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Diagnosing faults and illnesses with a quasi-time-invariant filter transfer function model

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  • Carniel, Roberto

Abstract

The problem of diagnosing a fault or a perturbation in a dynamical system (be it industrial or natural) can be restated as the characterization of the transfer function of a filter that acts on a “sane” or “unperturbed” system to transform it into a “faulted” or “perturbed” one. This filter would then be approximated as time-invariant (and linear) only for short time windows, while the time evolution of such transfer function on longer time scales leads to a simple but powerful general framework that allows to diagnose the onset and/or the time evolution of a fault or perturbation of the system.

Suggested Citation

  • Carniel, Roberto, 2008. "Diagnosing faults and illnesses with a quasi-time-invariant filter transfer function model," Chaos, Solitons & Fractals, Elsevier, vol. 35(1), pages 199-201.
    • Handle: RePEc:eee:chsofr:v:35:y:2008:i:1:p:199-201
    DOI: 10.1016/j.chaos.2006.06.094
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    References listed on IDEAS

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    1. Carniel, Roberto, 2005. "Development of a new diagnostic protocol using a neuro-dynamical tool," Chaos, Solitons & Fractals, Elsevier, vol. 24(1), pages 349-352.
    2. Pascolo, Paolo B. & Marini, Alfio & Carniel, Roberto & Barazza, Fausto, 2005. "Posture as a chaotic system and an application to the Parkinson’s disease," Chaos, Solitons & Fractals, Elsevier, vol. 24(5), pages 1343-1346.
    3. Pascolo, Paolo & Barazza, Fausto & Carniel, Roberto, 2006. "Considerations on the application of the chaos paradigm to describe the postural sway," Chaos, Solitons & Fractals, Elsevier, vol. 27(5), pages 1339-1346.
    4. Carniel, Roberto & Pin, Enrico Del & Budai, Riccardo & Pascolo, Paolo, 2005. "Identifying timescales and possible precursors of the awake to asleep transition in EOG time series," Chaos, Solitons & Fractals, Elsevier, vol. 23(4), pages 1259-1266.
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    Cited by:

    1. Pascolo, P. & Carniel, R. & Grimaz, S., 2009. "Dynamical models of the human eye and strabismus," Chaos, Solitons & Fractals, Elsevier, vol. 41(5), pages 2463-2470.
    2. Pascolo, P. & Carniel, R., 2009. "From time series analysis to a biomechanical multibody model of the human eye," Chaos, Solitons & Fractals, Elsevier, vol. 40(2), pages 966-974.
    3. Luan, Xiaoli & He, Shuping & Liu, Fei, 2009. "Neural network-based robust fault detection for nonlinear jump systems," Chaos, Solitons & Fractals, Elsevier, vol. 42(2), pages 760-766.

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