A Model-Based Fault Detection and Prognostics Scheme for Takagi–Sugeno Fuzzy Systems

Thumati, Balaje T.; Feinstein, Miles A.; Jagannathan, S.

doi:10.1109/tfuzz.2013.2272584

Cited by 40 publications

(30 citation statements)

References 24 publications

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“…Additionally, to show the effectiveness of the weighted H ∞ error performance for the designed reduced-order hybrid switched model, setx(0) = 0 (x(0) = 0,x(0) = 0), and the membership functions is selected as Figure 2 shows that the measured outputs of the original hybrid switched system (30)- (31), the third-dimension hybrid switched model (34), the second-dimension hybrid switched model (33) and the first-dimension hybrid switched model (32), while the output errors between the original nonlinear hybrid switched system and the reduced-order hybrid switched models are described in Figure 3.…”

Section: Illustrative Examplementioning

confidence: 99%

“…Since the nonlinear system can be modelled as a weighted total of combined linear sub-models via T-S fuzzy modelling, researchers have put a great deal of efforts into T-S fuzzy systems and numerous achievements have been obtained in this research field. To mention a few, the receding horizon disturbance attenuation analysis and dynamic decoupling are studied in [2,7], the fuzzy controllers are designed for nonlinear systems in [16,32,34,42], the the fault detection and H ∞ synchronization problem are solved in [17,30], the fuzzy-rule-dependent stability analysis and control problems are studied in [11,12,13,18], and the fuzzy filtering problems with the given pre-specified performance are investigated in [1,44].…”

Section: Introductionmentioning

confidence: 99%

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Reduced-order model approximation of fuzzy switched systems with pre-specified performance

Liu

Song

et al. 2016

Information Sciences

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The reduced-order model approximation problem for discrete-time hybrid switched nonlinear systems is addressed via TakagiSugeno (T-S) fuzzy modelling in this paper. For a high-dimension hybrid switched nonlinear system, our aim is on how to construct a reduced-dimension hybrid switched model, approximating its original high-order model well with a pre-specified system performance level. Firstly, the mean-square exponential stability analysis is provided, in which it guarantees the given weighted H ∞ system performance level for the augmented error dynamic system by the average dwell time analysis approach and the hybrid switched Lyapunov stability theory. The solution of corresponding model reduction problem with pre-specified performance is given by using the projection Lemma, by which the algorithm of the reduced-order hybrid switched model parameters are designed by the cone complementary linearization technique. Finally, the advantage and effectiveness of the proposed reduced-order model approximation approach are shown by the simulation result.

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Section: Illustrative Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reduced-order model approximation of fuzzy switched systems with pre-specified performance

Liu

Song

et al. 2016

Information Sciences

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“…Although various techniques and methods, such as fuzzy [5], neural network [6], statistics, etc., have been utilized for the development of ISHM; it still suffers from problems related to inefficient models, uncertainties and inadequate reasoning as well as being very costly and having time consuming processes. These problems remain to be addressed, mainly because the prognostics of a system relies heavily on the physics of failure models and degradation profiles that are known to be either inaccurate, inconsistent or very noisy.…”

Section: Introductionmentioning

confidence: 99%

Developing Prognostic Models Using Duality Principles for DC-to-DC Converters

Samie

Perinpanayagam

Alghassi

et al. 2015

IEEE Trans. Power Electron.

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Abstract-Within the field of Integrated SystemHealth management, there is still a lack of technological approaches suitable for the creation of adequate prognostic model for large applications whereby a number of similar or even identical subsystems and components are used. Existing similarity among a number of different systems, which are comprised of similar components but with different topologies, can be employed to assign the prognostics of one system to other systems using an inference engine. In the process of developing prognostics, this approach will thereby save resources and time. This paper presents a radically novel approach for building prognostic models based on system similarity in cases where duality principle in electrical systems is utilized. In this regard, unified damage model is created based on standard Tee/Pi models, prognostics model based on transfer functions, and RUL estimator based on how energy relaxation time of system is changed due to degradation. An advantage is; the prognostic model can be generalized such that a new system could be developed on the basis and principles of the prognostic model of other systems. Simple electronic circuits, DC-to-DC converters, are to be used as an experiment to exemplify the potential success of the proposed technique validated with prognostics models from particle filter.

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“…In our previous work [18], model-based fault detection and prognostics scheme was developed for TS fuzzy systems with measured premise variables. As explained earlier, it is very hard to measure premise variables of fuzzy systems [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…As explained in [18], a fault typically affects the system parameter. Evolution of the faulty system parameter is calculated using OLAD of the FD observer.…”

Section: Introductionmentioning

confidence: 99%

A fault prediction scheme for Takagi-Sugeno fuzzy systems with immeasurable premise variables and disturbance

Thumati

Jagannathan

2014

53rd IEEE Conference on Decision and Control

Self Cite

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As explained in the literature, it is very hard to measure premise variables of a Takagi-Sugeno (TS) fuzzy system. Therefore, in this paper, a fault detection and prediction (FDP) scheme is designed for a class of TS fuzzy systems with immeasurable (unknown) premise variables and external disturbances. A fault detection (FD) observer is designed to approximate the system output and the premise variables. Subsequently, a FD residual is generated by comparing the observer output with respect to the system output. The FD residual is evaluated to detect any faults in the system. Further, time-to-failure (TTF) of the TS fuzzy system is obtained by using a mathematical equation. Note the parameter update law and TTF scheme utilize the approximated premise variables since they are not measurable. Stability of the fault detection and TTF prediction results are verified using Lyapunov theory. Finally, a simulation study using a truck-trailer system is presented to verify the theoretical claims.

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A Model-Based Fault Detection and Prognostics Scheme for Takagi–Sugeno Fuzzy Systems

Cited by 40 publications

References 24 publications

Reduced-order model approximation of fuzzy switched systems with pre-specified performance

Reduced-order model approximation of fuzzy switched systems with pre-specified performance

Developing Prognostic Models Using Duality Principles for DC-to-DC Converters

A fault prediction scheme for Takagi-Sugeno fuzzy systems with immeasurable premise variables and disturbance

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