Distributed Fault Detection and Isolation Approach for Oil Pipelines
Abstract
:1. Introduction
2. Structural Analysis for ModelBased FDI
 Overdetermined subgraph ${G}^{+}$, with a Xcomplete matching that is not $\mathsf{\Sigma}$complete,
 Justdetermined subgraph ${G}^{0}$, with a complete matching,
 Underdetermined subgraph ${G}^{}$, with a $\mathsf{\Sigma}$complete matching that is not Xcomplete.
Structural Diagnosability
3. ModelBased Distributed FDI
Local Diagnosers Design
Algorithm 1: Local Diagnoser Design 
Given:${\mathsf{\Sigma}}_{i}({z}_{i},{x}_{i},{\mathtt{f}}_{i})$, $i=1,\dots ,n$ 

Result: Local optimal FMSO sets for subsystem ${\mathsf{\Sigma}}_{i}$, $i=1,\dots ,n$. 
Algorithm 2: Local diagnoser design with neighbors 
Given:${\mathsf{\Sigma}}_{i}({z}_{i},{x}_{i},{\mathtt{f}}_{i})$, $i=1,\dots ,n$, 

Result: Local optimal FMSO sets for subsystem ${\mathsf{\Sigma}}_{i}$, $i=1,\dots ,n$. 
4. Case Study: FDI System for Oil Pipeline
4.1. Brief Description of the Oil Pipeline
4.2. Mathematical Modeling of Oil Pipeline
4.3. FDI System for Oil Pipeline
5. Results and Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
 Devold, H. Oil and Gas Production Handbook. An Introduction to Oil and Gas Production, Transport, Refining and Petrochemical Industry; ABB Oil and Gas: Oslo, Sweden, 2013. [Google Scholar]
 Kennedy, J. Oil In Addition, Gas Pipeline Fundamentals, 2nd ed.; PennWell Books: Tulsa, Okla, USA, 1993. [Google Scholar]
 Kiefner, J.; Trench, C. Oil Pipeline Characteristics and Risk Factors: Illustrations from the Decade of Construction; American Petroleum Institute: New York, NY, USA, 2001. [Google Scholar]
 Lurie, M. Modeling of Oil Product and Gas Pipeline Transportation; WileyVCH Verlag GmbH & Co KGaA: Weinheim, Germany, 2009. [Google Scholar]
 Yoon, M.S.; Warren, C.B.; Adam, S. Pipeline System Automation and Control; ASME Press: New York, NY, USA, 2007. [Google Scholar]
 Subramanian, N. Improving Security of Oil Pipeline SCADA Systems Using Service Oriented Architectures; Springer: Berlin/Heidelberg, Germany, 2008. [Google Scholar]
 Muhlbauer, W.K. Pipeline Risk Management Manual; Gulf Professional Publishing: Burlington, NJ, USA, 2004. [Google Scholar]
 Gertler, J. Fault Detection and Diagnosis in Engineering Systems, 1st ed.; CRC Press: Boca Raton, FL, USA, 1998. [Google Scholar]
 Korbicz, J.; Koscielny, J.M.; Kowalczuk, Z.; Cholewa, W. Fault Diagnosis: Models, Artificial Intelligence, Applications, 1st ed.; Springer: Berlin/Heidelberg, Germany, 2004. [Google Scholar]
 Scott, S.; Barrufet, M. Worldwide Assessment of Industry Leak Detection Capabilities for Single & Multiphase Pipelines; Technical Report; A&M University: College Station, TX, USA, 2003. [Google Scholar]
 Qin, B.; Yunping, Z.; Min, F.; Xiaojian, S. Leakage detection technology of oil and gas transmission pipelines and its development trend. Petrol. Eng. Construct. 2007, 33, 19–23. [Google Scholar]
 Kowalczuk, Z.; Gunawickrama, K. Detecting and Locating Leaks in Transmission Pipelines; Springer: Berlin/Heidelberg, Germany, 2004. [Google Scholar]
 Zhang, J. Designing a costeffective and reliable pipeline leakdetection system. Pipes Pipelines Int. 1997, 42, 20–26. [Google Scholar]
 Wang, X.; Lambert, M.; Simpson, A.; Liggett, J.; Vítkovsky, J. Leak detection in pipelines using the damping of fluid transients. J. Hydraul. Eng. 2002, 128, 697–711. [Google Scholar] [CrossRef] [Green Version]
 Vásquez, J.W.; PérezZuñiga, G.; SotomayorMoriano, J.; Ospino, A. SuperAlarms with Diagnosis Proficiency Used as an Additional Layer of Protection Applied to an Oil Transport System. Entropy 2021, 23, 139. [Google Scholar] [CrossRef]
 Khorasgani, H.; Jung, D.; Biswas, G. Structural approach for distributed fault detection and isolation. IFACPapersOnLine 2015, 48, 72–77. [Google Scholar] [CrossRef]
 PérezZuñiga, C.; Chanthery, E.; TravéMassuyès, L.; Sotomayor, J. Faultdriven structural diagnosis approach in a distributed context. IFACPapersOnLine 2017, 50, 14254–14259. [Google Scholar] [CrossRef]
 Adegboye, M.A.; Fung, W.K.; Karnik, A. Recent advances in pipeline monitoring and oil leakage detection technologies: Principles and approaches. Sensors 2019, 19, 2548. [Google Scholar] [CrossRef] [Green Version]
 Datta, S.; Sarkar, S. A review on different pipeline fault detection methods. Loss. Prev. Process Ind. 2016, 41, 97–106. [Google Scholar] [CrossRef]
 Vásquez Capacho, J.W.; Perez Zuñiga, C.G.; Muñoz Maldonado, Y.A.; Ospino Castro, A. Simultaneous occurrences and falsepositives analysis in discrete event dynamic systems. J. Comput. Sci. 2020, 44, 101162. [Google Scholar] [CrossRef]
 Abhulimen, K.E.; Susu, A.A. Liquid pipeline leak detection system: Model development and numerical simulation. Chem. Eng. J. 2004, 97, 47–67. [Google Scholar] [CrossRef]
 EncisoSalas, L.; PérezZuñiga, G.; SotomayorMoriano, J. Fault diagnosis via neural ordinary differential equations. Appl. Sci. 2021, 11, 3776. [Google Scholar] [CrossRef]
 Blanke, M.; Kinnaert, M.; Lunze, J.; Staroswieckim, M. Diagnosis and FaultTolerant Control, 3rd ed.; Springer: Berlin/Heidelberg, Germany, 2016. [Google Scholar]
 Isermann, R. FaultDiagnosis Applications; Springer: Berlin/Heidelberg, Germany, 2011. [Google Scholar]
 Noursadeghi, E.; Raptis, I.A. Reducedorder distributed fault diagnosis for largescale nonlinear stochastic systems. J. Dyn. Syst. Meas. Control Trans. 2017, 140, 051009. [Google Scholar] [CrossRef]
 Bregon, A.; Daigle, M.; Roychoudhury, I.; Biswas, G.; Koutsoukos, X.; Pulido, B. An eventbased distributed diagnosis framework using structural model decomposition. Artif. Intell. 2014, 210, 1–35. [Google Scholar] [CrossRef]
 PérezZuniga, C.; Chantery, E.; TraveMassuyes, L.; Sotomayor, J.; Artigues, C. Decentralized diagnosis via structural analysis and integer programming. IFACPapersOnline 2018, 51, 168–175. [Google Scholar] [CrossRef]
 Verde, C.; Torres, L. Modeling and Monitoring of Pipelines and Networks; Springer: Berlin/Heidelberg, Germany, 2017. [Google Scholar]
 PérezZuniga, C.; SotomayorMoriano, J.; Chanthery, E.; TravéMassuyès, L.; Soto, M. Flotation process fault diagnosis via structural analysis. IFACPapersOnline 2019, 52, 225–230. [Google Scholar] [CrossRef]
 Saeed, N.; Abbod, M. Modelling oil pipelines grid: Neurofuzzy supervision system. Int. J. Intell. Syst. Appl. 2017, 9, 1–11. [Google Scholar] [CrossRef] [Green Version]
 Lauricella, M.; Farina, M.; Schneider, R.; Scattolini, R. A distributed fault detection and isolation algorithm based on moving horizon estimation. Proc. IFAC World Congr. 2017, 50, 15259–15264. [Google Scholar] [CrossRef]
 Lu, H.; Iseley, T.; Behbahani, S.; Fu, L. Leakage detection techniques for oil and gas pipelines: Stateoftheart. Tunn. Undergr. Space Technol. 2020, 98, 103249. [Google Scholar] [CrossRef]
 Baroudi, U.; AlRoubaiey, A.A.; Devendiran, A. Pipeline leak detection systems and data fusion: A survey. IEEE Access 2019, 7, 97426–97439. [Google Scholar] [CrossRef]
 Zhang, Y.; Chen, S.; Li, J.; Jin, S. Leak detection monitoring system of long distance oil pipeline based on dynamic pressure transmitter. Measurement 2014, 49, 382–389. [Google Scholar] [CrossRef]
 Wang, Z.; Wang, H.; Fu, L.; Mu, S.; Wang, L. Pipeline detection method based on multiplepressure sensor and negative pressure wave. Trans. Microsyst. Technol. 2015, 34, 115–118. [Google Scholar]
 Kotani, M.; Katsura, M.; Ozawa, S. Detection of gas leakage sound using modular neural networks for unknown environments. Neurocomputing 2004, 62, 427–440. [Google Scholar] [CrossRef]
 PérezZuñiga, G.; RivasPerez, R.; SotomayorMoriano, J.; SánchezZurita, V. Fault detection and isolation system based on structural analysis of an industrial seawater reverse osmosis desalination plant. Processes 2020, 8, 1100. [Google Scholar] [CrossRef]
 Krysander, M.; Åslund, J.; Nyberg, M. An efficient algorithm for finding minimal overconstrained subsystems for modelbased diagnosis. IEEE Trans. Syst. Man Cybem. Part A Syst. Hum. 2008, 38, 197–206. [Google Scholar] [CrossRef]
 PérezZuñiga, G.; Chanthery, E.; TravéMassuyès, L.; Sotomayor, J. Faultdriven minimal structurally overdetermined set in a distributed context. In Proceedings of the 27th International Workshop on Principles of Diagnosis: DX2016, Denver, CO, USA, 4–7 October 2016. [Google Scholar]
 RivasPerez, R.; SotomayorMoriano, J.; PerezZuñiga, C.G. Adaptive expert generalized predictive multivariable control of seawater RO desalination plant for a mineral processing facility. IFACPapersOnline 2017, 50, 10244–10249. [Google Scholar] [CrossRef]
 Sanni, S.E.; Olawale, A.S.; Adefila, S.S. Modeling of sand and crude oil flow in horizontal pipes during crude oil transportation. J. Eng. 2015, 2015, 457860. [Google Scholar] [CrossRef] [Green Version]
 CalderonValdez, S.N.; FeliuBatlle, V.; RivasPerez, R. Fractionalorder mathematical model of an irrigation main canal pool. Span. J. Agric. Res. 2015, 13, e0212. [Google Scholar] [CrossRef] [Green Version]
 Abhulimen, K.E.; Susu, A.A. Modelling complex pipeline network leak detection systems. Process Saf. Environ. Prot. 2007, 85, 579–598. [Google Scholar] [CrossRef]
 RivasPerez, R.; SotomayorMoriano, J.; PérezZuñiga, G.; SotoAngles, M. Realtime implementation of an expert model predictive controller in a pilotscale reverse osmosis plant for brackish and seawater desalination. Appl. Sci. 2019, 9, 2932. [Google Scholar] [CrossRef] [Green Version]
 Frank, W.M. Fluid Mechanics, 7th ed.; McGrawHill: New York, NY, USA, 2011. [Google Scholar]
 Sukarno, P.; Sidarto, K.A.; Trisnobudi, A.; Setyoadi, D. Leak detection modeling and simulation for oil pipeline with artificial intelligence method. ITB J. Eng. Sci. 2007, 39, 1–19. [Google Scholar] [CrossRef] [Green Version]
 HerránGonzalez, A.; De La Cruz, J.M.; De AndrésToro, B.; RiscoMartín, J.L. Modeling and simulation of a gas distribution pipeline network. Appl. Math. Model. 2009, 33, 1584–1600. [Google Scholar] [CrossRef]
 Bogdevičius, M.; Janutėnienė, J.; Jonikas, K.; Guseinovienė, E.; Drakšas, M. Mathematical modeling of oil transportation by pipelines using antiturbulent additives. J. Vibroeng. 2013, 15, 419–427. [Google Scholar]
No.  L, km  D, mm  $\mathit{\delta}$, mm  ${\mathit{z}}_{0}$, m  ${\mathit{z}}_{\mathit{L}}$, m 

1.  150  720  8  50  60 
2.  180  720  8  60  70 
3.  120  720  8  70  180 
No.  Pump Type  QH Equation  P.s.h., m 

1.  II 2500230  $\Delta H=2510.812\xb7{10}^{5}{Q}^{2}$  40 
2.  II 3600230  $\Delta H=2850.640\xb7{10}^{5}{Q}^{2}$  40 
3.  II 5000210  $\Delta H=2360.480\xb7{10}^{5}{Q}^{2}$  40 
Selection for  ${\mathit{\phi}}_{\mathit{i}}$  Equations of ${\mathit{\phi}}_{\mathit{i}}$  Required Subsystems  Fault Sensibility  

${\mathsf{\Sigma}}_{\mathbf{1}}$  ${\mathsf{\Sigma}}_{\mathbf{2}}$  ${\mathsf{\Sigma}}_{\mathbf{3}}$  ${\mathsf{\Sigma}}_{\mathbf{4}}$  
${\mathbf{f}}_{{\mathbf{Q}}_{\mathbf{1}}}$  ${\mathbf{f}}_{\mathbf{1}}$  ${\mathbf{f}}_{{\mathbf{h}}_{\mathbf{2}}}$  ${\mathbf{f}}_{\mathbf{2}}$  ${\mathbf{f}}_{{\mathbf{h}}_{\mathbf{3}}}$  ${\mathbf{f}}_{\mathbf{3}}$,${\mathbf{f}}_{{\mathbf{Q}}_{\mathbf{L}}}$  ${\mathbf{f}}_{{\mathbf{h}}_{\mathbf{L}}}$  
${\mathsf{\Sigma}}_{1}$  ${\phi}_{1}$  $\{{e}_{1},{e}_{2},{e}_{3},{e}_{5},{e}_{9},{e}_{10},{e}_{11}\}$  ${\mathsf{\Sigma}}_{1}$  ${\mathsf{\Sigma}}_{2}^{1,1}$  0  1  1  0  0  0  0  
${\phi}_{2}$  $\{{e}_{1},{e}_{2},{e}_{3},{e}_{4},{e}_{5},{e}_{11}\}$  ${\mathsf{\Sigma}}_{1}$  ${\mathsf{\Sigma}}_{2}^{1,1}$  1  0  1  0  0  0  0  
${\mathsf{\Sigma}}_{2}$  ${\phi}_{1}$  $\{{e}_{1},{e}_{2},{e}_{3},{e}_{5},{e}_{9},{e}_{10},{e}_{11}\}$  ${\mathsf{\Sigma}}_{1}^{1,2}$  ${\mathsf{\Sigma}}_{2}$  0  1  1  0  0  0  0  
${\phi}_{2}$  $\{{e}_{1},{e}_{2},{e}_{3},{e}_{4},{e}_{5},{e}_{11}\}$  ${\mathsf{\Sigma}}_{1}^{1,2}$  ${\mathsf{\Sigma}}_{2}$  1  0  1  0  0  0  0  
${\mathsf{\Sigma}}_{3}$  ${\phi}_{1}$  $\{{e}_{12},{e}_{13},{e}_{14},{e}_{16},{e}_{17},{e}_{20}\}$  ${\mathsf{\Sigma}}_{3}$  ${\mathsf{\Sigma}}_{4}^{1,3}$  0  0  0  0  1  0  1  
${\phi}_{2}$  $\{{e}_{10},{e}_{15},{e}_{16}\}$  ${\mathsf{\Sigma}}_{2}^{1,3}$  ${\mathsf{\Sigma}}_{3}$  0  0  0  1  0  0  0  
${\phi}_{3}$  $\{{e}_{6},{e}_{7},{e}_{8},{e}_{11},{e}_{15},{e}_{16},{e}_{17}\}$  ${\mathsf{\Sigma}}_{2}^{1,3}$  ${\mathsf{\Sigma}}_{3}$  0  0  1  1  1  0  0  
${\mathsf{\Sigma}}_{4}$  ${\phi}_{1}$  $\{{e}_{12},{e}_{13},{e}_{14},{e}_{17},{e}_{18},{e}_{19},{e}_{20}\}$  ${\mathsf{\Sigma}}_{3}^{1,4}$  ${\mathsf{\Sigma}}_{4}$  0  0  0  0  1  1  1  
${\phi}_{2}$  $\{{e}_{12},{e}_{13},{e}_{14},{e}_{16},{e}_{17},{e}_{20}\}$  ${\mathsf{\Sigma}}_{3}^{1,4}$  ${\mathsf{\Sigma}}_{4}$  0  0  0  0  1  0  1  
${\phi}_{3}$  $\{{e}_{6},{e}_{7},{e}_{8},{e}_{11},{e}_{12},{e}_{13},{e}_{14},{e}_{15},{e}_{18},{e}_{19},{e}_{20}\}$  ${\mathsf{\Sigma}}_{2}^{2,4}$  ${\mathsf{\Sigma}}_{4}$  0  0  1  1  0  1  1 
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PérezZuñiga, G.; SotomayorMoriano, J.; RivasPerez, R.; SanchezZurita, V. Distributed Fault Detection and Isolation Approach for Oil Pipelines. Appl. Sci. 2021, 11, 11993. https://doi.org/10.3390/app112411993
PérezZuñiga G, SotomayorMoriano J, RivasPerez R, SanchezZurita V. Distributed Fault Detection and Isolation Approach for Oil Pipelines. Applied Sciences. 2021; 11(24):11993. https://doi.org/10.3390/app112411993
Chicago/Turabian StylePérezZuñiga, Gustavo, Javier SotomayorMoriano, Raul RivasPerez, and Victor SanchezZurita. 2021. "Distributed Fault Detection and Isolation Approach for Oil Pipelines" Applied Sciences 11, no. 24: 11993. https://doi.org/10.3390/app112411993