Sensor Fault Diagnosis Based on a  Sliding Mode and Unknown Input Observer for Takagi‐Sugeno Systems with Uncertain Premise Variables

Gómez-Peñate, Samuel; Valencia‐Palomo, Guillermo; López‐Estrada, Francisco‐Ronay; Astorga‐Zaragoza, C.M.; Osornio-Ríos, Roque Alfredo; Santos‐Ruiz, Ildeberto

doi:10.1002/asjc.1913

Cited by 36 publications

(27 citation statements)

References 35 publications

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“…A possible technique to deal with uncertainties is by means of the robust H ∞ approach, which has been developed since the beginning of the eighties and has been applied intensely, with successful results, to convex systems [11,120,127,128].…”

Section: Robust Observersmentioning

confidence: 99%

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A Review of Convex Approaches for Control, Observation and Safety of Linear Parameter Varying and Takagi-Sugeno Systems

2019

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This paper provides a review about the concept of convex systems based on Takagi-Sugeno, linear parameter varying (LPV) and quasi-LPV modeling. These paradigms are capable of hiding the nonlinearities by means of an equivalent description which uses a set of linear models interpolated by appropriately defined weighing functions. Convex systems have become very popular since they allow applying extended linear techniques based on linear matrix inequalities (LMIs) to complex nonlinear systems. This survey aims at providing the reader with a significant overview of the existing LMI-based techniques for convex systems in the fields of control, observation and safety. Firstly, a detailed review of stability, feedback, tracking and model predictive control (MPC) convex controllers is considered. Secondly, the problem of state estimation is addressed through the design of proportional, proportional-integral, unknown input and descriptor observers. Finally, safety of convex systems is discussed by describing popular techniques for fault diagnosis and fault tolerant control (FTC).

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Section: Robust Observersmentioning

confidence: 99%

“…On the other hand, other works have considered the case in which the scheduling variables are measured inexactly, see for example, Refs. [128,161,162]. Further improvements have been provided by Ref.…”

Section: Unknown Input Observers-based Fault Isolationmentioning

confidence: 99%

A Review of Convex Approaches for Control, Observation and Safety of Linear Parameter Varying and Takagi-Sugeno Systems

2019

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“…Gong et al [28] proposed an optimal deducing model to locate faulty nodes based on the return message from the sink node. Jiang [29] [33] presented the design of a H ∞ sliding mode and an unknown input observer for Takagi-Sugeno systems to deal with the problem of inexact measurements of the premise variables. In Reference [34], a data-driven system based on PCA was designed to detect and quantify fluid leaks in an experimental pipeline.…”

Section: Introductionmentioning

confidence: 99%

A Novel Low-Complexity Fault Diagnosis Algorithm for Energy Internet in Smart Cities

et al. 2020

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The smart energy system, viewed as an “Energy Internet”, consists of the intelligent integration of decentralized sustainable energy sources, efficient distribution, and optimized power consumption. That implies the fault diagnosis for a smart energy system should be of low complexity. In this paper, we propose a Strong Tracking Unscented Kalman Filter ( S T U K F ) and modified Bayes’ classification-based Modified Three Sigma test ( M T S ), abbreviated as S F B T , for smart energy networks. The theoretical analysis and simulations indicate that S F B T detects faults with a high accuracy and a low complexity of O ( n ) .

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“…The dynamic behavior of rolling bearings tends to be complicated and flexible, and the vibration signal exhibits nonlinear and non-stationary characteristics. Many methods for fault diagnosis were developed, such as model-based methods [1,2], observer-based methods [3,4], and data-driven methods [5][6][7]. Analysis of vibration signal is a key technique for bearing fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for Rolling Element Bearings Based on Feature Space Reconstruction and Multiscale Permutation Entropy

Zhang

Zhou

2019

Entropy

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Aimed at distinguishing different fault categories of severity of rolling bearings, a novel method based on feature space reconstruction and multiscale permutation entropy is proposed in the study. Firstly, the ensemble empirical mode decomposition algorithm (EEMD) was employed to adaptively decompose the vibration signal into multiple intrinsic mode functions (IMFs), and the representative IMFs which contained rich fault information were selected to reconstruct a feature vector space. Secondly, the multiscale permutation entropy (MPE) was used to calculate the complexity of reconstructed feature space. Finally, the value of multiscale permutation entropy was presented to a support vector machine for fault classification. The proposed diagnostic algorithm was applied to three groups of rolling bearing experiments. The experimental results indicate that the proposed method has better classification performance and robustness than other traditional methods.

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Sensor Fault Diagnosis Based on a Sliding Mode and Unknown Input Observer for Takagi‐Sugeno Systems with Uncertain Premise Variables

Cited by 36 publications

References 35 publications

A Review of Convex Approaches for Control, Observation and Safety of Linear Parameter Varying and Takagi-Sugeno Systems

A Review of Convex Approaches for Control, Observation and Safety of Linear Parameter Varying and Takagi-Sugeno Systems

A Novel Low-Complexity Fault Diagnosis Algorithm for Energy Internet in Smart Cities

Fault Diagnosis for Rolling Element Bearings Based on Feature Space Reconstruction and Multiscale Permutation Entropy

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