Data-driven prognostics of rolling element bearings using a novel Error Based Evolving Takagi–Sugeno Fuzzy Model

Camargos, Murilo Osorio; Bessa, Iury; D’Angelo, Marcos Flávio Silveira Vasconcelos; Cosme, Luciana Balieiro; Palhares, Reinaldo M.

doi:10.1016/j.asoc.2020.106628

Cited by 24 publications

(17 citation statements)

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“…To demonstrate the basic architecture of the adaptive neuro-fuzzy model, it is considered to be a kind of first order Takagi-Sugeno fuzzy inference system [27][28][29]. Assume that there are two linguistic variables input x 1 and x 2 with output, and assume that the rule base contains two types of rules:…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

Reliability Modeling Using an Adaptive Neuro-Fuzzy Inference System: Gas Turbine Application

Hadroug

Hafaifa

Iratni

et al. 2021

Fuzzy Information and Engineering

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Recently, the development of the industry requires monitoring and follow-up of the working conditions of the facilities, to determine the reliability, availability, and durability of these systems, for objectively estimating the service life of these installations with reduced maintenance costs. In this sense, this work proposes a novel approach to reliability modeling, to determine failure assessment indicators based on an adaptive neuro-fuzzy inference system applied on a gas turbine. This is in order to describe the behavior of this rotating machine and to estimate their operating safety parameters, to improve its performance in terms of maintainability, availability, and operational safety with effective durability. The application of fuzzy rules to reliability estimation with practical implementations is innovative, making it possible to provide solutions to problems of reliable identification of gas turbines in their complex operating environments.

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Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

Reliability Modeling Using an Adaptive Neuro-Fuzzy Inference System: Gas Turbine Application

Hadroug

Hafaifa

Iratni

et al. 2021

Fuzzy Information and Engineering

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“…This data-driven fuzzy modeling is parameterized considering a data stream from scratch and the learning fuzzy controller is updated online, whenever the local fuzzy rules change, solving an LMI problem. Indeed, evolving fuzzy models are applied to a broad class of problems such as fault diagnosis and prognostics, 28 forecasting, 29 classification, 30 clustering, 31,32 and identification, 33 due to the flexibility and adaptability of the fuzzy rule base in terms of their parameters and semantics, which are useful properties to deal with (nonlinear) time-varying dynamics in data-stream-driven approaches.…”

Section: Introductionmentioning

confidence: 99%

Learning event‐triggered control based on evolving data‐driven fuzzy granular models

Cordovil

Coutinho

Bessa

et al. 2022

Intl J Robust & Nonlinear

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This article proposes a data-stream-driven event-triggered control strategy using evolving fuzzy models learned by granulation of input-output samples of nonlinear systems with unknown time-varying dynamics. The evolving fuzzy model is obtained online from a data stream ensuring data coverage based on the principle of justifiable granularity and controlled by an event-triggering learning mechanism dependent on the model accuracy. This evolving fuzzy model is used to design event-triggered fuzzy controller to stabilize networked control systems while reducing the used communication resources. The event-triggered learning mechanism is employed to determine the instants in which the event-triggered fuzzy controller should be redesigned. Numerical examples illustrate the effectiveness of the proposed learning event-triggered fuzzy control algorithm.

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“…Evolving fuzzy systems (eFS) [18] are universal approximators whose parameters and rule-based structure are updated from never-ending data streams, potentially subject to changes. eFS have been effectively employed in systems identification [5], filtering [22], prediction [8] [16], missing data handling [11], classification [2] [25], image recognition [12], fault detection [14] [19], fault prognostics [6] [7], and robust control [17] [23], to mention some.…”

Section: Introduction 1contextualizationmentioning

confidence: 99%

Incremental Learning and State-Space Evolving Fuzzy Control of Nonlinear Time-Varying Systems with Unknown Model

Leite¹,

Coutinho²,

Bessa³

et al. 2021

Preprint

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We present a method for incremental modeling and time-varying control of unknown nonlinear systems. The method combines elements of evolving intelligence, granular machine learning, and multi-variable control. We propose a State-Space Fuzzy-set-Based evolving Modeling (SS-FBeM) approach. The resulting fuzzy model is structurally and parametrically developed from a data stream with focus on memory and data coverage. The fuzzy controller also evolves, based on the data instances and fuzzy model parameters. Its local gains are redesigned in real-time -whenever the corresponding local fuzzy models change -from the solution of a linear matrix inequality problem derived from a fuzzy Lyapunov function and bounded input conditions. We have shown one-step prediction and asymptotic stabilization of the Henon chaos.

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Data-driven prognostics of rolling element bearings using a novel Error Based Evolving Takagi–Sugeno Fuzzy Model

Cited by 24 publications

References 50 publications

Reliability Modeling Using an Adaptive Neuro-Fuzzy Inference System: Gas Turbine Application

Reliability Modeling Using an Adaptive Neuro-Fuzzy Inference System: Gas Turbine Application

Learning event‐triggered control based on evolving data‐driven fuzzy granular models

Incremental Learning and State-Space Evolving Fuzzy Control of Nonlinear Time-Varying Systems with Unknown Model

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