., & Guzzo, J. A. (2017). A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing. Journal of Manufacturing Systems, 43, 25-34. Search in Google Scholar

Peng, C., & RuiWei, L. (2021). Process monitoring of batch process based on overcomplete broad learning network. Engineering Applications of Artificial Intelligence, 99, 104139. Search in Google Scholar

Syafrudin, M., Alfian, G., Fitriyani, N. L., & Rhee, J. (2018). Performance analysis of IoT-based sensor, big data processing, and machine learning model for real-time monitoring system in automotive manufacturing. Sensors, 18(9), 2946. Search in Google Scholar

Dong, Y., & Qin, S. J. (2018). A novel dynamic PCA algorithm for dynamic data modeling and process monitoring. Journal of Process Control, 67, 1-11. Search in Google Scholar

Ge, Z. (2017). Review on data-driven modeling and monitoring for plant-wide industrial processes. Chemometrics and Intelligent Laboratory Systems, 171, 16-25. Search in Google Scholar

Rossiter, J. A. (2017). Model-based predictive control: a practical approach. CRC press. Search in Google Scholar

Jiawen Gong,Bin Zou,Chen Xu,Jie Xu & Xinge You.(2024).Hybrid learning based on Fisher linear discriminant.Information Sciences120465-. Search in Google Scholar

Pantea Koochemeshkian,Eddy Ihou Koffi & Nizar Bouguila.(2024).Hidden Variable Models in Text Classification and Sentiment Analysis.Electronics(10), Search in Google Scholar

Sujan Ghimire,Ravinesh C. Deo,S. Ali Pourmousavi,David Casillas Pérez & Sancho Salcedo Sanz.(2024).Point-based and probabilistic electricity demand prediction with a Neural Facebook Prophet and Kernel Density Estimation model.Engineering Applications of Artificial Intelligence108702-. Search in Google Scholar

Xu Pengfei,Cao Qingbo,Shen Yalin,Chen Meiya,Ding Yanxu & Cheng Hongxia.(2022).Predicting the Motion of a USV Using Support Vector Regression with Mixed Kernel Function.Journal of Marine Science and Engineering(12),1899-1899. Search in Google Scholar

Han Xu,Lu Zhang,Xuanbo Wang,Baocheng Han,Zhengyuan Luo & Bofeng Bai.(2024).Improved genetic algorithm for pipe diameter optimization of an existing large-scale district heating network. Energy 131970-. Search in Google Scholar