A New Perspective on Fault Detection and Diagnosis for Plantwide Systems in the Era of Smart Process Manufacturing

Wangyan Li; Jie Bao

doi:10.1016/j.eng.2025.08.006

Engineering ›› 2025, Vol. 52 ›› Issue (9) :19 -24. DOI: 10.1016/j.eng.2025.08.006

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A New Perspective on Fault Detection and Diagnosis for Plantwide Systems in the Era of Smart Process Manufacturing

Wangyan Li ^a^,^b
, Jie Bao ^b^,^*

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Wangyan Li, Jie Bao. A New Perspective on Fault Detection and Diagnosis for Plantwide Systems in the Era of Smart Process Manufacturing. Engineering, 2025, 52(9): 19-24 DOI:10.1016/j.eng.2025.08.006

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References

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[1]	Tippett MJ, Bao J. Distributed model predictive control based on dissipativity. AIChE J 2013; 59(3):787-804.

[2]	Mhaskar P, Liu J, Christofides PD. Fault-tolerant process control: methods and applications. London: Springer-Verlag; 2013.

[3]	Isermann R. Fault-diagnosis applications: model-based condition monitoring: actuators, drives, machinery, plants, sensors, and fault-tolerant systems. Berlin: Springer-Verlag; 2011.

[4]	Li W, Yan Y, Bao J. Dissipativity-based distributed fault diagnosis for plantwide chemical processes. J Process Contr 2020; 96:37-48.

[5]	Lei Q, Wang R, Bao J. Fault diagnosis based on dissipativity property. Comput Chem Eng 2018; 108:360-71.

[6]	Ge Z, Chen J. Plant-wide industrial process monitoring: a distributed modeling framework. IEEE Trans Industr Inform 2016; 12(1):310-21.

[7]	Ding SX. Model-based fault diagnosis techniques: design schemes, algorithms, and tools. Berlin: Springer-Verlag; 2008.

[8]	Shang C, You F. Data analytics and machine learning for smart process manufacturing: recent advances and perspectives in the big data era. Engineering 2019; 5(6):1010-106.

[9]	Venkatasubramanian V, Rengaswamy R, Yin K, Kavuri SN. A review of process fault detection and diagnosis: part I: quantitative model-based methods. Comput Chem Eng 2003; 27(3):293-311.

[10]	Sharmin R, Shah SL, Sundararaj U. A PCA based fault detection scheme for an industrial high pressure polyethylene reactor. Macromol React Eng 2008; 2 (1):12-30.

[11]	Pearson K. On lines and planes of closest fit to systems of points in space. Lond Edinb Dublin Philos Mag J Sci 1901; 2(11):559-72.

[12]	Dunia R, Qin SJ. Joint diagnosis of process and sensor faults using principal component analysis. Control Eng Pract 1998; 6(4):457-69.

[13]	Yao Y, Bao J, Skyllas-Kazacos M, Welch BJ, Akhmetov S. Fault detection and diagnosis in Hall-Héroult cells based on individual anode current measurements using dynamic kernel PCA. Metall Mater Trans B 2018; 49 (4):2077-88.

[14]	Zhang T, Ye H, Wang W, Zhang H. Fault diagnosis for blast furnace ironmaking process based on two-stage principal component analysis. ISIJ Int 2014; 54 (10):2334-41.

[15]	Li W, Zhuo Y, Bao J, Shen Y. A data-based soft-sensor approach to estimating raceway depth in ironmaking blast furnaces. Powder Technol 2021; 390:529-38.

[16]	Mahadevan S, Shah SL. Fault detection and diagnosis in process data using one-class support vector machines. J Process Contr 2009; 19 (10):1627-39.

[17]	Qian J, Song Z, Yao Y, Zhu Z, Zhang X. A review on autoencoder based representation learning for fault detection and diagnosis in industrial processes. Chemom Intell Lab Syst 2022; 231:104711.

[18]	Memarian A, Damarla SK, Memarian A, Huang B. Detection of poor controller tuning with Gramian angular field (GAF) and StackAutoencoder (SAE). Comput Chem Eng 2024; 185:108652.

[19]	Ge Z, Song Z. Distributed PCA model for plant-wide process monitoring. Ind Eng Chem Res 2013; 52(5):1947-57.

[20]	Jiang Q, Huang B. Distributed monitoring for large-scale processes based on multivariate statistical analysis and Bayesian method. J Process Contr 2016; 46:75-83.

[21]	Khatib S, Daoutidis P. Generating optimal overlapping subsystems for distributed statistical fault detection subject to constraints. J Process Contr 2019; 80:143-51.

[22]	Willems JC. Paradigms and puzzles in the theory of dynamical systems. IEEE Trans Automat Contr 1991; 36(3):259-94.

[23]	Maupong TM, Rapisarda P. Data-driven control: a behavioral approach. Syst Control Lett 2017; 101:37-43.

[24]	Willems JC, Trentelman HL. On quadratic differential forms. SIAM J Contr Optim 1998; 36(5):1703-49.

[25]	Li W, Yan Y, Bao J. Data-based fault diagnosis via dissipativity-shaping. IEEE Control Syst Lett 2023; 7:484-9.

[26]	Maupong TM, Mayo-Maldonado JC, Rapisarda P. On Lyapunov functions and data-driven dissipativity. IFAC-PapersOnLine 2017; 50(1):7783-8.

[27]	Romer A, Berberich J, Köhler J, Allgöwer F. One-shot verification of dissipativity properties from input-output data. IEEE Control Syst Lett 2019; 3(3):709-14.

[28]	Wang R, Bao J, Yao Y. A data-centric predictive control approach for nonlinear chemical processes. Chem Eng Res Des 2019; 142:154-64.

[29]	Yan Y, Bao J, Huang B. Distributed data-driven predictive control via dissipative behavior synthesis. IEEE Trans Automat Contr 2024; 69 (5):2899-914.

[30]	Li W, Li S, Song J, Bao J, Yan Y, Yang Y. Fault detection for nonlinear process using data-based dissipativity in a lifted space. In:Proceedings of 2024 14th Asian Control Conference; 2024 Jul 5-8; Dalian, China. Piscataway: IEEE; 2024. p. 666-71.

[31]	Bao J, Zhang WZ, Lee PL. Decentralized fault-tolerant control system design for unstable processes. Chem Eng Sci 2003; 58(22):5045-54.

[32]	Han S, Yan Y, Bao J, Huang B. A big data-driven predictive control approach for nonlinear processes using behaviour clusters. J Process Contr 2024; 140:103252.

[33]	Tippett MJ, Bao J. Control of plant-wide systems using dynamic supply rates. Automatica 2014; 50(1):44-52.

[34]	Olfati-Saber R, Fax JA, Murray RM. Consensus and cooperation in networked multi-agent systems. Proc IEEE 2007; 95(1):215-33.

[35]	Boem F, Ferrari RMG, Keliris C, Parisini T, Polycarpou MM. A distributed networked approach for fault detection of large-scale systems. IEEE Trans Automat Contr 2017; 62(1):18-33.

[36]	Boem F, Ferrari RMG, Parisini T, Polycarpou MM. Distributed fault diagnosis for continuous-time nonlinear systems: the input-output case. Annu Rev Contr 2013; 37(1):163-9.

[37]	Shu Y, Ming L, Cheng F, Zhang Z, Zhao J. Abnormal situation management: challenges and opportunities in the big data era. Comput Chem Eng 2016; 91:104-13.