Decentralized fault detection and isolation using bond graph and PCA methods

  • Maroua Said
  • Radhia Fazai
  • Khaoula Ben Abdellafou
  • Okba TaoualiEmail author


This paper proposes a new method for fault detection and isolation of a large-scale system by the bond graph (BG) model and the principal component analysis (PCA) technique in a decentralized architecture. The proposed method is entitled the BG-PCA technique. The main objective is to address the problem of monitoring large-scale system components by fault detection and isolation (FDI) methods. In the modeling framework, the BG model is presented by exploiting its structural and causal properties and the diagnostic bond graph (DBG). In measurement noise and perturbation conditions, the probability density function takes place to generate a clear decision procedure to detect the operating mode. In our approach, we firstly generate the structured residues. Furthermore, with a decentralized architecture, we can locate in what subsystem a fault is first detected. Finally, for isolation, the generation of the structured residues method from PCA is exploited to ensure localization. To validate the suggested BG-PCA algorithm, simulations are computed on a three-tank system to show the efficiency of the proposed FDI method, and satisfactory results are found.


Bond graph Principal component analysis Fault detection and isolation Diagnostic bond graph Large-scale system 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



  1. 1.
    Leonhardt S, Ayoubi M (1997) Methods of fault diagnosis. Control Eng Pract 5(5):683–692CrossRefGoogle Scholar
  2. 2.
    Staroswiecki M (2000) Quantitative and qualitative models for fault detection and isolation. Control Eng Pract 14(3):301–325Google Scholar
  3. 3.
    Biswas G, Cordier MO, Lunze J, Trave-Massuyes L, Staroswiecki M (2004) Diagnosis of complex systems: Bridging the methodologies of the FDI and DX communities. IEEE Trans Syst Man Cybern B Cybern 34 (5):2159–2162CrossRefGoogle Scholar
  4. 4.
    Zhong M, Zhang L, Ding S, Zhou D (2017) A probabilistic approach to robust fault detection for a class of nonlinear systems. IEEE Trans Ind Electron 64(5):3930–3939CrossRefGoogle Scholar
  5. 5.
    Ferdowsi H, Jagannathan S (2017) Decentralized fault tolerant control of a class of nonlinear interconnected systems. IFAC Proceedings 15(2):527–536Google Scholar
  6. 6.
    Li W, Gui W, Xie Y, Ding S (2009) Decentralized Fault Detection System Design For Large-scale Interconnected System. IFAC Proceedings 42(8):816–821CrossRefGoogle Scholar
  7. 7.
    Boem F, Ferrari RMG, Parisini T (2011) Distributed fault detection and isolation of continuous-time non-linear systems. Eur J Control 17(5-6):603–620MathSciNetCrossRefGoogle Scholar
  8. 8.
    Reppa V, Polycarpou MM, Panayiotou CG (2015) Decentralized isolation of multiple sensor faults in large-scale interconnected nonlinear systems. IEEE Trans Autom Control 60 (6):1582– 1596MathSciNetCrossRefGoogle Scholar
  9. 9.
    Arrichiello F, Marino A, Pierri F (2015) Observer-based decentralized fault detection and isolation strategy for networked multirobot systems. IEEE Trans Control Syst Technol 23 (4):1465– 1476CrossRefGoogle Scholar
  10. 10.
    Odendaal HM, Jones T (2014) Actuator fault detection and isolation: an optimised parity space approach. Control Eng Pract 26:222–232CrossRefGoogle Scholar
  11. 11.
    Taouali O, Elaissi I, Messaoud H (2012) Online identification of nonlinear system using reduced kernel principal component analysis. Neural Comput & Applic 21(1):161–169CrossRefGoogle Scholar
  12. 12.
    Mulumba T, Afshari A, Yan K, Shen W, Norford LK (2015) Robust model-based fault diagnosis for air handling units. Energy Build 86(1):698–707CrossRefGoogle Scholar
  13. 13.
    Ould Bouamama B, Biswas G, Loureiro R, Merzouki R (2014) Graphical methods for diagnosis of dynamic systems. Annu Rev Control 38(2):199–219CrossRefGoogle Scholar
  14. 14.
    Mojallal A, Lotfifard S (2017) Multi-physics graphical model based fault detection and isolation in wind turbines. IEEE transactions on smart gridGoogle Scholar
  15. 15.
    Samantaray AK, Ould Bouamama B (2008) Model-based process supervision: a bond graph approach. Springer Science & Business MediaGoogle Scholar
  16. 16.
    Biswas G, Simon G, Mahadevan N, Narasimhan S, Ramirez J, Karsai G (2003) A robust method for hybrid diagnosis of complex systems. IFAC Proceedings 36(5):1023–1028CrossRefGoogle Scholar
  17. 17.
    Ould Bouamama B, Medjaher K, Samantaray AK, Staroswiecki M (2006) Supervision of an industrial steam generator. Part I: Bond graph modelling. Control Eng Pract 14(1):71–83CrossRefGoogle Scholar
  18. 18.
    Medjaher K, Zerhouni N (2013) Hybrid prognostic method applied to mechatronic systems. Int J Adv Manuf Technol 69(1-4):823–834CrossRefGoogle Scholar
  19. 19.
    Samantaray AK, Medjaher K, Ould Bouamama B, Staroswiecki M, Dauphin-Tanguy G (2006) Diagnostic bond graphs for online fault detection and isolation. Simul Model Pract Theory 14(3):237–262CrossRefGoogle Scholar
  20. 20.
    Gao Z, Cecati C, Ding SX (2015) 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):3757–3767CrossRefGoogle Scholar
  21. 21.
    Jha MS, Dauphin-Tanguy G, Ould Bouamama B (2016) Particle filter based hybrid prognostics for health monitoring of uncertain systems in bond graph framework. Mech Syst Signal Process 75(6):301–329CrossRefGoogle Scholar
  22. 22.
    Yang S, Bian C, Li X, Tan L, Tang D (2018) Optimized fault diagnosis based on FMEA-style CBR and BN for embedded software system. Int J Adv Manuf Technol 94 (9-12):3441– 3453CrossRefGoogle Scholar
  23. 23.
    Zaidi A, Ould Bouamama B, Tagina M (2012) Bayesian reliability models of Weibull systems: State of the art. Int J Appl Math Comput Sci 22(3):585–600MathSciNetCrossRefGoogle Scholar
  24. 24.
    Gertlert J, Li W, Huang Y, McAvor T (1998) Isolation enhanced principal component analysis. IFAC Proceedings 31(10):185–190CrossRefGoogle Scholar
  25. 25.
    Halligan GR, Jagannathan S (2011) PCA-Based fault isolation and prognosis with application to pump. Int J Adv Manuf Technol 55(5-8):699–707CrossRefGoogle Scholar
  26. 26.
    Chaouch H, Najeh T, Nabli L (2017) Multi-variable process data compression and defect isolation using wavelet PCA and genetic algorithm. Int J Adv Manuf Technol 91(1-4):869–878CrossRefGoogle Scholar
  27. 27.
    Marcondes Filho D, SantAnna AMO (2016) Principal component regression-based control charts for monitoring count data. Int J Adv Manuf Technol 85(5-8):1565–1574CrossRefGoogle Scholar
  28. 28.
    Alcala CF, Qin SJ (2010) Reconstruction-based contribution for process monitoring with kernel principal component analysis. Ind Eng Chem Res 49(17):7849–7857CrossRefGoogle Scholar
  29. 29.
    Guo W, Zhao H, Gao X, Kong L, Li Y (2018) An efficient representative for object recognition in structural health monitoring. Int J Adv Manuf Technol 94(9-12):3239–3250CrossRefGoogle Scholar
  30. 30.
    Jaffel I, Taouali O, Harkat MF, Kong L, Messaoud H (2015) Online process monitoring using a new PCMD index. Int J Adv Manuf Technol 80(5-8):947–957CrossRefGoogle Scholar
  31. 31.
    Gertler J, Li W, Huang Y, McAvoy T (1999) Isolation enhanced principal component analysis. AIChE Journal 45(2):323–334CrossRefGoogle Scholar
  32. 32.
    Zhang Z, Wang Y, Wang K (2013) Intelligent fault diagnosis and prognosis approach for rotating machinery integrating wavelet transform, principal component analysis, and artificial neural networks. Int J Adv Manuf Technol 68 (1-4):763– 773CrossRefGoogle Scholar
  33. 33.
    Touati Y, Merzouki R, Ould Bouamama B (2012) Robust diagnosis to measurement uncertainties using bond graph approach: Application to intelligent autonomous vehicle. Mechatronics 22(8):1148–1160CrossRefGoogle Scholar
  34. 34.
    Playtner H (1961) Analysis and design of engineering systems. MIT, CambridgeGoogle Scholar
  35. 35.
    Karnopp DC, Margolis DL, Rosenberg RC (1990) System dynamics: a unified approachGoogle Scholar
  36. 36.
    Breedveld PC (1984) Essential gyrators and equivalence rules for 3-port function structures. J Frankl Inst 318(2):77–89CrossRefGoogle Scholar
  37. 37.
    Bouallegue W, Bouslama S, Tagina M (2017) Robust fault detection and isolation in bond graph modelled processes with Bayesian networks. Int J Comput Appl Technol 55(1):46–54CrossRefGoogle Scholar
  38. 38.
    Dhouibi H, Bochra M, Messaoud H, Simeu-Abazi Z (2014) Diagnosis approach using bond graph and timed automata. MOSIM 2014, 10ème Conférence Francophone de Modélisation, Optimisation et SimulationGoogle Scholar
  39. 39.
    Ould Bouamama B (2003) Bond graph approach as analysis tool in thermofluid model library conception. J Frankl Inst 340(1):1–23MathSciNetCrossRefGoogle Scholar
  40. 40.
    Raich A, Çinar A (1996) Process disturbance diagnosis by statistical distance and angle measures. IFAC Proceedings 29(1):6602–6607CrossRefGoogle Scholar
  41. 41.
    MacGregor JF, Kourti T, Nomikos P (1996) Analysis, monitoring and fault diagnosis of industrial processes using multivariate statistical projection methods. IFAC Proceedings 29(1):5941–5946CrossRefGoogle Scholar
  42. 42.
    Jaffel I, Taouali O, Elaissi I, Messaoud H (2013) A new online fault detection method based on PCA technique. IMA J Math Control Inf 31(4):487–499MathSciNetCrossRefGoogle Scholar
  43. 43.
    Fazai R, Taouali O, Harkat MF, Bouguila N (2016) A new fault detection method for nonlinear process monitoring. Int J Adv Manuf Technol 87(9-12):3425–3436CrossRefGoogle Scholar
  44. 44.
    Harkat MF, Mourot G, Ragot J (2006) An improved PCA scheme for sensor FDI: Application to an air quality monitoring networkg. J Process Control 16(6):625–634CrossRefGoogle Scholar
  45. 45.
    Lahdhiri H, Taouali O, Elaissi I, Jaffel I, Harakat MF, Messaoud H (2017) A new fault detection index based on Mahalanobis distance and kernel method. Int J Adv Manuf Technol 91(5-8):2799–2809CrossRefGoogle Scholar
  46. 46.
    Chatti N, Guyonneau R, Hardouin L, Verron S, Lagrange S (2016) Model-based approach for fault diagnosis using set-membership formulation. Eng Appl Artif Intel 55:307–319sCrossRefGoogle Scholar
  47. 47.
    Fazai R, Mansouri M, Taouali O, Harkat MF, Bouguila N (2018) Online reduced kernel principal component analysis for process monitoring. J Process Control 61:1–11CrossRefGoogle Scholar
  48. 48.
    Yin S, Zhu X (2015) Intelligent particle filter and its application to fault detection of nonlinear syste. IEEE Trans Ind Electron 62(6):3852–3861Google Scholar
  49. 49.
    Harkat MF (2003) Détection et localisation de défauts par analyse en composantes principales. PhD thesis,Institut National Polytechnique de Lorraine-INPLGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Maroua Said
    • 1
  • Radhia Fazai
    • 2
  • Khaoula Ben Abdellafou
    • 3
  • Okba Taouali
    • 2
    Email author
  1. 1.Research Laboratory of Automation, Signal Processing and Image (LARATSI), National School of Engineering SousseUniversity of SousseSousseTunisia
  2. 2.Research Laboratory of Automation, Signal Processing and Image (LARATSI), National School of Engineering MonastirUniversity of MonastirMonastirTunisia
  3. 3.Research Laboratory Mars, ISITComUniversity of SousseSousseTunisia

Personalised recommendations