Recurrent Mechanism and Impulse Noise Filter for Establishing ANFIS

Nguyen, Sy Dzung; Choi, Seung–Bok; Seo, Тае-Il

doi:10.1109/tfuzz.2017.2701313

Cited by 33 publications

(22 citation statements)

References 31 publications

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“…In [15], an algorithm named FIN-ANFIS for building ANFIS via a recurrent mechanism and impulse noise filter was presented. Because the FIN-ANFIS will be employed in this paper to identify dynamic response relied on a matrix of distilled features, the filtering function should be hence extricated.…”

Section: Sparse Filteringmentioning

confidence: 99%

“…is kernel function; m > 1 is the fuzzy factor; and ϕ (x j ) − ϕ x 0 i 2 is the squared distance between x j andx 0 i in the kernel space. By choosing Gaussian kernel function and the method of Lagrange multipliers, the optimal centroids (5), optimal membership degree of the j-th data sample belonging to the i-th cluster, µ ij , (6), and the membership ofx il belonging to A k (7) can all be inferred from (4) (see [15,17] for more detail).…”

Section: Structure Of the Anfismentioning

confidence: 99%

“…The more reasonable the structure of the net is, the better the expression of the dynamic relationship of the system that can be obtained is. This increases the accuracy of applications and also increases the convergence rate in the training process [15]. However, there is no explicit and accurate answer for the question about how many layers and how many neurons in one layer should be used for each.…”

Section: Introductionmentioning

confidence: 99%

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A Method of Bearing Fault Diagnosis Using Singular Spectrum Analysis, Sparse Filtering and Anfis

Nguyen¹,

Nguyen

2018

JCC

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Abstract. Bearing is an important machine detail participating in almost all mechanical systems.Estimating online its operating condition to exploit actively the systems, therefore, is one of the most urgent requirements. This paper presents an online bearing damage identifying method named ASBDIM based on ANFIS (Adaptive Neuro-Fuzzy Inference System), Singular Spectrum Analysis (SSA) and sparse filtering. This is an online estimating process operated via two phases, offline and online one. In the offline period, by using SSA and sparse filtering, a database signed Off DaB is built whose inputs are features extracted from the measured data stream typed big data, while its outputs are values encoding the surveyed bearing damage statuses. The ANFIS is then employed to identify the dynamic response of the mechanical system corresponding to the bearing damage statuses reflected by the Off DaB. In the online period, first, at each estimating time, another database called On DaB is established using the way similar to the one used for building the Off DaB. The On DaB participates as inputs of the ANFIS to generate its outputs which are then compared with the corresponding encoded outputs to specify bearing real status at this time. Survey results based on different data sources showed the effectiveness of the proposed method.Keywords. Identifying bearing damage, AI for estimating damage, ANFIS based damage identification, SSA for identifying damage.

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Section: Sparse Filteringmentioning

confidence: 99%

Section: Structure Of the Anfismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Method of Bearing Fault Diagnosis Using Singular Spectrum Analysis, Sparse Filtering and Anfis

Nguyen¹,

Nguyen

2018

JCC

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“…As well known, the mathematical tools FL and ANN possess both the advantages and disadvantages as their specific characteristics. The hybrid structure ANFIS, where ANN and FL can interact to not only overcome partly the limitations of each model but also uphold their strong points , is, hence, considered as a reasonable option in many technology applications such as identifying [1-2, 4, 6, 12], predicting [9,11,17,25], controlling [3,5,7,[18][19][20][21][22][23][24][25][26], and filtering noise [14][15][16][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

ANFIS: Establishing and Applying to Managing Online Damage

Nguyen¹

2020

Fuzzy Logic

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Fuzzy logic (FL) and artificial neural networks (ANNs) own individual advantages and disadvantages. Adaptive neuro-fuzzy inference system (ANFIS), a fuzzy system deployed on the structure of ANN, by which FL and ANN can interact to not only overcome their limitations but also promote the ability of each model has been considered as a reasonable option in the real fields. With the vital strong points, ANFIS has been employed well in many technology applications related to filtering, identifying, predicting, and controlling noise. This chapter, however, focuses mainly on building ANFIS and its application to identifying the online bearing fault. First, a traditional structure of ANFIS as a data-driven model is shown. Then, a recurrent mechanism depicting the relation between the processes of filtering impulse noise (IN) and establishing ANFIS from a noisy measuring database is presented. Finally, one of the typical applications of ANFIS related to online managing bearing fault is shown.

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“…Focusing on this, various studies have been carried out. For example, applying the sliding mode technique [1][2][3][4][5], interpolating control rules of fuzzy logic [6][7][8][9][10][11][12][13], or building observers to compensate for the influence of UAD has obtained positive results [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

A Robust Vibration Control of a Magnetorheological Damper Based Railway Suspension Using a Novel Adaptive Type 2 Fuzzy Sliding Mode Controller

Nguyen

Jung

Choi

2017

Shock and Vibration

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This work proposes a novel adaptive type 2 fuzzy sliding controller (AT2FC) for vibration control of magnetorheological damper-(MRD-) based railway suspensions subjected to uncertainty and disturbance (UAD). The AT2FC is constituted of four main parts. The first one is a sliding mode controller (SMC) for specifying the main damping force supporting the suspension. This controller is designed via Lyapunov stability theory. The second one is an interpolation model based on an interval type 2 fuzzy logic system for determination of optimal parameters of the SMC. The third one is a nonlinear UAD observer to compensate for external disturbances. The fourth one is an inverse MRD model (T2F-I-MRD) for specifying the input current. In the operating process, an adaptively optimal structure deriving from the SMC is created (called the Ad-op-SMC) to adapt to the real status. Working as an actuator, the input current for MRD is then determined by the T2F-I-MRD to generate the required damping force which is estimated by the Ad-op-SMC and the nonlinear observer. It is shown that the obtained survey results reflect the AT2FC's excellent vibration control performance compared with the other controllers.

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Recurrent Mechanism and Impulse Noise Filter for Establishing ANFIS

Cited by 33 publications

References 31 publications

A Method of Bearing Fault Diagnosis Using Singular Spectrum Analysis, Sparse Filtering and Anfis

A Method of Bearing Fault Diagnosis Using Singular Spectrum Analysis, Sparse Filtering and Anfis

ANFIS: Establishing and Applying to Managing Online Damage

A Robust Vibration Control of a Magnetorheological Damper Based Railway Suspension Using a Novel Adaptive Type 2 Fuzzy Sliding Mode Controller

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