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Sep.  2020

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2020  >  7(5): 1279-1288
Alejandro White, Ali Karimoddini and Mohammad Karimadini, "Resilient Fault Diagnosis Under Imperfect Observations–A Need for Industry 4.0 Era," IEEE/CAA J. Autom. Sinica, vol. 7, no. 5, pp. 1279-1288, Sept. 2020. doi: 10.1109/JAS.2020.1003333
Citation: Alejandro White, Ali Karimoddini and Mohammad Karimadini, "Resilient Fault Diagnosis Under Imperfect Observations–A Need for Industry 4.0 Era," IEEE/CAA J. Autom. Sinica, vol. 7, no. 5, pp. 1279-1288, Sept. 2020. doi: 10.1109/JAS.2020.1003333
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Resilient Fault Diagnosis Under Imperfect Observations–A Need for Industry 4.0 Era

doi: 10.1109/JAS.2020.1003333
Funds:  This work was supported by the National Science Foundation (NSF) (1832110 and 2000320) and Air Force Research Laboratory (AFRL) and Office of the Secretary of Defense (OSD) (FA8750-15-2-0116)
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    Abstract
  • In smart industrial systems, in many cases, a fault can be captured as an event to represent the distinct nature of subsequent changes. Event-based fault diagnosis techniques are capable model-based methods for diagnosing faults from a sequence of observable events executed by the system under diagnosis. Most event-based diagnosis techniques rely on perfect observations of observable events. However, in practice, it is common to miss an observable event due to a problem in sensor-readings or communication/transmission channels. This paper develops a fault diagnosis tool, referred to as diagnoser, which can robustly detect, locate, and isolate occurred faults. The developed diagnoser is resilient against missed observations. A missed observation is detected from its successive sequence of events. Upon detecting a missed observation, the developed diagnoser automatically resets and then, asynchronously resumes the diagnosis process. This is achieved solely based on post-reset/activation observations and without interrupting the performance of the system under diagnosis. New concepts of asynchronous detectability and asynchronous diagnosability are introduced. It is shown that if asynchronous detectability and asynchronous diagnosability hold, the proposed diagnoser is capable of diagnosing occurred faults under imperfect observations. The proposed technique is applied to diagnose faults in a manufacturing process. Illustrative examples are provided to explain the details of the proposed algorithm. The result paves the way towards fostering resilient cyber-physical systems in Industry 4.0 context.

     

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  • [1]
    A. Schumacher, T. Nemeth, and W. Sihn, “Roadmapping towards industrial digitalization based on an industry 4.0 maturity model for manufacturing enterprises,” Procedia CIRP, vol. 79, pp. 409–414, 2019. doi: 10.1016/j.procir.2019.02.110
    [2]
    E. A. Lee, “Cyber physical systems: Design challenges,” in Proc. 11th IEEE Int. Symp. Object and Component-Oriented Real-Time Distributed Computing, Orlando, USA, 2008, pp. 363–369.
    [3]
    R. Baheti and H. Gill, “Cyber-physical systems,” in The Impact of Control Technology, T. Samad and A. M. Annaswamy, Eds. New York, USA: IEEE Control Systems Society, 2011, pp. 161–166.
    [4]
    D. O. M. Sanchez, “Sustainable development challenges and risks of industry 4.0: A literature review,” in Proc. Global IoT Summit, Aarhus, Denmark, 2019, pp. 1–6.
    [5]
    M. M. Alani and M. Alloghani, “Security challenges in the industry 4.0 era,” in Industry 4.0 and Engineering for a Sustainable Future, M. Dastbaz and P. Cochrane, Eds. Cham, Germany: Springer, 2019, pp. 117–136.
    [6]
    V. Alcácer and V. Cruz-Machado, “Scanning the industry 4.0: A literature review on technologies for manufacturing systems,” Eng. Sci. Technol.,Int. J., vol. 22, no. 3, pp. 899–919, Jun. 2019.
    [7]
    S. Jeschke, C. Brecher, T. Meisen, D. Özdemir, and T. Eschert, “Industrial internet of things and cyber manufacturing systems,” in Industrial Internet of Things, S. Jeschke, C. Brecher, H. B. Song, and D. B. Rawat, Eds. Cham, Germany: Springer, 2017, pp. 3–19.
    [8]
    R. M. Murray, K. J. Astrom, S. P. Boyd, R. W. Brockett, and G. Stein, “Future directions in control in an information-rich world,” IEEE Control Syst. Mag., vol. 23, no. 2, pp. 20–33, Apr. 2003. doi: 10.1109/MCS.2003.1188769
    [9]
    A. D. Pouliezos and G. S. Stavrakakis, Real Time Fault Monitoring of Industrial Processes. Dordrecht, Netherlands: Springer, 1994.
    [10]
    P. M. Frank, “Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: A survey and some new results,” Automatica, vol. 26, no. 3, pp. 459–474, May 1990. doi: 10.1016/0005-1098(90)90018-D
    [11]
    A. Diez-Olivan, J. Del Ser, D. Galar, and B. Sierra, “Data fusion and machine learning for industrial prognosis: Trends and perspectives towards industry 4.0,” Inf. Fusion, vol. 50, pp. 92–111, Oct. 2019.
    [12]
    J. Chen and R. J. Patton, Robust Model-Based Fault Diagnosis for Dynamic Systems. Boston, USA: Springer, 1999.
    [13]
    V. Venkatasubramanian, R. Rengaswamy, K. W. Yin, and S. N. Kavuri, “A review of process fault detection and diagnosis: Part I: Quantitative model-based methods,” Comput. Chem. Eng., vol. 27, no. 3, pp. 293–311, Mar. 2003.
    [14]
    A. Bhagwat, R. Srinivasan, and P. R. Krishnaswamy, “Fault detection during process transitions: A model-based approach,” Chem. Eng. Sci., vol. 58, no. 2, pp. 309–325, Jan. 2003. doi: 10.1016/S0009-2509(02)00520-1
    [15]
    R. Isermann, “Model-based fault-detection and diagnosis - status and applications,” Annu. Rev. Control, vol. 29, no. 1, pp. 71–85, 2005. doi: 10.1016/j.arcontrol.2004.12.002
    [16]
    S. Rajakarunakaran, P. Venkumar, D. Devaraj, and K. S. P. Rao, “Artificial neural network approach for fault detection in rotary system,” Appl. Soft Comput., vol. 8, no. 1, pp. 740–748, Jan. 2008. doi: 10.1016/j.asoc.2007.06.002
    [17]
    Y. F. Zhou, J. Hahn, and M. S. Mannan, “Fault detection and classification in chemical processes based on neural networks with feature extraction,” ISA Trans., vol. 42, no. 4, pp. 651–664, Oct. 2003. doi: 10.1016/S0019-0578(07)60013-5
    [18]
    Y. M. Zhang and J. Jiang, “Issues on integration of fault diagnosis and reconfigurable control in active fault-tolerant control systems,” in Fault Detection, Supervision and Safety of Technical Processes 2006, H. Y. Zhang, Ed. Oxford, UK: Elsevier Science Ltd, 2007, pp. 1437–1448.
    [19]
    X. D. Zhang, T. Parisini, and M. M. Polycarpou, “Adaptive fault-tolerant control of nonlinear uncertain systems: An information-based diagnostic approach,” IEEE Trans. Autom. Control, vol. 49, no. 8, pp. 1259–1274, Aug. 2004. doi: 10.1109/TAC.2004.832201
    [20]
    Y. Zheng, H. J. Fang, and H. O. Wang, “Takagi-sugeno fuzzy-model-based fault detection for networked control systems with Markov delays,” IEEE Trans. Syst.,Man,Cybern.,Part B Cybern., vol. 36, no. 4, pp. 924–929, Aug. 2006. doi: 10.1109/TSMCB.2005.861879
    [21]
    P. Y. Zhang, S. Shu, and M. C. Zhou, “An online fault detection model and strategies based on SVM-grid in clouds,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 2, pp. 445–456, Mar. 2018. doi: 10.1109/JAS.2017.7510817
    [22]
    W. S. Lee, D. L. Grosh, F. A. Tillman, and C. H. Lie, “Fault tree analysis, methods, and applications - a review,” IEEE Trans. Reliab., vol. R-34, no. 3, pp. 194–203, Aug. 1985. doi: 10.1109/TR.1985.5222114
    [23]
    J. D. Andrews and S. J. Dunnett, “Event-tree analysis using binary decision diagrams,” IEEE Trans. Reliab., vol. 49, no. 2, pp. 230–238, Jun. 2000. doi: 10.1109/24.877343
    [24]
    D. N. Pandalai and L. E. Holloway, “Template languages for fault monitoring of timed discrete event processes,” IEEE Trans. Autom. Control, vol. 45, no. 5, pp. 868–882, May 2000.
    [25]
    S. R. Das and L. E. Holloway, “Characterizing a confidence space for discrete event timings for fault monitoring using discrete sensing and actuation signals,” IEEE Trans. Syst.,Man,Cybern. - Part A:Syst. Hum., vol. 30, no. 1, pp. 52–66, Jan. 2000. doi: 10.1109/3468.823481
    [26]
    M. Gromov and T. A. C. Willemse, “Testing and model-checking techniques for diagnosis,” in Testing of Software and Communicating Systems, A. Petrenko, M. Veanes, J. Tretmans, and W. Grieskamp, Eds. Berlin, Heidelberg, Germany: Springer, 2007, pp. 138–154.
    [27]
    F. Cicirelli, A. Furfaro, and L. Nigro, “Model checking time-dependent system specifications using time stream petri nets and uppaal,” Appl. Math. Comput., vol. 218, no. 16, pp. 8160–8186, Apr. 2012.
    [28]
    U. Lerner, R. Parr, D. Koller, G. Biswas, “Bayesian fault detection and diagnosis in dynamic systems,” in Proc. 17th Nat. Conf. Artificial Intelligence and Twelfth Conf. Innovative Applications of Artificial Intelligence, Austin, USA, 2000, pp. 531–537.
    [29]
    Z. Simeu-Abazi, M. D. Mascolo, and M. Knotek, “Fault diagnosis for discrete event systems: Modelling and verification,” Reliab. Eng. Syst. Saf., vol. 95, no. 4, pp. 369–378, Apr. 2010.
    [30]
    M. P. Cabasino, A. Giua, and C. Seatzu, “Fault detection for discrete event systems using petri nets with unobservable transitions,” Automatica, vol. 46, no. 9, pp. 1531–1539, Sept. 2010.
    [31]
    R. Ammour, E. Leclercq, E. Sanlaville, and D. Lefebvre, “Datation of faults for markovian stochastic dess,” IEEE Trans. Autom. Control, vol. 64, no. 7, pp. 2961–2967, Jul. 2019.
    [32]
    J. Lunze, “Discrete-event modelling and fault diagnosis of discretely controlled continuous systems,” in Analysis and Design of Hybrid Systems 2006, C. Cassandras, A. Giua, C. Seatzu, and J. Zaytoon, Eds. Amsterdam, Netherlands: Elsevier, 2006, pp. 229–234.
    [33]
    K. Schmidt, “Abstraction-based failure diagnosis for discrete event systems,” Syst. Control Lett., vol. 59, no. 1, pp. 42–47, Jan. 2010. doi: 10.1016/j.sysconle.2009.11.004
    [34]
    G. G. Rigatos, “Fault detection and isolation based on fuzzy automata,” Inf. Sci., vol. 179, no. 12, pp. 1893–1902, May 2009.
    [35]
    P. Philips, K. B. Ramkumar, K. W. Lim, H. A. Preisig, and M. Weiss, “Automaton-based fault detection and isolation,” Comput. Chem. Eng., vol. 23, no. Suppl, pp. S215–S218, Jun. 1999.
    [36]
    G. P. Bhandari and R. Gupta, “Fault diagnosis in service-oriented computing through partially observed stochastic petri nets,” Serv. Oriented Comput. Appl., vol. 14, no. 1, pp. 35–47, Mar. 2020. doi: 10.1007/s11761-019-00279-5
    [37]
    F. Lin, “Diagnosability of discrete event systems and its applications,” Discrete Event Dyn. Syst., vol. 4, no. 2, pp. 197–212, May 1994. doi: 10.1007/BF01441211
    [38]
    N. Ran, H. Y. Su, and S. G. Wang, “An improved approach to test diagnosability of bounded petri nets,” IEEE/CAA J. Autom. Sinica, vol. 4, no. 2, pp. 297–303, Apr. 2017. doi: 10.1109/JAS.2017.7510406
    [39]
    R. Su and W. M. Wonham, “Global and local consistencies in distributed fault diagnosis for discrete-event systems,” IEEE Trans. Autom. Control, vol. 50, no. 12, pp. 1923–1935, Dec. 2005. doi: 10.1109/TAC.2005.860291
    [40]
    C. G. Cassandras and S. Lafortune, Introduction to Discrete Event Systems. 2nd ed. New York, USA: Springer, 2008.
    [41]
    P. J. G. Ramadge and W. M. Wonham, “The control of discrete event systems,” Proc. IEEE, vol. 77, no. 1, pp. 81–98, Jan. 1989. doi: 10.1109/5.21072
    [42]
    P. Baroni, G. Lamperti, P. Pogliano, and M. Zanella, “Diagnosis of a class of distributed discrete-event systems,” IEEE Trans. Syst.,Man,Cybern.-Part A:Syst. Hum., vol. 30, no. 6, pp. 731–752, Nov. 2000.
    [43]
    A. Philippot, M. Sayed-Mouchaweh, and V. Carré-Ménétrier, “Unconditional decentralized structure for the fault diagnosis of discrete event systems,” in IFAC Proc. Volumes, vol. 40, no. 6, pp. 67–72, 2007.
    [44]
    S. Bhattacharyya, R. Kumar, and Z. Huang, “A discrete event systems approach to network fault management: Detection and diagnosis of faults,” Asian J. Control, vol. 13, no. 4, pp. 471–479, Jul. 2011. doi: 10.1002/asjc.349
    [45]
    S. Bhattacharyya, Z. Huang, V. Chandra, and R. Kumar, “A discrete event systems approach to network fault management: Detection & diagnosis of faults,” in Proc. American Control Conf., Boston, USA, 2004, pp. 5108–5113.
    [46]
    M. Agarwal, S. Purwar, S. Biswas, and S. Nandi, “Intrusion detection system for PS-Poll DoS attack in 802.11 networks using real time discrete event system,” IEEE/CAA J. Autom. Sinica, vol. 4, no. 4, pp. 792–808, 2017. doi: 10.1109/JAS.2016.7510178
    [47]
    A. White and A. Karimoddini, “Event-based diagnosis of flight maneuvers of a fixed-wing aircraft,” Reliab. Eng. Syst. Saf., vol. 193, pp. 106609, Jan. 2020. doi: 10.1016/j.ress.2019.106609
    [48]
    M. Sampath, R. Sengupta, S. Lafortune, K. Sinnamohideen, and D. Teneketzis, “Diagnosability of discrete-event systems,” IEEE Trans. Autom. Control, vol. 40, no. 9, pp. 1555–1575, Sept. 1995. doi: 10.1109/9.412626
    [49]
    S. Hashtrudi Zad, R. H. Kwong, and W. M. Wonham, “Fault diagnosis in timed discrete-event systems,” in Proc. 38th IEEE Conf. Decision and Control, Phoenix, USA, 1999, pp. 1756–1761.
    [50]
    M. M. Karimi, A. Karimoddini, A. P. White, and I. W. Bates, “Event-based fault diagnosis for an unknown plant,” in Proc. IEEE 55th Conf. Decision and Control, Las Vegas, USA, 2016, pp. 7216–7221.
    [51]
    A. White and A. Karimoddini, “Asynchronous fault diagnosis of discrete event systems,” in Proc. American Control Conf., Seattle, USA, 2017, pp. 3224–3229.
    [52]
    A. White and A. Karimoddini, “Semi-asynchronous fault diagnosis of discrete event systems,” in Proc. IEEE Int. Conf. Systems, Man, and Cybernetics, Budapest, Hungary, 2016, pp. 3961–3966.
    [53]
    A. White, A. Karimoddini, and R. Su, “Fault diagnosis of discrete event systems under unknown initial conditions,” IEEE Trans. Autom. Control, vol. 64, no. 12, pp. 5246–5252, Dec. 2019. doi: 10.1109/TAC.2019.2912712
    [54]
    R. Fritz and P. Zhang, “Overview of fault-tolerant control methods for discrete event systems,” IFAC-PapersOnLine, vol. 51, no. 24, pp. 88–95, 2018. doi: 10.1016/j.ifacol.2018.09.533
    [55]
    J. Dai, A. Karimoddini, and H. Lin, “Achieving fault-tolerance and safety of discrete-event systems through learning,” in Proc. American Control Conf., Boston, USA, 2016, pp. 4835–4840.
    [56]
    M. Karimadini, A. Karimoddini, and A. Homaifar, “A survey on fault-tolerant supervisory control,” in Proc. IEEE 61st Int. Midwest Symp. Circuits and Systems, Windsor, Canada, 2018, pp. 733–738.
    [57]
    R. Su, “Distributed trace estimation under timing mismatch and channel distortion,” IEEE Trans. Autom. Control, vol. 53, no. 10, pp. 2409–2414, Nov. 2008. doi: 10.1109/TAC.2008.2007532
    [58]
    J. C. Basilio and S. Lafortune, “Robust codiagnosability of discrete event systems,” in Proc. American Control Conf., St. Louis, USA, 2009, pp. 2202–2209.
    [59]
    D. Thorsley, T. S. Yoo, and H. E. Garcia, “Diagnosability of stochastic discrete-event systems under unreliable observations,” in Proc. American Control Conf., Seattle, USA, 2008, pp. 1158–1165.
    [60]
    L. K. Carvalho, J. C. Basilio, and M. V. Moreira, “Robust diagnosis of discrete event systems against intermittent loss of observations,” Automatica, vol. 48, no. 9, pp. 2068–2078, Sept. 2012. doi: 10.1016/j.automatica.2012.06.042
    [61]
    L. K. Carvalho, M. V. Moreira, and J. C. Basilio, “Diagnosability of intermittent sensor faults in discrete event systems,” Automatica, vol. 79, pp. 315–325, May 2017. doi: 10.1016/j.automatica.2017.01.017
    [62]
    S. T. S. Lima, J. C. Basilio, S. Lafortune, and M. V. Moreira, “Robust diagnosis of discrete-event systems subject to permanent sensor failures,” IFAC Proc. Volumes, vol. 43, no. 12, pp. 90–97, 2010. doi: 10.3182/20100830-3-DE-4013.00017
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    Highlights

    • The paper develops a diagnosis tool which can robustly detect, locate, and isolate faults.
    • The developed diagnoser is resilient against missed observations.
    • In case of missed observation, the diagnoser automatically resets and asynchronously resumes the diagnosis process.
    • New concepts of asynchronous detectability and asynchronous diagnosability are introduced.
    • It is formally proved that the proposed diagnoser is capable of diagnosing faults under imperfect observations.

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