Big data and machine learning: A roadmap towards smart plants

doi:10.1007/s42524-022-0218-0

Front. Eng

2022, Vol. 9

Issue (4) : 623-639 https://doi.org/10.1007/s42524-022-0218-0

RESEARCH ARTICLE

Big data and machine learning: A roadmap towards smart plants

Bogdan DORNEANU¹, Sushen ZHANG², Hang RUAN³, Mohamed HESHMAT⁴, Ruijuan CHEN⁵, Vassilios S. VASSILIADIS⁶, Harvey ARELLANO-GARCIA¹(

)

¹. LS-Prozess und Anlagentechnik, Brandenburgische Technische Universität Cottbus-Senftenberg, 03044 Cottbus, Germany
². Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, CB2 1TN, United Kingdom
³. School of Mathematics, University of Edinburgh, Edinburgh, EH9 3FD, United Kingdom
⁴. Department of Architecture and Building Environment, University of the West of England, Bristol, BS16 1QY, United Kingdom
⁵. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
⁶. Cambridge Simulation Solutions LTD., Larnaca 7550, Cyprus

Download: PDF(1347 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Industry 4.0 aims to transform chemical and biochemical processes into intelligent systems via the integration of digital components with the actual physical units involved. This process can be thought of as addition of a central nervous system with a sensing and control monitoring of components and regulating the performance of the individual physical assets (processes, units, etc.) involved. Established technologies central to the digital integrating components are smart sensing, mobile communication, Internet of Things, modelling and simulation, advanced data processing, storage and analysis, advanced process control, artificial intelligence and machine learning, cloud computing, and virtual and augmented reality. An essential element to this transformation is the exploitation of large amounts of historical process data and large volumes of data generated in real-time by smart sensors widely used in industry. Exploitation of the information contained in these data requires the use of advanced machine learning and artificial intelligence technologies integrated with more traditional modelling techniques. The purpose of this paper is twofold: a) to present the state-of-the-art of the aforementioned technologies, and b) to present a strategic plan for their integration toward the goal of an autonomous smart plant capable of self-adaption and self-regulation for short- and long-term production management.

Keywords big data machine learning artificial intelligence smart sensor cyber–physical system Industry 4.0 intelligent system digitalization

Corresponding Author(s): Harvey ARELLANO-GARCIA

Just Accepted Date: 13 July 2022 Online First Date: 01 September 2022 Issue Date: 08 December 2022

Cite this article:

Bogdan DORNEANU,Sushen ZHANG,Hang RUAN, et al. Big data and machine learning: A roadmap towards smart plants[J]. Front. Eng, 2022, 9(4): 623-639.

URL:

https://academic.hep.com.cn/fem/EN/10.1007/s42524-022-0218-0
https://academic.hep.com.cn/fem/EN/Y2022/V9/I4/623

Fig.1 DNN architecture with two hidden layers.

Tab.1 Comparison of fault detection and prediction algorithms

Fig.2 Intelligent system architecture (URLLC = Ultra Reliable Low Latency Communications; mMTC = Massive Machine-Type Communications).

Fig.3 Decision-making module.

1	A Abid, M T Khan, J Iqbal, ( 2021). A review on fault detection and diagnosis techniques: Basics and beyond. Artificial Intelligence Review, 54( 5): 3639– 3664 https://doi.org/10.1007/s10462-020-09934-2
2	S Adloor, S V Vassiliadis, ( 2020). An optimal control approach to scheduling maintenance and production in parallel lines of reactors using decaying catalysts. Computers & Chemical Engineering, 142: 107025 https://doi.org/10.1016/j.compchemeng.2020.107025
3	S Adloor, S V Vassiliadis, ( 2021). An optimal control approach to considering uncertainties in kinetic parameters in the maintenance scheduling and production of a process using decaying catalysts. Computers & Chemical Engineering, 149: 107277 https://doi.org/10.1016/j.compchemeng.2021.107277
4	R Ahmed, V Sreeram, Y Mishra, M D Arif, ( 2020). A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization. Renewable & Sustainable Energy Reviews, 124: 109792 https://doi.org/10.1016/j.rser.2020.109792
5	P Aivaliotis, K Georgoulias, G Chryssolouris, ( 2019). The use of Digital Twin for predictive maintenance in manufacturing. International Journal of Computer Integrated Manufacturing, 32( 11): 1067– 1080 https://doi.org/10.1080/0951192X.2019.1686173
6	S Akmal, R Batres, ( 2013). A methodology for developing manufacturing process ontologies. Journal of Japan Industrial Management Association, 64( 2E): 303– 316 https://doi.org/10.11221/jima.64.303
7	R Al-Ali, L Bulej, J Kofron, T Bures, ( 2022). A guide to design uncertainty-aware self-adaptive components in cyber–physical systems. Future Generation Computer Systems, 128: 466– 489 https://doi.org/10.1016/j.future.2021.10.027
8	R Al-Amri, R K Murugesan, M Man, A F Abdulateef, M A Al-Sharafi, A A Alkahtani, ( 2021). A review of machine learning and deep learning techniques for anomaly detection in IoT data. Applied Sciences, 11( 12): 5320 https://doi.org/10.3390/app11125320
9	R Al Ismaili, M W Lee, D I Wilson, S V Vassiliadis, ( 2018). Heat exchanger network cleaning scheduling: From optimal control to mixed-integer decision making. Computers & Chemical Engineering, 111: 1– 15 https://doi.org/10.1016/j.compchemeng.2017.12.004
10	R Al Ismaili, M W Lee, D I Wilson, S V Vassiliadis, ( 2019). Optimisation of heat exchanger network cleaning schedules: Incorporating uncertainty in fouling and cleaning model parameters. Computers & Chemical Engineering, 121: 409– 421 https://doi.org/10.1016/j.compchemeng.2018.11.009
11	M A Alsheikh, S Lin, D Niyato, H P Tan, ( 2014). Machine learning in wireless sensor networks: Algorithms, strategies and applications. IEEE Communications Surveys and Tutorials, 16( 4): 1996– 2018 https://doi.org/10.1109/COMST.2014.2320099
12	M T Amin, S Imtiaz, F Khan, ( 2018). Process system fault detection and diagnosis using a hybrid technique. Chemical Engineering Science, 189: 191– 211 https://doi.org/10.1016/j.ces.2018.05.045
13	R Arunthavanathan, F Khan, S Ahmed, S Imtiaz, ( 2021). An analysis of process fault diagnosis methods from safety perspectives. Computers & Chemical Engineering, 145: 107197 https://doi.org/10.1016/j.compchemeng.2020.107197
14	B C G Assis, J C Lemos, F S Liporace, S G Oliveira, E M Quieroz, F L P Pessoa, A L H Costa, ( 2015). Dynamic optimization of the flow rate distribution in heat exchanger networks for fouling mitigation. Industrial & Engineering Chemistry Research, 54( 25): 6497– 6507 https://doi.org/10.1021/acs.iecr.5b00453
15	A Ayadi O Ghorbel M S Bensaleh A Obeid M Abid ( 2017). Data classification in water pipeline based on wireless sensors networks. In: Proceedings of the 14th International Conference on Computer Systems and Applications (AICCSA). Hammamet: IEEE/ACS, 1212– 1217
16	I Baklouti, M Mansouri, A Ben Hamida, H Nounou, M Nounou, ( 2018). Monitoring of wastewater treatment plants using improved univariate statistical technique. Process Safety and Environmental Protection, 116: 287– 300 https://doi.org/10.1016/j.psep.2018.02.006
17	T Bandyszak, M Daun, B Tenbergen, P Kuhs, S Wolf, T Weyer, ( 2020). Orthogonal uncertainty modeling in the engineering of cyber–physical systems. IEEE Transactions on Automation Science and Engineering, 17( 3): 1250– 1265 https://doi.org/10.1109/TASE.2020.2980726
18	R Batres, ( 2017). Ontologies in process systems engineering. Chemie Ingenieur Technik, 89( 11): 1421– 1431 https://doi.org/10.1002/cite.201700037
19	R Batres, M West, D Leal, D Price, K Masaki, Y Shimada, T Fuchino, Y Naka, ( 2007). An upper ontology based on ISO 15926. Computers & Chemical Engineering, 31( 5–6): 519– 534 https://doi.org/10.1016/j.compchemeng.2006.07.004
20	A Baykasoğlu, F S Madenoglu, ( 2021). Greedy randomized adaptive search procedure for simultaneous scheduling of production and preventive maintenance activities in dynamic flexible job shops. Soft Computing, 25( 23): 14893– 14932 https://doi.org/10.1007/s00500-021-06053-0
21	B Behdani Z Lukszo A Adhitya R Srinivasan ( 2009). Agent-based modelling to support operations management in a multi-plant enterprise. In: Proceedings of the International Conference on Networking, Sensing and Control. Okayama: IEEE, 323– 328
22	J C Bendul, H Blunck, ( 2019). The design space of production planning and control for Industry 4.0. Computers in Industry, 105: 260– 272 https://doi.org/10.1016/j.compind.2018.10.010
23	I D L Bogle, ( 2017). A perspective on smart process manufacturing research challenges for Process Systems Engineers. Engineering, 3( 2): 161– 165 https://doi.org/10.1016/J.ENG.2017.02.003
24	T P Carvalho, F A A M N Soares, R Vita, R P Francisco, J P Basto, S G S Alcala, ( 2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 137: 106024 https://doi.org/10.1016/j.cie.2019.106024
25	I Castelo-Branco, F Cruz-Jesus, T Oliveira, ( 2019). Assessing Industry 4.0 readiness in manufacturing: Evidence for the European Union. Computers in Industry, 107: 22– 32 https://doi.org/10.1016/j.compind.2019.01.007
26	L Chiang, B Lu, I Castillo, ( 2017). Big data analytics in chemical engineering. Annual Review of Chemical and Biomolecular Engineering, 8( 1): 63– 85 https://doi.org/10.1146/annurev-chembioeng-060816-101555 pmid: 28301733
27	M C Chiu, C D Tsai, T L Li, ( 2020). An integrative machine learning method to improve fault detection and productivity performance in cyber–physical systems. Journal of Computing and Information Science in Engineering, 20( 2): 021009 https://doi.org/10.1115/1.4045663
28	B Dafflon, N Moalla, Y Ouzrout, ( 2021). The challenges, approaches, and used techniques of CPS for manufacturing in Industry 4.0: A literature review. International Journal of Advanced Manufacturing Technology, 113( 7–8): 2395– 2412 https://doi.org/10.1007/s00170-020-06572-4
29	S Dey, H E Perez, S J Moura, ( 2019). Model-based battery thermal fault diagnostics: Algorithms, analysis and experiments. IEEE Transactions on Control Systems Technology, 27( 2): 576– 587 https://doi.org/10.1109/TCST.2017.2776218
30	A Dorri, S S Kanhere, R Jurdak, ( 2018). Multi-agent systems: A survey. IEEE Access, 6: 28573– 28593 https://doi.org/10.1109/ACCESS.2018.2831228
31	F J Ekaputra, M Sabou, E Serral, E Kiesling, S Biffl, ( 2017). Ontology-based data integration in multi-disciplinary engineering environments: A review. Open Journal of Information Systems, 4( 1): 1– 26 https://doi.org/10.24167/sisforma.v4i1.1040
32	R Elhdad, N Chilamkurti, T Torabi, ( 2013). An ontology-based framework for process monitoring and maintenance in petroleum plant. Journal of Loss Prevention in the Process Industries, 26( 1): 104– 116 https://doi.org/10.1016/j.jlp.2012.10.001
33	G Fadlallah, D Rebaine, H Mcheick, ( 2021). A greedy scheduling approach for peripheral mobile intelligent systems. IoT, 2( 2): 249– 274 https://doi.org/10.3390/iot2020014
34	A M Farid, ( 2015). Multi-agent system design principles for resilient coordination & control of future power systems. Intelligent Industrial Systems, 1( 3): 255– 269 https://doi.org/10.1007/s40903-015-0013-x
35	H Fatorachian, H Kazemi, ( 2018). A critical investigation of Industry 4.0 in manufacturing: Theoretical operationalisation framework. Production Planning and Control, 29( 8): 633– 644 https://doi.org/10.1080/09537287.2018.1424960
36	X Fei, N Shah, N Verba, K M Chao, V Sanchez-Anguix, J Lewandowski, A James, Z Usman, ( 2019). CPS data streams analytics based on machine learning for Cloud and Fog computing: A survey. Future Generation Computer Systems, 90: 435– 450 https://doi.org/10.1016/j.future.2018.06.042
37	L Fumagalli, M Macchi, A Giacomin, ( 2017). Orchestration of preventive maintenance interventions. IFAC-PapersOnLine, 50( 1): 13976– 13981 https://doi.org/10.1016/j.ifacol.2017.08.2417
38	A Gilchrist, ( 2016). Industry 4.0: The Industrial Internet of Things. Berkeley, CA: Apress
39	A Grenyer, J A Erkoyuncu, Y Zhao, R Roy, ( 2021). A systematic review of multivariate uncertainty quantification for engineering systems. CIRP Journal of Manufacturing Science and Technology, 33: 188– 208 https://doi.org/10.1016/j.cirpj.2021.03.004
40	D Gürdür, J El-khoury, M Törngren, ( 2019). Digitalising Swedish industry: What is next? Data analytics readiness assessment of Swedish industry, according to survey results. Computers in Industry, 105: 153– 163 https://doi.org/10.1016/j.compind.2018.12.011
41	F Harirchi, N Ozay, ( 2018). Guaranteed model-based fault detection in cyber–physical systems: A model invalidation approach. Automatica, 93: 476– 488 https://doi.org/10.1016/j.automatica.2018.03.040
42	P Hehenberger, B Vogel-Heuser, D Bradley, B Eynard, T Tomiyama, S Achiche, ( 2016). Design, modelling, simulation and integration of cyber physical systems: Methods and applications. Computers in Industry, 82: 273– 289 https://doi.org/10.1016/j.compind.2016.05.006
43	J Hong, K Moon, K Lee, K Lee, M L Pinedo, ( 2022). An iterated greedy matheuristic for scheduling steelmaking-continuous casting process. International Journal of Production Research, 60( 2): 623– 643 https://doi.org/10.1080/00207543.2021.1975839
44	S Hosseini, S Kalam, K Barker, J E Ramirez-Marquez, ( 2020). Scheduling multi-component maintenance with a greedy heuristic local search algorithm. Soft Computing, 24( 1): 351– 366 https://doi.org/10.1007/s00500-019-03914-7
45	D Ivanov, S Sethi, A Dolgui, B Sokolov, ( 2018). A survey on control theory applications to operational systems, supply chain management, and Industry 4.0. Annual Reviews in Control, 46: 134– 147 https://doi.org/10.1016/j.arcontrol.2018.10.014
46	Z Jiao, P Hu, H Xu, Q Wang, ( 2020). Machine learning and deep learning in chemical health and safety: A systematic review of techniques and applications. ACS Chemical Health and Safety, 27( 6): 316– 334 https://doi.org/10.1021/acs.chas.0c00075
47	C Kan, H Yang, S Kumara, ( 2018). Parallel computing and network analytics for fast Industrial Internet-of-Things (IIoT) machine information processing and condition monitoring. Journal of Manufacturing Systems, 46: 282– 293 https://doi.org/10.1016/j.jmsy.2018.01.010
48	H Kaur, G Singh, J Minhas, ( 2013). Review of machine learning and anomaly detection techniques. International Journal of Computer Applications Technology and Research, 2( 2): 185– 187 https://doi.org/10.7753/IJCATR0202.1020
49	A Khalaf K Djouani Y Hamam Y Alayli ( 2010). Evidence-based mathematical maintenance model for medical equipment. In: Proceedings of the International Conference on Electronic Devices, Systems and Applications. Kuala Lumpur: IEEE, 222– 226
50	J S Kong, D M Frangopol, ( 2003). Life-cycle reliability-based maintenance cost optimization of deteriorating structures with emphasis on bridges. Journal of Structural Engineering, 129( 6): 818– 828 https://doi.org/10.1061/(ASCE)0733-9445(2003)129:6(818
51	S Krämer S Engell ( 2018). Resource Efficiency of Processing Plants: Monitoring and Improvement. Hoboken, NJ: John Wiley & Sons
52	K Kravari, N Bassiliades, ( 2015). A survey of agent platforms. Journal of Artificial Societies and Social Simulation, 18( 1): 11 https://doi.org/10.18564/jasss.2661
53	A Kumar, M Gupta, ( 2018). A review of activities of fifth generation mobile communication system. Alexandria Engineering Journal, 57( 2): 1125– 1135 https://doi.org/10.1016/j.aej.2017.01.043
54	J Kwak, T Lee, C O Kim, ( 2015). An incremental clustering-based fault detection algorithm for class-imbalanced process data. IEEE Transactions on Semiconductor Manufacturing, 28( 3): 318– 328 https://doi.org/10.1109/TSM.2015.2445380
55	J Lee, M Ghaffari, S Elmeligy, ( 2011). Self-maintenance and engineering immune systems: Towards smarter machines and manufacturing systems. Annual Reviews in Control, 35( 1): 111– 122 https://doi.org/10.1016/j.arcontrol.2011.03.007
56	Z Li, Y Wang, K S Wang, ( 2017). Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers: Industry 4.0 scenario. Advances in Manufacturing, 5( 4): 377– 387 https://doi.org/10.1007/s40436-017-0203-8
57	Y Liu D Hou J Bao Y Qi ( 2017). Multi-step ahead time series forecasting for different data patterns based on LSTM recurrent neural network. In: Proceedings of the 14th Web Information Systems and Applications Conference (WISA). Liuzhou: IEEE, 305– 310
58	Z Liu, J Wang, ( 2020). Human–cyber–physical systems: Concepts, challenges and research opportunities. Frontiers of Information Technology & Electronic Engineering, 21( 11): 1535– 1553 https://doi.org/10.1631/FITEE.2000537
59	J Lohmer, R Lasch, ( 2021). Production planning and scheduling in multi-factory production networks: A systematic literature review. International Journal of Production Research, 59( 7): 2028– 2054 https://doi.org/10.1080/00207543.2020.1797207
60	Y Lu, ( 2017). Industry 4.0: A survey on technologies, applications and open research issues. Journal of Industrial Information Integration, 6: 1– 10 https://doi.org/10.1016/j.jii.2017.04.005
61	Y Lu, Q Li, Z Pan, S Y Liang, ( 2018). Prognosis of bearing degradation using gradient variable forgetting factor RLS combined with time series model. IEEE Access, 6: 10986– 10995 https://doi.org/10.1109/ACCESS.2018.2805280
62	Y Luo, L Cheng, Y Liang, J Fu, G Peng, ( 2021). DEEPNOISE: Learning sensor and process noise to detect data integrity attacks in CPS. China Communications, 18( 9): 192– 209 https://doi.org/10.23919/JCC.2021.09.015
63	S Luthra, S K Mangla, ( 2018). Evaluating challenges to Industry 4.0 initiatives for supply chain sustainability in emerging economies. Process Safety and Environmental Protection, 117: 168– 179 https://doi.org/10.1016/j.psep.2018.04.018
64	F Lv C Wen Z Bao M Liu ( 2016). Fault diagnosis based on deep learning. In: Proceedings of the American Control Conference (ACC). Boston, MA: IEEE, 6851– 6856
65	H Martins F Januário L Palma A Cardoso P Gil ( 2015). A machine learning technique in a multi-agent framework for online outliers detection in wireless sensor networks. In: Proceedings of 41st Annual Conference of the IEEE Industrial Electronics Society. Yokohama: IEEE, 688– 693
66	P Mazidi, Y Tohidi, A Ramos, M A Sanz-Bobi, ( 2018). Profit-maximization generation maintenance scheduling through bi-level programming. European Journal of Operational Research, 264( 3): 1045– 1057 https://doi.org/10.1016/j.ejor.2017.07.008
67	S D J McArthur, E M Davidson, V M Catterson, A L Dimeas, N D Hatziargyriou, F Ponci, T Funabashi, ( 2007). Multi-agent systems for power engineering applications – Part I: Concepts, approaches and technical challenges. IEEE Transactions on Power Systems, 22( 4): 1743– 1752 https://doi.org/10.1109/TPWRS.2007.908471
68	D L McGuiness F van Harmelen ( 2004). OWL web ontology language overview
69	M Mohamed, ( 2018). Challenges and benefits of Industry 4.0: An overview. International Journal of Supply and Operations Management, 5( 3): 256– 265 https://doi.org/10.22034/2018.3.7
70	J Morbach, A Wiesner, W Marquardt, ( 2009). OntoCAPE: A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering, 33( 10): 1546– 1556 https://doi.org/10.1016/j.compchemeng.2009.01.019
71	A I Moustapha, R R Selmic, ( 2008). Wireless sensor network modelling using modified recurrent neural networks: Application to fault detection. IEEE Transactions on Instrumentation and Measurement, 57( 5): 981– 988 https://doi.org/10.1109/TIM.2007.913803
72	E Musulin, F Roda, M Basualdo, ( 2013). A knowledge-driven approach for process supervision in chemical plants. Computers & Chemical Engineering, 59: 164– 177 https://doi.org/10.1016/j.compchemeng.2013.06.009
73	S Nannapaneni, S Mahadevan, A Dubey, Y T T Lee, ( 2020). Online monitoring and control of a cyber–physical manufacturing process under uncertainty. Journal of Intelligent Manufacturing, 195: 1289– 1304 pmid: 33363318
74	A B Nassif, M Abu Talib, Q Nasir, F M Dakalbab, ( 2021). Machine learning for anomaly detection: A systematic review. IEEE Access, 9: 78658– 78700 https://doi.org/10.1109/ACCESS.2021.3083060
75	S Natarajan, K Ghosh, R Srinivasan, ( 2012). An ontology for distributed process supervision of large-scale chemical plants. Computers & Chemical Engineering, 46: 124– 140 https://doi.org/10.1016/j.compchemeng.2012.06.009
76	A Nayak, R R Levalle, S Lee, S Y Nof, ( 2016). Resource sharing in cyber–physical systems: Modelling framework and case studies. International Journal of Production Research, 54( 23): 6969– 6983 https://doi.org/10.1080/00207543.2016.1146419
77	E Negri, V Pandhare, L Cattaneo, J Singh, M Macchi, J Lee, ( 2021). Field-synchronized Digital Twin framework for production scheduling with uncertainty. Journal of Intelligent Manufacturing, 32( 4): 1207– 1228 https://doi.org/10.1007/s10845-020-01685-9
78	M Nikraz, P A Bahri, ( 2005). An agent-oriented approach to integrated process operations in chemical plants. Computer-Aided Chemical Engineering, 20: 1585– 1590 https://doi.org/10.1016/S1570-7946(05)80106-3
79	E Oztemel, S Gursev, ( 2020). Literature review of Industry 4.0 and related technologies. Journal of Intelligent Manufacturing, 31( 1): 127– 182 https://doi.org/10.1007/s10845-018-1433-8
80	C G Palacín, J L Pitarch, C Jasch, C A Méndez, Prada C de, ( 2018). Robust integrated production-maintenance scheduling for an evaporation network. Computers & Chemical Engineering, 110: 140– 151 https://doi.org/10.1016/j.compchemeng.2017.12.005
81	P Polverino, M Sorrentino, C Pianese, ( 2017). A model-based diagnostic technique to enhance faults isolability in solid oxide fuel cell systems. Applied Energy, 204: 1198– 1214 https://doi.org/10.1016/j.apenergy.2017.05.069
82	F Qiao, J Liu, Y Ma, ( 2021). Industrial big-data-driven and CPS-based adaptive production scheduling for smart manufacturing. International Journal of Production Research, 59( 23): 7139– 7159 https://doi.org/10.1080/00207543.2020.1836417
83	S Rajasegarar, C Leckie, J C Bezdek, M Palaniswami, ( 2010). Centered hyperspherical and hyperellipsoidal one-class support vector machines for anomaly detection in sensor networks. IEEE Transactions on Information Forensics and Security, 5( 3): 518– 533 https://doi.org/10.1109/TIFS.2010.2051543
84	S Rashid U Akram S Qaisar S A Khan E Felemban ( 2014). Wireless sensor network for distributed event detection based on machine learning. In: Proceedings of the International Conference on Internet of Things (iThings), Green Computing and Communications (GreenCom), and Cyber, Physical and Social Computing (CPSCom). Taipei: IEEE, 540– 545
85	M S Reis, G Gins, T J Rato, ( 2019). Incorporation of process-specific structure in statistical process monitoring: A review. Journal of Quality Technology, 51( 4): 407– 421 https://doi.org/10.1080/00224065.2019.1569954
86	H Ruan, B Dorneanu, H Arellano-Garcia, P Xiao, L Zhang, ( 2022). Deep learning-based fault prediction in wireless sensor network embedded cyber–physical systems for industrial processes. IEEE Access, 10: 10867– 10879 https://doi.org/10.1109/ACCESS.2022.3144333
87	R Sahal, J G Breslin, M I Ali, ( 2020). Big data and stream processing platforms for Industry 4.0 requirements mapping for a predictive maintenance use case. Journal of Manufacturing Systems, 54: 138– 151 https://doi.org/10.1016/j.jmsy.2019.11.004
88	M Said, K ben Abdellafou, O Taouali, ( 2020). Machine learning technique for data-driven fault detection of nonlinear processes. Journal of Intelligent Manufacturing, 31( 4): 865– 884 https://doi.org/10.1007/s10845-019-01483-y
89	Y Saif, M Rizwan, A Almansoori, A Elkamel, ( 2019). MINLP model for reverse osmosis network design under time-variant operation constraints. Industrial & Engineering Chemistry Research, 58( 49): 22315– 22323 https://doi.org/10.1021/acs.iecr.9b05450
90	F L Santamaria, S Macchietto, ( 2018). Integration of optimal cleaning scheduling and control of heat exchanger network undergoing fouling: Model and formulation. Industrial & Engineering Chemistry Research, 57( 38): 12842– 12860 https://doi.org/10.1021/acs.iecr.8b01701
91	R Seiger, C Keller, F Niebling, T Schlegel, ( 2015). Modelling complex and flexible processes for smart cyber–physical environments. Journal of Computational Science, 10: 137– 148 https://doi.org/10.1016/j.jocs.2014.07.001
92	A Sharma, G S Yadava, S G Deshmukh, ( 2011). A literature review and future perspectives on maintenance optimization. Journal of Quality in Maintenance Engineering, 17( 1): 5– 25 https://doi.org/10.1108/13552511111116222
93	R Sharpe, K van Lopik, A Neal, P Goodall, P P Conway, A A West, ( 2019). An industrial evaluation of an Industry 4.0 reference architecture demonstrating the need for the inclusion of security and human components. Computers in Industry, 108: 37– 44 https://doi.org/10.1016/j.compind.2019.02.007
94	Z Shi P Zeng H Yu ( 2017). An ontology-based manufacturing description for flexible production. In: Proceedings of 2nd International Conference on Advanced Robotics and Mechatronics (ICARM). Heifei and Tai’an: IEEE, 362– 267
95	I M Steinberg, M Steinberg, ( 2009). Radio-frequency tag with optoelectronic interface for distributed wireless chemical and biological sensor applications. Sensors and Actuators B: Chemical, 138( 1): 120– 125 https://doi.org/10.1016/j.snb.2009.02.040
96	F Tao, Q Qi, A Liu, A Kusiak, ( 2018). Data-driven smart manufacturing. Journal of Manufacturing Systems, 48: 157– 169 https://doi.org/10.1016/j.jmsy.2018.01.006
97	I A Udugama, C L Gargalo, Y Yamashita, M A Taube, A Palazoglu, B R Young, K V Gernaey, M Kulahci, C Bayer, ( 2020). The role of big data in industrial (bio)chemical process operations. Industrial & Engineering Chemistry Research, 59( 34): 15283– 15297 https://doi.org/10.1021/acs.iecr.0c01872
98	S Vaidya, P Ambad, S Bhosle, ( 2018). Industry 4.0: A glimpse. Procedia Manufacturing, 20: 233– 238 https://doi.org/10.1016/j.promfg.2018.02.034
99	A van Horenbeek, L Pintelon, P Muchiri, ( 2010). Maintenance optimization models and criteria. International Journal of System Assurance Engineering and Management, 1: 189– 200 https://doi.org/10.1007/s13198-011-0045-x
100	G Wan P Wang Z Nie L Xue P Zeng ( 2017). Online reconfiguration of automatic production line using IEC 61499 FBs combined with MAS and ontology. In: Proceedings of 43rd Annual Conference of the IEEE Industrial Electronics Society. Beijing: IEEE, 6683– 6688
101	J Wan, X Li, H N Dai, A Kusiak, M Martinez-Garcia, D Li, ( 2021). Artificial-intelligence-driven customized manufacturing factory: Key technologies, applications, and challenges. Proceedings of the IEEE, 109( 4): 377– 398 https://doi.org/10.1109/JPROC.2020.3034808
102	H Wang Y Zhang ( 2008). Multi-agent based chemical plant process monitoring and management system. In: Proceedings of 4th International Conference on Wireless Communications, Networking and Mobile Computing. Dalian: IEEE, 1– 4
103	K Wang, L Zhuo, Y Shao, D Yue, K F Tsang, ( 2016). Toward distributed data processing on intelligent leak-points prediction in petrochemical industries. IEEE Transactions on Industrial Informatics, 12( 6): 2091– 2102 https://doi.org/10.1109/TII.2016.2537788
104	Y Wang, Y Si, B Huang, Z Lou, ( 2018). Survey on the theoretical research and engineering applications of multivariate statistics process monitoring algorithms: 2008–2017. The Canadian Journal of Chemical Engineering, 96( 10): 2073– 2085 https://doi.org/10.1002/cjce.23249
105	Y Wilhelm, P Reimann, W Gauchel, B Mitschang, ( 2021). Overview on hybrid approaches to fault detection and diagnosis: Combining data-driven, physics-based and knowledge-based models. Procedia CIRP, 99: 278– 283 https://doi.org/10.1016/j.procir.2021.03.041
106	A Willner, V Gowtham, ( 2020). Toward a reference architecture model for industrial edge computing. IEEE Communications Standards Magazine, 4( 4): 42– 48 https://doi.org/10.1109/MCOMSTD.001.2000007
107	J Xie, C C Liu, ( 2017). Multi-agent systems and their applications. Journal of International Council on Electrical Engineering, 7( 1): 188– 197 https://doi.org/10.1080/22348972.2017.1348890
108	L D Xu, E L Xu, L Li, ( 2018). Industry 4.0: State of the art and future trends. International Journal of Production Research, 56( 8): 2941– 2962 https://doi.org/10.1080/00207543.2018.1444806
109	Z Xu, Y Zhang, H Li, W Yang, Q Qi, ( 2020). Dynamic resource provisioning for cyber–physical systems in cloud-fog-edge computing. Journal of Cloud Computing, 9( 1): 32 https://doi.org/10.1186/s13677-020-00181-y
110	L Xue Y Liu P Zeng H Yu Z Shi ( 2015). An ontology based scheme for sensor description in context awareness system. In: Proceedings of the International Conference on Information and Automation. Lijiang: IEEE, 817– 820
111	H C Yan J H Zhou C K Pang ( 2016). New types of faults detection and diagnosis using a mixed soft & hard clustering framework. In: Proceedings of 21st International Conference on Emerging Technologies and Factory Automation (ETFA). Berlin: IEEE, 1– 6
112	J Yan, Y Meng, L Lu, L Li, ( 2017). Industrial big data in an Industry 4.0 environment: Challenges, schemes, and applications for predictive maintenance. IEEE Access, 5: 23484– 23491 https://doi.org/10.1109/ACCESS.2017.2765544
113	Z Zhang, A Mehmood, L Shu, Z Huo, Y Zhang, M Mukherjee, ( 2018). A survey on fault diagnosis in wireless sensor networks. IEEE Access, 6: 11349– 11364 https://doi.org/10.1109/ACCESS.2018.2794519
114	J Zhao, L Cui, L Zhao, T Qiu, B Chen, ( 2009). Learning HAZOP expert system by case-based reasoning and ontology. Computers & Chemical Engineering, 33( 1): 371– 378 https://doi.org/10.1016/j.compchemeng.2008.10.006
115	R Y Zhong, X Xu, E Klotz, R T Newman, ( 2017). Intelligent manufacturing in the context of Industry 4.0: A review. Engineering, 3( 5): 616– 630 https://doi.org/10.1016/J.ENG.2017.05.015
116	J Zhou, Y Zhou, B Wang, J Zang, ( 2019). Human–cyber–physical systems (HCPSs) in the context of new-generation intelligent manufacturing. Engineering, 5( 4): 624– 636 https://doi.org/10.1016/j.eng.2019.07.015
117	X Zhou, M Zhu, W Yu, ( 2021). Maintenance scheduling for flexible multistage manufacturing systems with uncertain demands. International Journal of Production Research, 59( 19): 5831– 5843 https://doi.org/10.1080/00207543.2020.1791998
118	S Zidi, T Moulahi, B Alaya, ( 2018). Fault detection in wireless sensor networks through SVM classifier. IEEE Sensors Journal, 18( 1): 340– 347 https://doi.org/10.1109/JSEN.2017.2771226

[1]	Yang OU, Qiang GUO, Jianguo LIU. Identifying spreading influence nodes for social networks[J]. Front. Eng, 2022, 9(4): 520-549.
[2]	Qian SHI, Chenyu LIU, Chao XIAO. Machine learning in building energy management: A critical review and future directions[J]. Front. Eng, 2022, 9(2): 239-256.
[3]	Christoph Paul SCHIMANSKI, Gabriele PASETTI MONIZZA, Carmen MARCHER, Dominik T. MATT. Development of a BIM-based production planning and control system for Lean Construction through advancement and integration of existing management techniques[J]. Front. Eng, 2021, 8(3): 429-441.
[4]	Chinemelu J. ANUMBA, Abiola AKANMU, Xiao YUAN, Congwen KAN. Cyber–physical systems development for construction applications[J]. Front. Eng, 2021, 8(1): 72-87.
[5]	Yili REN, Jia LIANG, Jian SU, Gang CAO, He LIU. Data sharing mechanism of various mineral resources based on blockchain[J]. Front. Eng, 2020, 7(4): 592-604.
[6]	James M. TIEN. Convergence to real-time decision making[J]. Front. Eng, 2020, 7(2): 204-222.
[7]	Moulay Larbi CHALAL, Benachir MEDJDOUB, Nacer BEZAI, Raid SHRAHILY. Big Data to support sustainable urban energy planning: The EvoEnergy project[J]. Front. Eng, 2020, 7(2): 287-300.
[8]	Feng YANG, Manman WANG. A review of systematic evaluation and improvement in the big data environment[J]. Front. Eng, 2020, 7(1): 27-46.
[9]	Xiaohong CHEN. The development trend and practical innovation of smart cities under the integration of new technologies[J]. Front. Eng, 2019, 6(4): 485-502.
[10]	Liang WANG, Xiaolong XUE, Rebecca J. YANG, Xiaowei LUO, Hongying ZHAO. Built environment and management: Exploring grand challenges and management issues in built environment[J]. Front. Eng, 2019, 6(3): 313-326.
[11]	Jack Xunjie LUO. Fully automatic container terminals of Shanghai Yangshan Port phase IV[J]. Front. Eng, 2019, 6(3): 457-462.
[12]	Panos M. PARDALOS, Mahdi FATHI. A discussion of objective function representation methods in global optimization[J]. Front. Eng, 2018, 5(4): 515-523.
[13]	Donald KENNEDY, Simon P. PHILBIN. The imperative need to develop guidelines to manage human versus machine intelligence[J]. Front. Eng, 2018, 5(2): 182-194.
[14]	Jing LIN, Uday KUMAR. IN2CLOUD: A novel concept for collaborative management of big railway data[J]. Front. Eng, 2017, 4(4): 428-436.
[15]	Jonathan Jingsheng SHI, Saixing ZENG, Xiaohua MENG. Intelligent data analytics is here to change engineering management[J]. Front. Eng, 2017, 4(1): 41-48.

Viewed

Full text

Abstract

Cited

Shared

Discussed