Performance-driven closed-loop optimization and control for smart manufacturing processes in the cloud-edge-device collaborative architecture: : A review and new perspectives
Authors: 
Chi Zhang, Yilin Wang, Ziyan Zhao, Xiaolu Chen, Hao Ye, Shixin Liu, Ying Yang, Kaixiang PengAuthors Info & Claims
Chi Zhang, Yilin Wang, Ziyan Zhao, Xiaolu Chen, Hao Ye, Shixin Liu, Ying Yang, Kaixiang PengAuthors Info & ClaimsReferences
[1]
C. Abeykoon, Design and Applications of soft sensors in polymer processing: a review, IEEE Sens. J. 19 (8) (2019) 2801–2813.
[2]
M. Afrin, J. Jin, A. Rahman, et al., Multi-objective resource allocation for edge cloud based robotic workflow in smart factory, Future Gener. Comput. Syst. 97 (2019) 119–130.
[3]
M. Afrin, J. Jin, A. Rahman, et al., Multi-objective resource allocation for edge cloud based robotic workflow in smart factory, Future Gener. Comput. Syst. 97 (2019) 119–130.
[4]
W. Ahmad, S.A. Khan, J.-M. Kim, A hybrid prognostics technique for rolling element bearings using adaptive predictive models, IEEE Trans. Ind. Electron. 65 (2) (2017) 1577–1584.
[5]
C.F. Alcala, S.J.J. Qin, Reconstruction-based contribution for process monitoring, Automatica 45 (7) (2009) 1593–1600.
[6]
M.J. Arauzo-Bravo, J.M. Cano-Izquierdo, E. Gomez-Sanchez, M.J. López-Nieto, Y.A. Dimitriadis, J. López-Coronado, Automatization of a penicillin production process with soft sensors and an adaptive controller based on neuro fuzzy systems, Control Eng. Pract. 12 (9) (2004) 1073–1090.
[7]
H. Arruda, R. Bavaresco, R. Kunst, E. Bugs, G. Pesenti, J. Barbosa, Data science methods and tools for Industry 4.0: a systematic literature review and taxonomy, Sensors 23 (11) (2023) 5010.
[8]
G. Athanasopoulos, R.J. Hyndman, N. Kourentzes, F. Petropoulos, Forecasting with temporal hierarchies, Eur. J. Oper. Res. 262 (1) (2017) 60–74.
[9]
H. Aytug, M.A. Lawley, K. Mckay, et al., Executing production schedules in the face of uncertainties: a review and some future directions, Eur. J. Oper. Res. 161 (1) (2005) 86–110.
[10]
S.R. Bader, I. Grangel-Gonzalez, P. Nanjappa, et al., A knowledge graph for industry 4.0, \\ Proc. Eur. Semant. Web Conf. (2020).
[11]
P. Bellavista, C. Giannelli, M. Mamei, M. Mendula, M. Picone, Application-driven network-aware digital twin management in industrial edge environments, IEEE Trans. Ind. Inform. 17 (11) (2021) 7791–7801.
[12]
T. Berghout, M. Benbouzid, T. Bentrcia, W. Lim, Y. Amirat, Federated learning for condition monitoring of industrial processes: a review on fault diagnosis methods, challenges, and prospects, Electronics 12 (1) (2023) 1158.
[13]
A. Bernasconi, A. Canakoglu, M. Masseroli, S. Ceri, META-BASE: A novel architecture for large-scale genomic metadata integration, IEEE/ACM Trans. Comput. Biol. Bioinforma. 19 (1) (2022) 543–557.
[14]
M. Braglia, D. Castellano, M. Frosolini, M. Gallo, Overall material usage effectiveness (OME): a structured indicator to measure the effective material usage within manufacturing processes, Prod. Plan. Control 29 (2) (2018) 143–157.
[15]
Nadjib Brahimi, Tarik Aouam, Multi-item production routing problem with backordering: a MILP approach, Int. J. Prod. Res. 54 (4) (2016) 1076–1093.
[16]
B.P. Cai, L. Huang, M. Xie, Bayesian networks in fault diagnosis, IEEE Trans. Ind. Inform. 13 (5) (2017) 2227–2240.
[17]
J.M. Cano-Izquierdo, J. Ibarrola, M.A. Kroeger, Control loop performance assessment with a dynamic neuro-fuzzy model, IEEE Trans. Autom. Sci. Eng. 9 (2) (2012) 377–389.
[18]
K. Cao, Y. Liu, G. Meng, Q. Sun, An overview on edge computing research, IEEE Access 8 (2020) 85714–85728.
[19]
T.Y. Chai, Operational optimization and feedback control for complex industrial processes, Acta Autom. Sin. 39 (11) (2013) 1744–1757.
[20]
H. Chen, W. Qin, L. Wang, Task partitioning and offloading in IoT cloud-edge collaborative computing framework: a survey, J. Cloud Comput. 11 (1) (2022) 86.
[21]
H.S. Chen, Z.B. Yan, Y. Yao, T.B. Huang, Y.S. Wong, Systematic procedure for Granger-causality-based root cause diagnosis of chemical process faults, Ind. Eng. Chem. Res. 57 (29) (2018) 9500–9512.
[22]
J. Chen, Y. Wu, Y. Yang, et al., An Efficient Memristor-Based Circuit Implementation of Squeeze-and-Excitation Fully Convolutional Neural Networks, IEEE Trans. Neural Netw. Learn. Syst. 33 (4) (2022) 1779–1790.
[23]
J. Chen，X. Qiu，P. Liu，X. Huang. Meta Multi-Task Learning for Sequence Modeling [C]// Proceedings of the AAAI Conference on Artificial Intelligence. (2024)
[24]
J.H. Chen, K.C. Liu, On-line batch process monitoring using dynamic PCA and dynamic PLS models, Chem. Eng. Sci. 57 (1) (2002) 63–75.
[25]
X. Chen, S. Li, J. Zhai, Q. Li, Expert system based adaptive dynamic matrix control for ball mill grinding circuit, Expert Syst. Appl. 36 (1) (2009) 716–723.
[26]
Y. Chen, L. Zheng, J. Huang, et al., Prediction of gas emission based on grey-generalized regression neural network, IOP Conf. Ser. Earth Environ. Sci. 467 (2020) DOI:10.1088/1755-1315/467/1/012056.
[27]
C. Chien, C. Chen, Data-driven framework for tool health monitoring and maintenance strategy for smart manufacturing, IEEE Trans. Semicond. Manuf. 33 (4) (2020) 644–652.
[28]
S.W. Choi, I.-B. Lee, Multiblock PLS-based localized process diagnosis, J. Process Control 15 (3) (2005) 295–306.
[29]
Chengbin Chu, Feng Chu, Jinhong Zhong, Shanlin Yang, A polynomial algorithm for a lot-sizing problem with backlogging, outsourcing and limited inventory, Comput. Ind. Eng. 64 (1) (2013) 200–210.
[30]
M. Ciccarelli, A. Papetti, M. Germani, Exploring how new industrial paradigms affect the workforce: A literature review of Operator 4.0, J. Manuf. Syst. 70 (2023) 464–483.
[31]
Y. Cui, Z. Lai, X. Wang, N. Dai, QuickSync: improving synchronization efficiency for mobile cloud storage services, IEEE Trans. Mob. Comput. 16 (12) (2017) 3513–3526.
[32]
Cyber-Physical Systems. Program Announcements & Information. The National Science Foundation, 4201 Wilson Boulevard, Arlington, Virginia22230, USA.〈http://www.nsf.gov/publications/pub_summ.jsp?ods_key=nsf08611〉, July 21, 2009.
[33]
H. Ding, R. Hou, X. Ding, A data fusion equipment monitoring method based on fuzzy set and improved D-S evidence theory, 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), IEEE, 2018,.
[34]
J. Ding, H. Modares, T. Chai, F.L. Lewis, Data-based multi-objective plant-wide performance optimization of industrial processes under dynamic environments, IEEE Trans. Ind. Inform. 12 (2) (2016) 454–465.
[35]
J. Ding, C. Yang, Y. Chen, T. Chai, Research progress and prospects of intelligent optimization decision making in complex industrial process, Acta Autom. Sin. 44 (11) (2018) 1931–1943.
[36]
S.X. Ding, Data-driven design of fault diagnosis and fault-tolerant control systems[M], Springer, 2014.
[37]
T. Ding, W. Jia, M. Shahidehpour, O. Han, Y. Sun, Z. Zhang, Review of optimization methods for energy hub planning, operation, trading, and control, IEEE Trans. Sustain. Energy 13 (3) (2022) 1802–1818.
[38]
C.L. Dong, J. Zhou, A new algorithm of cubic dynamic uncertain causality graph for speeding up temporal causality inference in fault diagnosis, IEEE Trans. Reliab. 72 (2) (2023) 662–677.
[39]
S. Dusmez, H. Duran, B. Akin, Remaining useful lifetime estimation for thermally stressed power MOSFETs based on on-state resistance variation, IEEE Trans. Ind. Appl. 52 (3) (2016) 2554–2563.
[40]
S.D. Eksioglu, H.E. Romeijn, P.Md Pardalos, Cross-facility management of production and transportation planning problem, Comput. Oper. Res. 33 (11) (2006) 3231–3251.
[41]
J. Fan, Y. Jiang, T. Chai, MPC-based setpoint compensation with unreliable wireless communications and constrained operational conditions, Neurocomputing 270 (2017) 110–121.
[42]
Z. Feng, Y. Li, B. Sun, C. Yang, H. Zhu, Z. Chen, A trend-based event-triggering fuzzy controller for the stabilizing control of a large-scale zinc roaster, J. Process Control 97 (2021) 59–71.
[43]
Z. Feng, Y. Li, B. Sun, C. Yang, H. Zhu, Z. Chen, A trend-based event-triggering fuzzy controller for the stabilizing control of a large-scale zinc roaster[J], J. Process Control 97 (2021) 59–71.
[44]
P.M. Frank, Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: A survey and some new results, Automatica 26 (3) (1990) 459–474.
[45]
W. Gang, L. Xiangyang, W. Guangen, G. Yong, M. Simin, Research on data fusion method based on rough set theory and bp neural network, 2020 Int. Conf. Comput. Eng. Appl. (ICCEA), Guangzhou, China (2020) 269–272. DOI: 10.1109/ICCEA50009.2020.00065.
[46]
X.Q. Gao, Y. Fan, C. Shang, D.X. Huang, A review of control loop monitoring and diagnosis: Prospects of controller maintenance in big data era, Chin. J. Chem. Eng. 24 (8) (2016) 952–962.
[47]
Z. Gao, C. Cecati, S.X. Ding, A survey of fault diagnosis and fault-tolerant techniques—Part I: Fault diagnosis with model-based and signal-based approaches, IEEE Trans. Ind. Electron. 62 (6) (2015) 3757–3767.
[48]
Z. Ge, Improved two-level monitoring system for plant-wide processes, Chemom. Intell. Lab. Syst. 132 (2014) 141–151.
[49]
Z. Ge, Distributed predictive modeling framework for prediction and diagnosis of key performance index in plant-wide processes, J. Process Control 65 (2018) 107–117.
[50]
Z. Ge, Z. Song, Two-level multiblock statistical monitoring for plant-wide processes, Korean J. Chem. Eng. 26 (2009) 1467–1475.
[51]
Z. Ge, Z. Song, F. Gao, Review of recent research on data-based process monitoring, Ind. Eng. Chem. Res. 52 (10) (2013) 3543–3562.
[52]
D. Georgakopoulos, P.P. Jayaraman, M. Fazia, et al., Internet of Things and edge cloud computing roadmap for manufacturing, IEEE Cloud Comput. 3 (4) (2016) 66–73.
[53]
M. Ghahramani, Y. Qiao, M.C. Zhou, A. O'Hagan, J. Sweeney, AI-based modeling and data-driven evaluation for smart manufacturing processes, IEEE/CAA J. Autom. Sin. vol. 7 (4) (2020) 1026–1037.
[54]
E. Ghomi, A.M. Rahmani, N. Qader, Cloud manufacturing: challenges, recent advances, open research issues, and future trends, Int. J. Adv. Manuf. Technol. 102 (2019) 3613–3619.
[55]
A. Golmohammadi, S. Tabbakh, R. Ghaemi, A review on workflow scheduling and resource allocation algorithms in distributed mobile clouds, Trans. Emerg. Telecommun. Technol. 34 (8) (2023) 4811.
[56]
K.C. Green, J.S. Armstrong, Simple versus complex forecasting: The evidence, J. Bus. Res. 68 (8) (2015) 1678–1685.
[57]
S. Grime, H.F. Durrant-Whyte, Data fusion in decentralized sensor networks, Control Eng. Pract. 2 (5) (1994) 849–863.
[58]
J. Guo, M. Martinez-Garci, Key technologies towards smart manufacturing based on swarm intelligence and edge computing, Comput. Electr. Eng. 92 (2021).
[59]
X. Guo, S. Liu, M. Zhou, G. Tian, Dual-objective program and scatter search for the optimization of disassembly sequences subject to multi-resource constraints, IEEE Trans. Autom. Sci. Eng. 15 (3) (2018) 1091–1103.
[60]
T.J. Harris, Assessment of control loop performance, Can. J. Chem. Eng. 67 (5) (1989) 856–861.
[61]
T.J. Harris, C.T. Seppala, L.D. Desborough, A review of performance monitoring and assessment techniques for univariate and multivariate control systems, J. Process Control 9 (1) (1999) 1–17.
[62]
B. He, K. Bai, Digital twin-based sustainable intelligent manufacturing: a review, Adv. Manuf. 9 (2021) 1–21.
[63]
K. He, X. Zhang, S. Ren, et al., Deep Residual Learning for Image Recognition, Proc. 2016 IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) (2016).
[64]
M. Heydar, E. Mardaneh, R. Loxton, Approximate dynamic programming for an energy-efficient parallel machine scheduling problem, Eur. J. Oper. Res. 302 (1) (2022) 363–380.
[65]
S. Hong, B. Wang, G. Li, Q. Hong, Performance degradation assessment for bearing based on ensemble empirical mode decomposition and Gaussian mixture model, J. Vib. Acoust. 136 (6) (2014).
[66]
L. Hu, Y. Miao, G. Wu, M.M. Hassan, I. Humar, iRobot-factory: an intelligent robot factory based on cognitive manufacturing and edge computing, Future Gener. Comput. Syst. 90 (2019) 569–577.
[67]
B. Huang, Performance assessment of processes with abrupt changes of disturbances, Can. J. Chem. Eng. 77 (5) (1999) 1044–1054.
[68]
J. Huang, X. Yan, Double-step block division plant-wide fault detection and diagnosis based on variable distributions and relevant features, J. Chemom. 29 (11) (2015) 587–605.
[69]
J. Huang, O.K. Ersoy, X. Yan, Fault detection in dynamic plant-wide process by multi-block slow feature analysis and support vector data description, ISA Trans. 85 (2019) 119–128.
[70]
K. Huang, Z. Tao, C. Wang, T. Guo, C. Yang, W. Gui, Cloud-edge collaborative method for industrial process monitoring based on error-triggered dictionary learning, IEEE Trans. Ind. Inform. 18 (12) (2022) 8957–8966.
[71]
T. Hubauer, S. Lamparter, P. Haase, et al. Use Cases of the Industrial Knowledge Graph at Siemens[C]\\Proceedings of the ISWC, 2018.
[72]
M. Hung, Y. Lin, H. Hsiao, et al., A Novel Implementation framework of digital twins for intelligent manufacturing based on container technology and cloud manufacturing services, IEEE Trans. Autom. Sci. Eng. 19 (3) (2022) 1614–1630.
[73]
S. Iarovyi, W.M. Mohammed, A. Lobov, B.R. Ferrer, J.L.M. Lastra, Cyber–physical systems for open-knowledge-driven manufacturing execution systems, Proc. IEEE 104 (5) (2016) 1142–1154.
[74]
A. Ikegwu, H. Nweke, C. Anikwe, U. Alo, O. Okonkwo, Big data analytics for data-driven industry: a review of data sources, tools, challenges, solutions, and research directions, Clust. Comput. 25 (2022) 3343–3387.
[75]
M.D. Ilić, J. Joo, L. Xie, M. Prica, N. Rotering, A decision-making framework and simulator for sustainable electric energy systems, IEEE Trans. Sustain. Energy 2 (1) (2011) 37–49.
[76]
J.R. Jackson, Simulation research on job shop production, Nav. Res. Logist. Q. 4 (4) (1957) 287–295.
[77]
J. Jäschke, Y. Cao, V. Kariwala, Self-optimizing control – a survey, Annu. Rev. Control 43 (2017) 199–223.
[78]
K. Javed, R. Gouriveau, N. Zerhouni, A new multivariate approach for prognostics based on extreme learning machine and fuzzy clustering, IEEE Trans. Cybern. 45 (12) (2015) 2626–2639.
[79]
M. Jelali, An overview of control performance assessment technology and industrial applications, Control Eng. Pract. 14 (5) (2006) 441–466.
[80]
L.Diaz C. Jenny, Ocampo-Martinez Carlos, Energy efficiency in discrete-manufacturing systems: Insights, trends, and control strategies, J. Manuf. Syst. 52 (2019) 131–145.
[81]
J. Jeon, A. Panagiotelis, F. Petropoulos, Probabilistic forecast reconciliation with applications to wind power and electric load, Eur. J. Oper. Res. 279 (2) (2019) 364–379.
[82]
B. Jiang, J. Li, H. Wang, H. Song, Privacy-preserving federated learning for industrial edge computing via hybrid differential privacy and adaptive compression, IEEE Trans. Ind. Inform. 19 (2) (2023) 1136–1144.
[83]
Q. Jiang, X. Yan, Monitoring multi-mode plant-wide processes by using mutual information-based multi-block PCA, joint probability, and Bayesian inference, Chemom. Intell. Lab. Syst. 136 (2014) 121–137.
[84]
Q. Jiang, X. Yan, Nonlinear plant-wide process monitoring using MI-spectral clustering and Bayesian inference-based multiblock KPCA, J. Process Control 32 (2015) 38–50.
[85]
Q. Jiang, B. Wang, X. Yan, Multiblock independent component analysis integrated with Hellinger distance and Bayesian inference for non-Gaussian plant-wide process monitoring, Ind. Eng. Chem. Res. 54 (9) (2015) 2497–2508.
[86]
Q. Jiang, Z. Song, Y. Yao, Z. Zhu, et al., A review on autoencoder based representation learning for fault detection and diagnosis in industrial processes, Chemom. Intell. Lab. Syst. 231 (2022).
[87]
Q.C. Jiang, X.F. Yan, B. Huang, Review and perspectives of data-driven distributed monitoring for industrial plant-wide processes, Ind. Eng. Chem. Res. 58 (29) (2019) 12899–12912.
[88]
S. Jiang, Z. Zheng, M. Liu, A multi-stage dynamic soft scheduling algorithm for the uncertain steelmaking-continuous casting scheduling problem, Appl. Soft Comput. 60 (2017) 722–736.
[89]
Y. Jiang, S. Yin, J. Dong, O. Kaynak, A review on soft sensors for monitoring, control, and optimization of industrial processes, IEEE Sens. J. 21 (11) (2020) 12868–12881.
[90]
Y. Jiang, S. Yin, O. Kaynak, Performance supervised plant-wide process monitoring in industry 4.0: A roadmap, IEEE Open J. Ind. Electron. Soc. 2 (2020) 21–35.
[91]
Z. Jiang, Y. Jin, E. Mingcheng, et al., Distributed dynamic scheduling for cyber-physical production systems based on a multi-agent system, IEEE Access 6 (2017) 1855–1869.
[92]
Z. Jiang, S. Yuan, J. Ma, et al., The evolution of production scheduling from Industry 3.0 through Industry 4.0, Int. J. Prod. Res. 60 (11) (2022) 3534–3554.
[93]
Ding Jinliang, Yang Cuie, Chen Yuandong, et al., Research progress and prospects of intelligent optimization decision making in complex industrial process, Acta Autom. Sin. 44 (11) (2018) 1931–1943.
[94]
N. Kaur, A. Kumar, R. Kumar, A systematic review on task scheduling in Fog computing: Taxonomy, tools, challenges, and future directions, Concuttency Comput. -Pract. Exp. 33 (21) (2021) 6432.
[95]
Z. Kazemi, J.K. Rask, C. Gomes, E. Yildiz, P.G. Larsen, Movable factory—A systematic literature review of concepts, requirements, applications, and gaps, J. Manuf. Syst. 69 (2023) 189–207.
[96]
V.R. Kebande, F.M. Awaysheh, R.A. Ikuesan, et al., A block chain-based multi-factor authentication model for cloud-enabled Internet of vehicles, Sensors 21 (18) (2021) 6018.
[97]
W. Khan, E. Ahmed, S. Hakak, et al., Edge computing: a survey, Future Gener. Comput. Syst. 97 (2019) 219–235.
[98]
A. Kiam Heong, G. Chong, L. Yun, PID control system analysis, design, and technology, IEEE Trans. Control Syst. Technol. 13 (4) (2005) 559–576.
[99]
J.K. Kimotho, T. Hemsel, and W. Sextro. Estimation of remaining useful lifetime of piezoelectric transducers based on self-sensing[J]. IEEE Transactions on Reliability, 66(3): 914-923.(2024).
[100]
T. Koenings, M. Krueger, H. Luo, S.X. Ding, A data-driven computation method for the gap metric and the optimal stability margin, IEEE Trans. Autom. Control 63 (3) (2017) 805–810.
[101]
M. Kong, J. Pei, J. Xu, et al., A robust optimization approach for integrated steel production and batch delivery scheduling with uncertain rolling times and deterioration effect, Int. J. Prod. Res. 58 (17) (2020) 5132–5154.
[102]
N. Kourentzes, B. Rostami-Tabar, D.K. Barrow, Demand forecasting by temporal aggregation: Using optimal or multiple aggregation levels?, J. Bus. Res. 78 (2017) 1–9.
[103]
D.J. Kozub. Monitoring and diagnosis of automated controllers in the chemical process industries[C]\\Proceedings of AIChE Annual Meeting, St. Louis, USA, 1993.
[104]
J.V. Kresta, J.F. Macgregor, T.E. Marlin, Multivariate statistical monitoring of process operating performance, Can. J. Chem. Eng. 69 (1) (1991) 35–47.
[105]
A. Kusiak, Smart manufacturing must embrace big data, Nature 544 (7648) (2017) 23–25.
[106]
J. Lee, B. Kang, S.H. Kang, Integrating independent component analysis and local outlier factor for plant-wide process monitoring, J. Process Control 21 (7) (2011) 1011–1021.
[107]
J.H. Lee, Model predictive control: Review of the three decades of development, Int. J. Control, Autom. Syst. 9 (3) (2011) 415–424.
[108]
J.M. Lee, C.K. Yoo, S.W. Choi, P.A. Vanrolleghem, I.B. Lee, Nonlinear process monitoring using kernel principal component analysis, Chem. Eng. Sci. 59 (1) (2004) 223–234.
[109]
W.S. Lee, D.L. Grosh, F.A. Tillman, C.H. Lie, Fault tree analysis, methods, and applications - a review, IEEE Trans. Reliab. 34 (3) (1985) 194–203.
[110]
J. Leng, D. Yan, Q. Liu, K. Xu, et al., ManuChain: combining permissioned block chain with a holistic optimization model as bi-level intelligence for smart manufacturing, IEEE Trans. Syst., Man, Cybern.: Syst. 50 (1) (2020) 182–192.
[111]
J.M.L. Lesaca, E.Z. Guerrero, J.M. Lopez-Guede, J. Ramos-Hernanz, A.L. Juaristi, O. Akizu, Measuring global effectiveness of integrated electric energy systems, Int. J. Hydrog. Energy 42 (28) (2017) 18121–18133.
[112]
R. Lestage, A. Pomerleau, D. Hodouin, Constrained real-time optimization of a grinding circuit using steady-state linear programming supervisory control, Powder Technol. 124 (3) (2002) 254–263.
[113]
J. Li, B. Kiumarsi, T. Chai, F.L. Lewis, J. Fan, Off-policy reinforcement learning: optimal operational control for two-time-scale industrial processes, IEEE Trans. Cybern. 47 (12) (2017) 4547–4558.
[114]
L. Li, Y. Wang, B. Sun, Y. Qian, Operating performance assessment for transition state of industrial processes, Chem. Eng. Technol. 43 (12) (2020) 2567–2575.
[115]
B. Lin, B. Recke, J.K. Knudsen, S. B. Jørgensen. A systematic approach for soft sensor development, Comput. Chem. Eng. 31 (2007) 419–425.
[116]
Y. Lin, M. Hung, H. Huang, C. Chen, et al., Development of advanced manufacturing cloud of things (AMCoT)—a smart manufacturing platform, IEEE Robot. Autom. Lett. 2 (3) (2017) 1809–1816.
[117]
J. Liu, L. Pineda, E. Pacitti, A. Costan, et al., Efficient scheduling of scientific workflows using hot metadata in a multisite cloud, IEEE Trans. Knowl. Data Eng. 31 (10) (2019) 1940–1953.
[118]
J. Liu, Y. Sun, C. Han, et al., Deep Representation Learning on Long-Tailed Data: A Learnable Embedding Augmentation Perspective, IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) (2020) 2967–2976.
[119]
L. Liu, Q. Guo, D. Liu, Y. Peng, Data-driven remaining useful life prediction considering sensor anomaly detection and data recovery, IEEE Access 7 (2019) 58336–58345.
[120]
X. Liu, F. Chu, C. Chu, C. Wang, Lot sizing with bounded inventory and lost sales, Int. J. Prod. Res. 45 (24) (2007) 5881–5894.
[121]
Y. Liu, Z.Q. Ge, Weighted random forests for fault classification in industrial processes with hierarchical clustering model selection, J. Process Control 64 (2018) 62–70.
[122]
Y. Liu, F. Wang, F. Gao, H. Cui, Hierarchical multiblock T-PLS based operating performance assessment for plant-wide processes, Ind. Eng. Chem. Res. 57 (43) (2018) 14617–14627.
[123]
Y. Liu, F. Wang, Y. Chang, F. Gao, D. He, Performance-relevant kernel independent component analysis based operating performance assessment for nonlinear and non-Gaussian industrial processes, Chem. Eng. Sci. 209 (2019).
[124]
Y. Liu, C. Chi, Y. Zhang, T. Tang, Identification and resolution for industrial internet: architecture and key technology, IEEE Internet Things J. 9 (18) (2022) 16780–16794.
[125]
Z. Liu, W. Zhang, T. Quek, S. Lin, Deep fusion of heterogeneous sensor data, IEEE Int. Conf. Acoust., Speech Signal Process. (ICASSP) 2017 (2017) 5965–5969. DOI: 10.1109/ICASSP.2017.7953301.
[126]
J. Long, Z. Zheng, X. Gao, Dynamic scheduling in steelmaking-continuous casting production for continuous caster breakdown, Int. J. Prod. Res. 55 (11) (2017) 3197–3216.
[127]
J. Long, Z. Sun, P.M. Pardalos, et al., A robust dynamic scheduling approach based on release time series forecasting for the steelmaking-continuous casting production, Appl. Soft Comput. 92 (2020).
[128]
X. Lu, B. Kiumarsi, T. Chai, Y. Jiang, F.L. Lewis, Operational control of mineral grinding processes using adaptive dynamic programming and reference governor, IEEE Trans. Ind. Inform. 15 (4) (2019) 2210–2221.
[129]
Y. Lu, X. Huang, K. Zhang, S. Maharjan, Y. Zhang, Low-latency federated learning and blockchain for edge association in digital twin empowered 6G networks, IEEE Trans. Ind. Inform. 17 (7) (2021) 5098–5107.
[130]
H. Luo, H. Zhao, S. Yin, Data-driven design of fog-computing-aided process monitoring system for large-scale industrial processes, IEEE Trans. Ind. Inform. 14 (10) (2018) 4631–4641.
[131]
J. Ma, H. Zhou, C. Liu, et al., Study on edge-cloud collaborative production scheduling based on enterprises with multi-factory, IEEE Access 8 (2020) 30069–30080.
[132]
L. Ma, J. Dong, K.X. Peng, Root cause diagnosis of quality-related faults in industrial multimode processes using robust Gaussian mixture model and transfer entropy, Neurocomputing 285 (2018) 60–73.
[133]
L. Ma, J. Dong, C. Hu, K. Peng, A novel decentralized detection framework for quality-related faults in manufacturing industrial processes, Neurocomputing 428 (2021) 30–41.
[134]
L. Ma, J. Dong, K. Peng, A novel multilabel classification framework for coupling faultsin hot rolling processes, IEEE Trans. Control Syst. Technol. 30 (2) (2022) 877–884.
[135]
L. Ma, M. Wang, J. Dong, K. Peng, A novel distributed detection framework for quality-related faults in industrial plant-wide processes, Neurocomputing 492 (2022) 126–136.
[136]
Iraj Mahdavi, Kaveh Taghizadeh, Morteza Bagherpour, Maghsud Solimanpur, Modeling of multi-period multi-product production planning considering production routes., Int. J. Prod. Res. 50 (6) (2012) 1749–1766.
[137]
A. Maxim, D. Copot, C. Copot, C.M. Ionescu, The 5W’s for Control as Part of Industry 4.0: Why, What, Where, Who, and When—A PID and MPC Control Perspective, Inventions 4 (1) (2019) 1–9.
[138]
H. Miao, B. Li, C. Sun, J. Liu, Joint learning of degradation assessment and RUL prediction for aeroengines via dual-task deep LSTM networks, IEEE Trans. Ind. Inform. 15 (9) (2019) 5023–5032.
[139]
P. Miller, R. Swanson, C. Heckler, Contribution plots: a missing link in multivariate quality control, Appl. Math. Comput. Sci. 8 (4) (1998) 775–792.
[140]
Q. Min, Y. Lu, Z. Liu, C. Su, B. Wang, Machine learning based digital twin framework for production optimization in petrochemical industry, Int. J. Inf. Manag. 49 (2019) 502–519.
[141]
M. Mohammadipour, J.E. Boylan, Forecast horizon aggregation in integer autoregressive moving average (INARMA) models, Omega 40 (6) (2012) 703–712.
[142]
P. Muchiri, L. Pintelon, Performance measurement using overall equipment effectiveness (OEE): literature review and practical application discussion, Int. J. Prod. Res. 46 (13) (2008) 3517–3535.
[143]
K. Muthiah, S. Huang, Overall throughput effectiveness (OTE) metric for factory-level performance monitoring and bottleneck detection, Int. J. Prod. Res. 45 (20) (2007) 4753–4769.
[144]
C. Nan, F. Khan, M.T. Iqbal, Real-time fault diagnosis using knowledge-based expert system, Process Saf. Environ. Prot. 86 (2008) 55–71.
[145]
T. Nesztler and M. Georgescu. Advances and challenges for scalable cloud-based infrastructure for building data analysis and simulation[C]\\the Proceedings of Building Simulation 2019: 16th Conference of IBPSA, 2020.
[146]
D. Niu, X. Liu, Y. Liu, M. Jia, Optimization control of hydrometallurgical leaching process based on IDE-MPC, Miner. Eng. 176 (2022).
[147]
A.M. Ornek, O. Cengiz, Capacitated lot sizing with alternative routings and overtime decisions, Int. J. Prod. Res. 44 (24) (2006) 5363–5389.
[148]
X. Ou, Q. Chang, N. Chakraborty, A method integrating Q-Learning with approximate dynamic programming for gantry work cell scheduling, IEEE Trans. Autom. Sci. Eng. 18 (1) (2020) 85–93.
[149]
D. Ouelhadj, S. Petrovic, A survey of dynamic scheduling in manufacturing systems, J. Sched. 12 (4) (2009) 417–431.
[150]
D. Park, S. Kim, Y. An, J.Y. Jung, LiReD: a light-weight real-time fault detection system for edge computing using LSTM recurrent neural networks, Sensors 18 (7) (2018) 2110.
[151]
K. Peng, K. Zhang, J. Dong, B. You, Quality-relevant fault detection and diagnosis for hot strip mill process with multi-specification and multi-batch measurements, J. Frankl. Inst. 352 (3) (2015) 987–1006.
[152]
K. Peng, H. Huang, B. Zhao, et al., Intelligent computation offloading and resource allocation in IIoT with end-edge-cloud computing using NSGA-III, IEEE Trans. Netw. Sci. Eng. (2022).
[153]
F. Petropoulos, Forecasting: theory and practice, Int. J. Forecast. 38 (3) (2022) 705–871.
[154]
Z. Ping, C. Tianyou, W. Hong, Intelligent optimal-setting control for grinding circuits of mineral processing process, IEEE Trans. Autom. Sci. Eng. 6 (4) (2009) 730–743.
[155]
K.V. Pontes, I.J. Wolf, M. Embiruçu, W. Marquardt, Dynamic real-time optimization of industrial polymerization processes with fast dynamics, Ind. Eng. Chem. Res. 54 (47) (2015) 11881–11893.
[156]
F. Qian, The future of smart process manufacturing, Engineering 22 (2023) 20–22.
[157]
F. Qian, W.M. Zhong, W.L. Du, Fundamental theories and key technologies for smart and optimal manufacturing in the process industry, Engineering 3 (2017) 154–160.
[158]
S.J. Qin, Control performance monitoring - a review and assessment, Comput. Chem. Eng. 23 (2) (1998) 173–186.
[159]
S.J. Qin, S. Valle, M.J. Piovoso, On unifying multiblock analysis with application to decentralized process monitoring, J. Chemom.: A J. Chemom. Soc. 15 (9) (2001) 715–742.
[160]
M. Quinones-Grueiro, A. Prieto-Moreno, C. Verde, O. Llanes-Santiago, Data-driven monitoring of multimode continuous processes: a review, Chemom. Intell. Lab. Syst. 189 (2019) 56–71.
[161]
M. Raj, S. Swaroop, S. Kumar, R. Bhushan, V. Kumar, M. Borkar, Kobetsu kaizen losses analysis to enhance the overall plant effectiveness in steel manufacturing industry–a case study at JSPL, Raigarh, Int. Res. J. Eng. Technol. 4 (2017) 12.
[162]
A. Ravi, N.S. Kaisare, A multi-objective dynamic RTO for plant-wide control, IFAC Pap. 53 (1) (2020) 368–373.
[163]
H. Ren, Z.W. Chen, Z.H. Jiang, C.H. Yang, W.H. Gui, An industrial multilevel knowledge graph-based local-global monitoring for plant-wide processes, IEEE Trans. Instrum. Meas. 70 (2021) 3526715.
[164]
L. Ren, Z.D. Jia, T. Wang, Y.H. Ma, L.H. Wang, LM-CNN: a cloud-edge collaborative method for adaptive fault diagnosis with label sampling space enlarging, IEEE Trans. Ind. Inform. 18 (12) (2022) 9057–9067.
[165]
L. Ribeiro, M. Björkman, Transitioning from standard automation solutions to cyber-physical production systems: an assessment of critical conceptual and technical challenges, IEEE Syst. J. 12 (4) (2018) 3816–3827.
[166]
M. Rong, H. Shi, B. Song, Y. Tao, Multi-block dynamic weighted principal component regression strategy for dynamic plant-wide process monitoring, Measurement 183 (2021).
[167]
D. Rossit, F. Tohmé, M. Frutos, Production planning and scheduling in Cyber-Physical Production Systems: a review, Int. J. Comput. Integr. Manuf. 32 (4) (2019) 385–395.
[168]
B. Rostami-Tabar, M.Z. Babai, A. Syntetos, Y. Ducq, A note on the forecast performance of temporal aggregation, Nav. Res. Logist. 61 (7) (2014) 489–500.
[169]
B. Rostami-Tabar, M.Z. Babai, A. Syntetos, Y. Ducq, Demand forecasting by temporal aggregation, Nav. Res. Logist. 60 (6) (2013) 479–498.
[170]
C. Rusinko, Green manufacturing: an evaluation of environmentally sustainable manufacturing practices and their impact on competitive outcomes, IEEE Trans. Eng. Manag. 54 (3) (2007) 445–454.
[171]
I. Sabuncuoglu, S. Goren, Hedging production schedules against uncertainty in manufacturing environment with a review of robustness and stability research, Int. J. Comput. Integr. Manuf. 22 (2) (2009) 138–157.
[172]
M. Satyanarayanan, The emergence of edge computing, Computer 50 (1) (2017) 30–39.
[173]
M. Schwenzer, M. Ay, T. Bergs, D. Abel, Review on model predictive control: an engineering perspective, Int. J. Adv. Manuf. Technol. 117 (5) (2021) 1327–1349.
[174]
Y. Shan, J. Liu, Y. Xu, J. Zhou, A combined multi-objective optimization model for degradation trend prediction of pumped storage unit, Measurement 169 (2021).
[175]
Y. Shardt, H. Hao, S.X. Ding, A new soft-sensor-based process monitoring scheme incorporzating infreqzuent KPI measurements, IEEE Trans. Ind. Electron. 62 (6) (2015) 3843–3851.
[176]
Y.A. Shardt, H. Hao, S.X. Ding, A new soft-sensor-based process monitoring scheme incorporating infrequent KPI measurements, IEEE Trans. Ind. Electron. 62 (6) (2014) 3843–3851.
[177]
B.J. Shean, J.J. Cilliers, A review of froth flotation control, Int. J. Miner. Process. 100 (2011) 57–71.
[178]
Yu Shengping, Tianyou Chai, Rescheduling method for starting time delay in steelmaking and continuous casting production processes, Acta Autom. Sin. 42 (03) (2016) 358–374.
[179]
Y. Shi, Z. Zhang, L. Xie, H. Su, ILC-based two-layer strategy for economic performance improvement in industrial MPC systems, J. Process Control 108 (2021) 136–147.
[180]
X.-S. Si, W. Wang, C.-H. Hu, D.-H. Zhou, Remaining useful life estimation–a review on the statistical data driven approaches, Eur. J. Oper. Res. 213 (1) (2011) 1–14.
[181]
J. Siderska, K. Jadaan, Cloud manufacturing: a service-oriented manufacturing paradigm. A review paper, Eng. Manag. Prod. Serv. 10 (1) (2018) 22–31.
[182]
D. Sinha, R. Roy, Reviewing cyber-physical system as a part of smart factory in industry 4.0, IEEE Eng. Manag. Rev. 48 (2) (2020) 103–117.
[183]
F. Song, M. Zhu, Y. Zhou, I. You, H. Zhang, Smart collaborative tracking for ubiquitous power IoT in edge-cloud interplay domain, IEEE Internet Things J. 7 (7) (2020) 6046–6055.
[184]
P. Song, C. Zhao, Slow down to go better: a survey on slow feature analysis, IEEE Trans. Neural Netw. Learn. Syst. 35 (3) (2024) 3416–3436.
[185]
E. Spiliotis, F. Petropoulos, V. Assimakopoulos, Improving the forecasting performance of temporal hierarchies, Plos One 14 (10) (2019).
[186]
E. Spiliotis, F. Petropoulos, N. Kourentzes, V. Assimakopoulos, Cross-temporal aggregation: Improving the forecast accuracy of hierarchical electricity consumption, Appl. Energy 261 (2020).
[187]
J.M. Spitter, C.A.J. Hurkens, A.G. de Kok, J.K. Lenstra, E.G. Negenman, Linear programming models with planned lead times for Supply Chain Operations Planning, Eur. J. Oper. Res. 163 (3) (2005) 706–720.
[188]
L. Sun, S. Yu, Scheduling a real-world hybrid flow shop with variable processing times using Lagrangian relaxation, Int. J. Adv. Manuf. Technol. 78 (9-12) (2015) 1961–1970.
[189]
C. Szegedy, V. Vanhoucke, S. Ioffe S, et al., Rethinking the Inception Architecture for Computer Vision, 2016 IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) (2016) DOI: 10.1109/CVPR.2016.308.
[190]
H. Tang, C. Li, J. Bai, et al., Dynamic resource allocation strategy for latency-critical and computation-intensive applications in cloud–edge environment, Comput. Commun. 134 (2019) 70–82.
[191]
J. Tang, G. Liu, Q.T. Pan, A review on representative swarm intelligence algorithms for solving optimization problems: applications and trends, IEEE/CAA J. Autom. Sin. 8 (10) (2021) 1627–1643.
[192]
L. Tang, Y. Zhao, J. Liu, An improved differential evolution algorithm for practical dynamic scheduling in steelmaking-continuous casting production, IEEE Trans. Evolut. Comput. 18 (2) (2014) 209–225.
[193]
Ajinkya Tanksale, J.K. Jha, Solving multi-region multi-facility inventory allocation and transportation problem: A case of Indian public distribution system, Comput. Ind. Eng. 110 (2017) 175–190.
[194]
Kostis Taxakis, Chrissoleon Papadopoulos, A design model and a production-distribution and inventory planning model in multi-product supply chain networks, Int. J. Prod. Res. 54 (21) (2016) 6436–6457.
[195]
J. Tian, D. Han, L. Xiao, et al., Multi-scale deep coupling convolutional neural network with heterogeneous sensor data for intelligent fault diagnosis, J. Intell. Fuzzy Syst.: Appl. Eng. Technol. 41 (2021) 2225–2238.
[196]
Y. Tian, T. Li, J. Xiong, M. Bhuiyan, J. Ma, C. Peng, A blockchain-based machine learning framework for edge services in IIoT, IEEE Trans. Ind. Inform. 18 (3) (2022) 1918–1929.
[197]
A. Tighazoui, C. Sauvey, N. Sauer, Predictive-reactive strategy for identical parallel machine rescheduling, Comput. Oper. Res. 134 (2021).
[198]
Franklina Maria Bragion Toledo, Vinicius Amaral Armentano, A Lagrangian-based heuristic for the capacitated lot-sizing problem in parallel machines, Eur. J. Oper. Res. 175 (2) (2006) 1070–1083.
[199]
S. Torkaman, S.M.T. Fatemi Ghomi, B. Karimi, Multi-stage multi-product multi-period production planning with sequence-dependent setups in closed-loop supply chain, Comput. Ind. Eng. 113 (2017) 602–613.
[200]
Shaghayegh Vaziri, Arash Zaretalab, Mohammad Esmaeili, S.T.A. Niaki, An integrated production and procurement design for a multi-period multi-product manufacturing system with machine assignment and warehouse constraint, Appl. Soft Comput. 70 (2018) 238–262.
[201]
H. Vedam, V. Venkatasubramanian, Signed digraph based multiple fault diagnosis, Comput. Chem. Eng. 21 (1997) 655–660.
[202]
H. Vedam, V. Venkatasubramanian, PCA-SDG based process monitoring and fault diagnosis, ([J]) Control Eng. Pract. 7 (7) (1999) 903–917.
[203]
Y.M. Wan, F. Yang, N. Lv, et al., Statistical root cause analysis of novel faults based on digraph models, Chem. Eng. Res. Des. 91 (1) (2013) 87–99.
[204]
B. Wang, F. Tao, X. Fang, et al., Smart manufacturing and intelligent manufacturing: a comparative review, Engineering 7 (2021) 738–757.
[205]
C. Wang, J. Liu, K. Wu, et al., Solving multi-task optimization problems with adaptive knowledge transfer via anomaly detection, IEEE Trans. Evolut. Comput. 26 (2) (2022) 304–318.
[206]
D. Wang, F. Liu, Y. Jin, A proactive scheduling approach to steel rolling process with stochastic machine breakdown, Nat. Comput. 18 (4) (2019) 679–694.
[207]
Q.Z. Wang, Q. Li, K. Wang, H. Wang, P. Zeng. Efficient federated learning for fault diagnosis in industrial cloud-edge computing[J]. Computing, 103(10): 2319-2337.(2024a).
[208]
T. Wang, H. Ke, X. Zheng, K. Wang, A.K. Sangaiah, A. Liu, Big Data Cleaning Based on Mobile Edge Computing in Industrial Sensor-Cloud, IEEE Trans. Ind. Inform. 16 (2) (2020) 1321–1329.
[209]
X. Wang, Y. Li, Content retrieval based on vehicular cloud in internet of vehicles, (Y.) IEEE Trans. Comput. Soc. Syst. 6 (3) (2019) 582–591.
[210]
Y. Wang, C. Zhao, S. Yang, et al., MPCSM: Microservice placement for edge-cloud collaborative smart manufacturing, IEEE Trans. Ind. Inform. 17 (9) (2021) 5898–5908.
[211]
Y. Wang, Y. Peng, Y. Zi, X. Jin, and K.L. Tsui. A two-stage data-driven-based prognostic approach for bearing degradation problem[J]. IEEE Transactions on Industrial Informatics, 12(3): 924-932.(2024b).
[212]
Gui Weihua, Wang Chenghong, Xie Yongfang, et al., The necessary way to realize great-leap-forward development of process industries, Bull. Natl. Nat. Sci. Found. China 5 (2015) 337–342.
[213]
Tao Wu, Fan Xiao, Canrong Zhang, Yan He, Zhe Liang, The green capacitated multi-item lot sizing problem with parallel machines, Comput. Oper. Res. 98 (2018) 149–164.
[214]
Y. Xia, Cloud control systems, IEEE/CAA J. Autom. Sin. 2 (2) (2015) 134–142.
[215]
Y. Xia, R. Gao, M. Lin, Green energy complementary based on intelligent power plant cloud control system, Acta Autom. Sin. 46 (9) (2020) 1844–1868.
[216]
Y. Xiao, S. Zheng, J. Shi, X. Du, J. Hong, Knowledge graph-based manufacturing process planning: A state-of-the-art review, J. Manuf. Syst. 70 (2023) 417–435.
[217]
X. Xu, From cloud computing to cloud manufacturing, Robot. Comput. Integr. Manuf. 28 (1) (2012) 75–86.
[218]
H. Xue, A. Zhang, L. Su, et al., Deep Fusion: A Deep Learning Framework for the Fusion of Heterogeneous Sensory Data[J], ACM, 2019, DOI: 10.1145/3323679.3326513.
[219]
Y. Yadekar, E. Shehab, J. Mehnen, Taxonomy and uncertainties of cloud manufacturing, Int. J. Agil. Syst. Manag. 9 (1) (2016) 48–66.
[220]
C. Yag, B. Sun, Y. Li, K. Huang, W. Gui, Cooperative optimization and intelligent control of complex production processes, Acta Autom. Sin. 49 (3) (2023) 528–539.
[221]
W. Yan, D. Tang, Y. Lin, A data-driven soft sensor modeling method based on deep learning and its application, IEEE Trans. Ind. Electron. 64 (5) (2016) 4237–4245.
[222]
C. Yang, S. Lan, L. Wang, et al., Big data driven edge-cloud collaboration architecture for cloud manufacturing: a software defined perspective, IEEE Access 8 (1)) (2020) 45938–45950.
[223]
E. Yang, F. Hao, J. Gao, Y. Wu, G. Min, Entity Spatio-temporal Evolution Summarization in Knowledge Graphs, 2020 IEEE Int. Conf. Knowl. Graph (ICKG) (2020) 181–187,.
[224]
H.B. Yang, S.K. Ong, A.Y.C. Nee, G.D. Jiang, X.S. Mei, Microservices-based cloud-edge collaborative condition monitoring platform for smart manufacturing systems, Int. J. Prod. Res. (2022),.
[225]
Y. Yang, W. Chen, L. Wei, et al., Robust optimization for integrated scrap steel charge considering uncertain metal elements concentrations and production scheduling under time-of-use electricity tariff, J. Clean. Prod. 176 (2018) 800–812.
[226]
Z. Yang, Z. Ge, On paradigm of industrial big data analytics: from evolution to revolution, IEEE Trans. Ind. Inform. 18 (12) (2022) 8373–8388.
[227]
L. Yao, Z. Ge, Industrial big data modeling and monitoring framework for plant-wide processes, IEEE Trans. Ind. Inform. 17 (9) (2021) 6399–6408.
[228]
H. Ye, K. Liu, A Generic Online nonparametric monitoring and sampling strategy for high-dimensional heterogeneous processes, IEEE Trans. Autom. Sci. Eng. 19 (3) (2022) 1503–1516.
[229]
L. Ye, Y. Liu, Z. Fei, J. Liang, Online probabilistic assessment of operating performance based on safety and optimality indices for multimode industrial processes, Ind. Eng. Chem. Res. 48 (24) (2009) 10912–10923.
[230]
Y. Ye, J. Li, Z. Li, et al., Robust optimization and stochastic programming approaches for medium-term production scheduling of a large-scale steelmaking continuous casting process under demand uncertainty, Comput. Chem. Eng. 66 (2014) 165–185.
[231]
S. Yin, S.X. Ding, X. Xie, H. Luo, A review on basic data-driven approaches for industrial process monitoring, IEEE Trans. Ind. Electron. 61 (11) (2014) 6418–6428.
[232]
J. Yu, Bearing performance degradation assessment using locality preserving projections and Gaussian mixture models, Mech. Syst. Signal Process. 25 (7) (2011) 2573–2588.
[233]
Yang Yu, Xiaochuan Luo, Qiang Liu, Model predictive control of a dynamic nonlinear PDE system with application to continuous casting, J. Process Control Volume 65 (2018) 41–55.
[234]
H. Yuan, M.C. Zhou, Profit-maximized collaborative computation offloading and resource allocation in distributed cloud and edge computing systems, IEEE Trans. Autom. Sci. Eng. 18 (3) (2020) 1277–1287.
[235]
N. Zhai, X. Zhou, S. Li, H. Shi, Soft sensor model for billet temperature in multiple heating furnaces based on transfer learning, IEEE Trans. Instrum. Meas. (2023),.
[236]
C. Zhang, K. Peng, J. Dong, A novel plant-wide process monitoring framework based on distributed Gap-SVDD with adaptive radius, Neurocomputing 350 (2019) 1–12.
[237]
C. Zhang, K. Peng, J. Dong, X. Zhang, KPI-related operating performance assessment based on distributed ImRMR-KOCTA for hot strip mill process, Expert Syst. Appl. 209 (2022).
[238]
H. Zhang, Y. Deng, Engine fault diagnosis based on sensor data fusion considering information quality and evidence theory, Adv. Mech. Eng. 10 (11) (2018) 1–9. DOI:10.1177/1687814018809184.
[239]
H. Zhang, B. Lennox, Integrated condition monitoring and control of fed-batch fermentation processes, J. Process Control 14 (1) (2004) 41–50.
[240]
H. Zhang, C. Zhang, J. Dong, K. Peng, A new key performance indicator oriented industrial process monitoring and operating performance assessment method based on improved Hessian locally linear embedding, Int. J. Syst. Sci. 53 (16) (2022) 3538–3555.
[241]
K. Zhang, H. Hao, Z. Chen, S.X. Ding, K. Peng, A comparison and evaluation of key performance indicator-based multivariate statistics process monitoring approaches, J. Process Control 33 (2015) 112–126.
[242]
M.Q. Zhang, X. Jiang, Y. Xu, X.L. Luo, Decentralized dynamic monitoring based on multi-block reorganized subspace integrated with Bayesian inference for plant-wide process, Chemom. Intell. Lab. Syst. 193 (2019).
[243]
R. Zhang, S. Song, C. Wu, Robust scheduling of hot rolling production by local search enhanced ant colony optimization algorithm, IEEE Trans. Ind. Inform. 16 (4) (2020) 2809–2819.
[244]
X. Zhang, L. Qi, W. Dou, Q. He, et al., MRMondrian: scalable multidimensional anonymisation for big data privacy preservation, IEEE Trans. Big Data 8 (1) (2022) 125–139.
[245]
Y. Zhang, Q. Yang, A survey on multi-task learning, IEEE Trans. Knowl. Data Eng. 34 (12) (2022) 5596–56609.
[246]
Y. Zhang, X. Zhang, J. Li, et al., Semi-Supervised Contrastive Learning With Similarity Co-Calibration, IEEE Trans. Multimed. 25 (2023) 1749–1759.
[247]
Z. Zhang, Z. Zhao, S. Qin, S. Liu, M. Zhou, Multi-product multi-stage multi-period resource allocation for minimizing batch-processing steel production cost, IEEE Trans. Autom. Sci. Eng. (2024),.
[248]
Y.W. Zhang, H. Zhou, S.J. Qin, T.Y. Chai, Decentralized fault diagnosis of large-scale processes using multiblock kernel partial least squares, IEEE Trans. Ind. Inform. 6 (1) (2010) 3–10.
[249]
Yale Zhang, Michael S. Dudzic, Online monitoring of steel casting processes using multivariate statistical technologies: From continuous to transitional operations, J. Process Control Volume 16 (Issue 8) (2006) 819–829.
[250]
Z. Zhang, Y. Zhang, D. Yue, C. Dou, X. Ding, H. Zhang, Economic-driven hierarchical voltage regulation of incremental distribution networks: a cloud-edge collaboration based perspective, IEEE Trans. Ind. Inform. 18 (3) (2022) 1746–1757.
[251]
Z.H. Zhang, T. Jiang, S.H. Li, Y.P. Yang, Automated feature learning for nonlinear process monitoring - an approach using stacked denoising autoencoder and k-nearest neighbor rule, ([J]) J. Process Control 64 (2018) 49–61.
[252]
C. Zhao, Y. Yao, F. Gao, F. Wang, Statistical analysis and online monitoring for multimode processes with between-mode transitions, Chem. Eng. Sci. 65 (22) (2010) 5961–5975.
[253]
Y. Zhao, S. Ye, X. Chen, et al., Polynomial response surface based on basis function selection by multitask optimization and ensemble modeling, Complex Intell. Syst. 8 (2) (2022) 1015–1034.
[254]
Z. Zhao, Z. Bian, J. Liang, S. Liu, M. Zhou, Scheduling and logistics optimization for batch manufacturing processes with temperature constraints and alternative thermal devices, IEEE Trans. Ind. Informat. (2024),.
[255]
Z. Zhao, Q. Jiang, S. Liu, M. Zhou, X. Yang, X. Guo, Energy, cost and job-tardiness-minimized scheduling of energy-intensive and high-cost industrial production systems, Eng. Appl. Artif. Intell. 133 (108477) (2024).
[256]
Z. Zhao, X. Li, S. Liu, M. Zhou, X. Yang, Multi-mobile-robot transport and production integrated system optimization, IEEE Trans. Autom. Sci. Eng. (2024),.
[257]
Z. Zhao, S. Liu, M. Zhou, et al., Dual-objective mixed integer linear program and memetic algorithm for an industrial group scheduling problem, IEEE/CAA J. Autom. Sin. 8 (6) (2021) 1199–1209.
[258]
Z. Zhao, S. Liu, M. Zhou, et al., Decomposition method for new single-machine scheduling problems from steel production systems, IEEE Trans. Autom. Sci. Eng. 17 (3) (2020) 1376–1387.
[259]
Z. Zhao, S. Liu, M. Zhou, et al., Heuristic scheduling of batch production processes based on petri nets and iterated greedy algorithms, IEEE Trans. Autom. Sci. Eng. 19 (1) (2022) 251–261.
[260]
R. Zhong, X. Xu, E. Klotz, et al., Intelligent manufacturing in the context of industry 4.0: a review, Engineering 3 (5) (2017) 616–630.
[261]
D. Zhou, Y. Hu, Fault diagnosis techniques for dynamic systems, Acta Autom. Sin. 35 (6) (2009) 748–758.
[262]
J. Zhou, Y. Chen, W. Wang, S. He, D. Meng, A highly reliable metadata service for large-scale distributed file systems, IEEE Trans. Parallel Distrib. Syst. 31 (2) (2020) 374–392.
[263]
P. Zhou, T. Chai, J. Sun, Intelligence-based supervisory control for optimal operation of a DCS-controlled grinding system, IEEE Trans. Control Syst. Technol. 21 (1) (2013) 162–175.
[264]
X. Zhou, Y. Gao, C. Li, Z. Huang, A multiple gradient descent design for multi-task learning on edge computing: multi-objective machine learning approach, IEEE Trans. Netw. Sci. Eng. 9 (1) (2022) 121–133.
[265]
J. Zhu, Z. Ge, Z. Song, Distributed Gaussian mixture model for monitoring multimode plant-wide process, 2016 Chinese Control and Decision Conference (CCDC), IEEE, 2016, pp. 5826–5831.
[266]
Q. Zhu, S.J. Qin, Supervised diagnosis of quality and process faults with canonical correlation analysis, Ind. Eng. Chem. Res. 58 (26) (2019) 11213–11223.
[267]
X. Zou, C. Zhao, Concurrent assessment of process operating performance with joint static and dynamic analysis, IEEE Trans. Ind. Inform. 16 (4) (2020) 2776–2786.
Index Terms
- Performance-driven closed-loop optimization and control for smart manufacturing processes in the cloud-edge-device collaborative architecture: A review and new perspectives
Index terms have been assigned to the content through auto-classification.
Recommendations
Cloud-based smart manufacturing for personalized candy packing application
Industry 4.0 has been proposed to address personalized consumption demands by building cyber-physical production systems for smart manufacturing. Although cloud manufacturing and some integrated frameworks for smart factory have been presented in ...
Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues
HighlightsThis paper reviews the current status and advancement of Digital Twin-driven smart manufacturing, with highlights on the following aspects:
- Presented the connotation of ...
AbstractThis paper reviews the recent development of Digital Twin technologies in manufacturing systems and processes, to analyze the connotation, application scenarios, and research issues of Digital Twin-driven smart manufacturing in the ...
Smart manufacturing applications for inspection and quality assurance processes
AbstractSmart manufacturing had a high impact in recent years within the inspection and quality assurance processes, providing innovative technologies in machine learning. Consequently, the article presents a systematic review of the applications of ...
Comments
Information & Contributors
Information
Published In
Elsevier B.V.
Publisher
Elsevier Science Publishers B. V.
Netherlands
Publication History
Published: 01 November 2024
Author Tags
Qualifiers
- Review-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 24 Jan 2025