A Data-Driven Method for Online Monitoring Tube Wall Thinning Process in Dynamic Noisy Environment

In this paper, an interval type-2 evolving fuzzy Kalman filter is designed for processing of unobservable spectral components of uncertain experimental data. The adopted methodology consider the following steps: an initial model of the interval type-2 fuzzy Kalman filter, which is off-line identified from an initial window of the experimental data; the updating of antecedent proposition of interval type-2 fuzzy Kalman filter by using an interval type-2 formulation of evolving Takagi-Sugeno (eTS) clustering algorithm and the updating of consequent proposition by using a type-2 fuzzy formulation of Observer/Kalman Filter Identification (OKID) algorithm, taking into account the multivariable recursive Singular Spectral Analysis of the experimental data. The computational results for tracking the Mackey-Glass chaotic time series illustrate the efficiency of proposed methodology as compared to relevant approaches from literature, and the experimental results for tracking a 2DoF helicopter demonstrate its applicability.

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A Data-Driven Method for Online Monitoring Tube Wall Thinning Process in Dynamic Noisy Environment

Cited by 3 publications

References 37 publications

Interval type-2 evolving fuzzy Kalman filter for processing of unobservable spectral components from uncertain experimental data

Interval type-2 evolving fuzzy Kalman filter for processing of unobservable spectral components from uncertain experimental data

Capacity Degradation Assessment of Lithium-Ion Battery Considering Coupling Effects of Calendar and Cycling Aging

Type-2 evolving fuzzy Kalman filter design based on unobservable spectral components space for interval tracking of non-stationary experimental data

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