ИСПОЛЬЗОВАНИЕ МЕТОДОВ МАШИННОГО ОБУЧЕНИЯ И ИСКУССТВЕННОГО ИНТЕЛЛЕКТА В ХИМИЧЕСКОЙ ТЕХНОЛОГИИ. ЧАСТЬ II

Виль Ришатович Нигматуллин, Николай Анатольевич Руднев

Аннотация


Статья является второй частью работы, в которой рассмотрено применение методов машинного обучения и искусственного интеллекта для различных задач химической технологии, таких как моделирование, автоматизация и оптимизация процессов, контроль качества и безопасности, поиск новых соединений и катализаторов. Для данных целей были использованы искусственные нейронные сети, метод решающих деревьев, бустинг, регрессия, а также их комбинации. Во второй части работы рассматриваются следующие вопросы: умное производство, технологии больших данных, поиск и обнаружение неисправностей, предсказание свойств катализаторов, а также кратко представлены наиболее популярные программные средства.

Ключевые слова


машинное обучение;нейронные сети;глубокие нейронные сети;бустинг;решающие деревья;оптимизация;инструментарии машинного обучения;математическая модель;Big Data;machine learning;neural networks;deep neural networks;boosting;decision trees;optimization;machine learning tools;mathematical model;Big Data;

Полный текст:

PDF

Литература


Hu T., Li P., Zhang C., Liu R. Design and Application of a Real-Time Industrial Ethernet Protocol Under Linux Using RTAI // International Journal of Computer Integrated Manufacturing. 2013. No. 26 (5). P. 429-439. DOI: 10.1080/0951192X.2012.731609.

Ye Y., Hu T., Zhang C., Luo W. Design and Development of a CNC Machining Process Knowledge Base Using Cloud Technology // The International Journal of Advanced Manufacturing Technology. 2016. Vol. 94. Issue 9-12. P. 3413-3425. DOI 10.1007/s00170-016-9338-1.

Tao F., Qi Q. New IT Driven Service-Oriented Smart Manufacturing: Framework and Characteristics // IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2017. No. 49 (1). P. 81-91. DOI: 10.1109/TSMC.2017.2723764.

Ang J., Goh C., Saldivar A., Li Y. Energy-Efficient Through-Life Smart Design, Manufacturing and Operation of Ships in an Industry 4.0 Environment // Energies. 2017. No. 10(5). P. 610. DOI: 10.3390/en10050610.

Huang Z., Hu T., Peng C., Hou M., Zhang C. Research and Development of Industrial Real-Time Ethernet Performance Testing System Used for CNC System // The International Journal of Advanced Manufacturing Technology. 2016. Vol. 83. Issue 5-8. P. 1199-1207. DOI: 10.1109/MIC.2017.18.

Lalanda P., Morand D., Chollet S. Autonomic Mediation Middleware for Smart Manufacturing // IEEE Internet Computing. 2017. No. 21 (1). P. 32-39. DOI: 10.1109/MIC.2017.18.

Wang L., Torngren M., Onori M. Current Status and Advancement of Cyber-Physical Systems in Manufacturing // Journal of Manufacturing Systems. 2015. No. 37 (2). P. 517-527. DOI: 10.1016/j.jmsy.2015.04.008.

Wang P., Gao R.X., Fan Z. Cloud Computing for Cloud Manufacturing: Benefitsand Limitations // Journal of Manufacturing Science and Engineering. 2015. No. 137 (4). URL: https://www.researchgate.net/publication/ 277594751_Cloud_Computing_for_Cloud_Manufacturing_Benefits_and_Limitations (accessed 27.08.2019). DOI: 10.1115/1.4030209.

Lu Y., Xu X., Xu J. Development of a Hybrid Manufacturing Cloud // Journal of Manufacturing Systems. 2014. No. 33 (4). P. 551-566. DOI: 10.1016/j.jmsy.2014.05.003.

Wu D., Rosen D.W., Schaefer D. Cloud-Based Design and Manufacturing: Status and Promise // Cloud-Based Design and Manufacturing. Cham, Switzerland. Springer International Publishing, 2014. P. 1-24. DOI: 10.1007/978-3-319-07398-9_1.

Choudhary A.K. Data Mining in Manufacturing: a Review Based on the Kind of Knowledge // Journal of Intelligent Manufacturing. 2009. No. 20. P. 501-521. DOI: 10.1007/s10845-008-0145-x.

Lade P., Ghosh R., Srinivasan S. Manufacturing Analytics and Industrial Internet of Things // IEEE Intelligent Systems. 2017. No. 32 (3). P. 74-79. DOI: 10.1109/MIS.2017.49.

Teti R., Jemielniak K., O’Donnell G.E., Dornfeld D. Advanced Monitoring of Machining Operations // CIRP Annals - Manufacturing Technology. 2010. No. 59 (2). P. 717-739. DOI: 10.1016/j.cirp.2010.05.010.

Wu D., Jennings C., Terpenny J., Gao R.X., Kumara S. A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests // Journal of Manufacturing Science and Engineering. 2017. Vol. 139. Issue 7. URL: https://asmedigitalcollection.asme.org/manufacturingscience/article/139/7/071018/454654/A-Comparative-Study-on-Machine-Learning-Algorithms (accessed 27.08.2019). DOI: 10.1115/1.4036350.

Xie X. A Review of Recent Advances in Surface Defect Detection Using Texture Analysis Techniques // Electronic Letters on Computer Vision and Image Analysis. 2008. No. 7. P. 11-22. DOI:10.5565/rev/elcvia.268.

Neogi N., Mohanta D.K., Dutta P.K. Review of Vision-Based Steel Surface Inspection Systems // Journal on Image and Video Processing. 2014. No. 50. URL: https://jivp-eurasipjournals.springeropen.com/articles/10.1186/ 1687-5281-2014-50 (accessed 27.08.2019). DOI:10.1186/1687-5281-2014-50.

Park J.K., Kwon B.K., Park J.H., Kang D.J. Machine Learning-Based Imaging Systemfor Surface Defect Inspection // International Journal of Precision Engineering and Manufacturing-Green Technology. 2016. Vol. 3. Issue 3. P. 303-310. DOI: 10.1007/s40684-016-0039-x.

Janssens O., Slavkovikj V., Vervisch B., Stockman K., Loccufier M., Verstockt S. Convolution Neural Network Based Fault Detection for Rotating Machinery // Journal of Sound and Vibration. 2016. No. 377. P. 331-345. DOI: 10.1016/j.jsv.2016.05.027.

Lu C., Wang Z., Zhou B. Intelligent Fault Diagnosis of Rolling Bearing Using Hierarchical Convolution Network Based Health State Classification // Advanced Engineering Informatics. 2017. Vol. 32. P. 139-151. DOI: 10.1016/j.aei.2017.02.005.

Guo X., Chen L., Shen C. Hierarchical Adaptive Deep Convolution Neural Network and Its Application to Bearing Fault Diagnosis // Measurement. 2016. No. 93. P. 490-502. DOI: 10.1016/j.measurement.2016.07.054.

Verstraete D., Droguett E., Meruance V., Modarres M., Ferrada A. Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings // Shock and Vibration. 2017. Vol. 2017. P. 1-17. DOI:10.1155/2017/5067651.

Chen Z.Q., Li C., Sanchez R.V. Gearbox Fault Identification and Classification with Convolution Neural Networks // Shock and Vibration. 2015. Vol. 2015. P. 1-10. DOI: 10.1155/2015/390134.

Wang P., Ananya Yan R., Gao R.X. Virtualization and Deep Recognition for System Fault Classification // Journal of Manufacturing Systems. 2017. No. 44. P. 310-316. DOI: 10.1016/j.jmsy.2017.04.012.

Dong H., Yang L., Li H. Small Fault Diagnosis of Front-End Speed Controlled Wind Generator Based on Deep Learning // WSEAS Transactions on Circuits and Systems. 2016. Vol. 15. P. 64-72.

Wang J., Zhuang J., Duan L., Cheng W. A Multi-Scale Convolution Neural Network for Featureless Fault Diagnosis // Proceedings of 2016 International Symposium on Flexible Automation (ISFA). Cleveland, State of Ohio, USA. 2016. P. 65-70. DOI: 10.1109/ISFA.2016.7790137.

Yu H., Khan F., Garaniya V. Non-Linear Gaussian Belief Network Based Fault Diagnosis for Industrial Processes // Journal of Process Control. 2015. No. 35. P. 178-200. DOI: 10.1016/j.jprocont.2015.09.004.

Shao H., Jiang H., Zhang X., Niu M. Rolling Bearing Fault Diagnosis Using an Optimization Deep Belief Network // Measurement Science and Technology. 2015. Vol. 26. No. 11. DOI: 10.1088/0957-0233/26/11/115002.

Gan M., Wang C., Zhu C. Construction of Hierarchical Diagnosis Network Based on Deep Learning and its Application in the Fault Pattern Recognition of Rolling Element Bearings // Mechanical Systems and Signal Processing. 2016. Vol. 72-73. P. 92-104. DOI: 10.1016/j.ymssp.2015.11.014.

Zhao R., Yan R., Wang J., Mao K. Learning to Monitor Machine Health with Convolution Bi-Directional LSTM Networks // Sensors. 2017. Vol. 17. No. 2. P. 273-290. DOI: 10.3390/s17020273.

Malhotra P., Vig L., Shroff G., Agarwal P. Long Short Term Memory Networks for Anomaly Detection in Time Series // Computational Intelligence, and Machine Learning - 2015 Proceeding of 23rd European Symposium on Artificial Neural Networks. Bruges, Belgium. 2015. P. 89-94.

Lee J., Kao H.A., Yang S. Service Innovation and Smart Analytics for Industry 4:0 and Big Data Environment // Procedia CIRP. 2014. No. 16. P. 3-8. DOI: 10.1016/j.procir.2014.02.001.

Chen C., Zhang C.Y. Data-Intensive Applications, Challenges, Techniques and Technologies: a Survey on Big Data // Information Sciences. 2014. Vol. 275. P. 314-347. DOI: 10.1016/j.ins.2014.01.015.

Floudas C.A., Niziolek A.M., Onel O., Matthews L.R. Multi-Scale Systems Engineering or Energy and the Environment: Challenges and Opportunities // AIChE Journal. 2016. Vol. 62. P. 602-623. DOI:10.1002/aic.15151.

Helu M., Libes D., Lubell J., Lyons K., Morris K. Enabling Smart Manufacturing Technologies for Decision-Making Support // ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference Proceedings of the 36th ASME Computers and Information in Engineering Conference. Charlotte, North Carolina, USA. 2016. URL: https://tsapps.nist.gov/publication/get_ pdf.cfm?pub_id=920656 (accessed 27.08.2019). DOI: 10.1115/DETC2016-59721.

Beck D.A.C., Carothers J.M., Subramanian V.R., Pfaendtner J. Data Science: Accelerating Innovation and Discovery in Chemical Engineering // AIChE Journal. 2016. Vol. 62. Issue 5. P. 1402-1416. DOI: 10.1002/aic.15192.

Chiang L.H., Russell E.L., Braatz R.D. Fault Detection and Diagnosis in Industrial Systems. London: Springer-Verlag London Limited, 2001. 279 p. DOI: 10.1007/978-1-4471-0347-9.

Venkatasubramanian V., Rengaswamy R., Yin K., Kavuri S.N. A Review of Process Fault Detection and Diagnosis // Computers & Chemical Engineering. 2003. Vol. 27. Issue 3. P. 293-311. DOI: 10.1016/S0098-1354(02)00160-6.

Melis Onel, Chris A. Kieslich, Yannis A. Guzman, Christodoulos A. Floudas, Efstratios N. Pistikopoulos. Big Data Approach to Batch Process Monitoring: Simultaneous Fault Detection and Diagnosis Using Nonlinear Support Vector Machine-based Feature Selection // Computers and Chemical Engineering. 2018. Vol. 115. P. 46-63. DOI: 10.1016/j.compchemeng.2018.03.025.

Zhu J., Ge Z., Song Z. Distributed Parallel PCA for Modeling and Monitoring of Large-Scale Plant-Wide Processes with Big Data // IEEE Transactions on Industrial Informatics. 2017. Vol. 13. Issue 4. P. 1877-1885. DOI: 10.1109/TII.2017.2658732.

Ge Z., Song Z., Ding S.X., Huang B. Data Mining and Analytics in the Process Industry: The Role of Machine Learning // IEEE Access. 2017. Vol. 5. P. 20590-20616. DOI: 10.1109/ACCESS.2017.2756872.

White T. Hadoop: The Definitive Guide. 2012. 688 p.

Singh V., Gupta R.K., Sevakula R.K., Verma N.K. Comparative Analysis of Gaussian Mixture Model, Logistic Regression and Random Forest for Big Data Classification Using Map Reduce // Proceedings of 2016 11th International Conference on Industrial and Information Systems (ICIIS). Roorkee, India. 2016. P. 333-338. DOI: 10.1109/ICIINFS.2016.8262961.

Zhu J. Monitoring Big Process Data of Industrial Plants with Multiple Operating Modes Based on Hadoop // Journal of the Taiwan Institute of Chemical Engineers. 2018. Vol. 91. P. 10-21. DOI: 10.1016/j.jtice.2018.05.020.

Kroll B. System Modelling Based on Machine Learning for Anomaly Detection and Predictive Maintenance in Industrial Plants // Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA). Barcelona, Spain. 2014. P. 1-7. DOI: 10.1109/ETFA.2014.7005202.

Susto G. Machine Learning for Predictive Maintenance: A Multiple Classifier Approach // IEEE Transactions on Industrial Informatics. 2015. Vol. 11. Issue 3. P. 812-820. DOI: 10.1109/TII.2014.2349359.

Wuest T., Irgens C. An Approach to Monitoring Quality in Manufacturing Using Supervised Machine Learning on Produce State Data // Journal of Intelligent Manufacturing. 2014. Vol. 25. Issue 5. P. 1167-1180. DOI: 10.1007/s10845-013-0761-y.

Caoimhe M. Carbery, Roger Woods, Adele H. Marshall. A Bayesian Network Based Learning System for Modeling Faults in Large-Scale Manufacturing // 2018 IEEE International Conference on Industrial Technology. Lyon, France. 2018. P. 1357-1362. DOI: 10.1109/ICIT.2018.8352377.

Rato T., Reis M., Schmitt E., Hubert M., De Ketelaere B. A Systematic Comparison of PCA-Based Statistical Process Monitoring Methods for High-Dimensional, Time-Dependent Processes // AIChE Journal. 2016. Vol. 62. Issue 5. P. 1478-1493. DOI: 10.1002/aic.15062.

Fan J., Wang Y. Fault Detection and Diagnosis of Non-Linear Non-Gaussian Dynamic Processes Using Kernel Dynamic Independent Component Analysis // Information Sciences. 2014. Vol. 259. P. 369-379. DOI: 10.1016/j.ins.2013.06.021.

Ayubi Rad M.A., Yazdanpanah M.J. Designing Supervised Local Neural Network Classifiers Based on EM Clustering for Fault Diagnosis of Tennessee Eastman Process // Chemometrics and Intelligent Laboratory Systems. 2015. Vol. 146. P. 149-157. DOI: 10.1016/j.chemolab.2015.05.013.

Eslamloueyan R. Designing a Hierarchical Neural Network Based on Fuzzy Clustering for Fault Diagnosis of the Tennessee-Eastman Process // Applied Soft Computing. 2011. Vol. 11. Issue 1. P. 1407-1415. DOI:10.1016/j.asoc.2010.04.012.

Wu H., Zhao J. Deep Convolutional Neural Network Model Based Chemical Process Fault Diagnosis // Computers and Chemical Engineering. 2018. Vol. 115. P. 185-197. DOI: 10.1016/j.compchemeng.2018.04.009.

Chen F., Li H., Xu Z., Hou S., Yang D. User-Friendly Optimization Approach of Fed-Batch Fermentation Conditions for the Production Using Artificial Neural Networks and Support Vector Machine // Electronic Journal of Biotechnology. 2015. Vol. 18. No. 4. P. 273-280. DOI: 10.1016/j.ejbt.2015.05.001.

Kite S., Hattori T., Murakami Y. Estimation of Catalytic Performance by Neural Network - Product Distribution in Oxidative Dehydrogenation of Ethylbenzene // Applied Catalysis A General. 1994. No. 114. P. 173-178.

Mohammed M.L., Patel D., Mbeleck R., Niyogi D., Sherrington D.C., Saha B. Optimization of Alkene Epoxidation Catalysed by Polymer Supported Mo(VI) Complexes and Application of Artificial Neural Network for the Prediction of Catalytic Performances // Applied Catalysis A General. 2013. No. 466. P. 142-152. DOI: 10.1016/j.apcata.2013.06.055.

Raccuglia P., Elbert K.C., Adler P.D.F. Machine-Learning-Assisted Materials Discovery Using Failed Experiments // Nature. 2016. Vol. 533. P. 73-76. DOI: 10.1038/nature17439.

Baumes L., Farrusseng D., Lengliz M., Mirodatos C. Using Artificial Neural Networks to Boost High-Throughput Discovery in Heterogeneous Catalysis // QSAR & Combinatorial Science. 2004. Vol. 23. Issue 9. P. 767-778. DOI: 10.1002/qsar.200430900.

Machine Learning Made in a Minute. Accord.NET Framework. URL: http://accord-framework.net (дата обращения: 10.12.2018).

H2O.ai. URL: https://www.h2o.ai (дата обращения: 10.12.2018).

Github Apache PredictionIO. URL: https://github.com/apache/ predictionio (дата обращения: 10.12.2018).

Deep Learning for Java. Eclipse Deeplearning4j. URL: https://deeplearning4j.org (дата обращения: 10.12.2018).

Torch. URL: https://torch.ai (дата обращения: 10.12.2018).

IBM Watson. URL: https://www.ibm.com/watson (дата обращения: 10.12.2018).

Github IBM Watson Documentation. URL: https://github.com/watson-developer-cloud (дата обращения: 10.12.2018).

TensorFlow. URL: https://www.tensorflow.org (дата обращения: 10.12.2018).

Deep Learning. Theano. URL: http://www.deeplearning.net/ software/theano (дата обращения: 10.12.2018).

Keras Documentation. URL: https://keras.io (дата обращения: 10.12.2018).

CatBoost. URL: https://tech.yandex.ru/catboost (дата обращения: 10.12.2018).




DOI: http://dx.doi.org/10.17122/ogbus-2019-5-202-238

Ссылки

  • На текущий момент ссылки отсутствуют.