Algorithm for Estimating Online Bearing Fault Upon the Ability to Extract Meaningful Information From Big Data of Intelligent Structures

Tran, Quang Thinh; Nguyen, Sy Dzung; Seo, Тае-Il

doi:10.1109/tie.2018.2847704

Cited by 22 publications

(14 citation statements)

References 32 publications

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“…is specified by Eq. (17). For the defuzzification, if the center-average method is used, the output of the qth data sample is expressed via the membership values in the input fuzzy space of x q as follows:…”

Section: Structure Of Anfismentioning

confidence: 99%

“…Layer Π (product layer) specifies membership values based on Eq. (17). Layer N (normalization) estimates the normalized membership value of a data sample belonging to each fuzzy set upon Eq.…”

Section: Structure Of Anfismentioning

confidence: 99%

“…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%

“…To maintain the accuracy of the inverse MRD model, the ANFIS was retrained after each certain period due to the dynamic response of the MRD depending quite deeply on temperature. Another more active approach is filtering noise or preprocessing data [7,9,11,17,21,[38][39][40].In [11,17],whereANFISswereemployedtopredictthehealthofmechanical systems, vibration signal was always measured and filtered to update the ANFISs. Related to the preprocessing data to set up ANFIS,itcanobservethattomaintainthe stability of the above online ANFIS-based applications, reducing time delay is really meaningful.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Structure Of Anfismentioning

confidence: 99%

Section: Structure Of Anfismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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ANFIS: Establishing and Applying to Managing Online Damage

Nguyen¹

2020

Fuzzy Logic

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“…Ni, Lu, & Lu, 2017;Seuba, Deville, Guizard, & Stevenson, 2016;Sha, Radzieński, Cao, & Ostachowicz, 2019;Wu, Zeng, Huang, & Song, 2017;Yang & Frangopol, 2018;Yazdanipour & Pourgol-Mohammad, 2016;Ye, Hong, Zhang, Zhu, & Fuh, 2018) Logistic regressions (Bissacot et al, 2016;Egnew et al, 2018;Gauthier, Hétu, & Allard, 2015;Jang et al, 2018;Regan, Canturk, Slavkovsky, Niezrecki, & Inalpolat, 2016;Wazen, Fernandes, Aoki, & De Souza, 2013;W. Zhang et al, 2015) Neuro-Fuzzy (Adoko, Gokceoglu, Wu, & Zuo, 2013;Aydin & Kisi, 2015;Guruprasad & Behera, 2015;Hasheminejad, Sohankar, & Hajiannia, 2018;Naderpour & Mirrashid, 2019;Nanda, Das, Das, & Das, 2015;Tran, Nguyen, & Seo, 2019) Conclusions Deep learning methods are fast-evolving. Some of them have advanced to be specialized in a particular application domain.…”

Section: Annmentioning

confidence: 99%

Structural Damage Diagnosis and Prediction Using Machine Learning and Deep Learning Models: Comprehensive Review of Advances<strong> </strong>

Mosavi¹

2019

Preprint

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The loss of integrity and adverse effect on mechanical properties can be concluded as attributing miro/macro-mechanics damage in structures, especially in composite structures. Damage as a progressive degradation of material continuity in engineering predictions for any aspects of initiation and propagation requires to be identified by a trustworthy mechanism to guarantee the safety of structures. Besides the materials design, structural integrity and health are usually prone to be monitored clearly. One of the most powerful methods for the detection of damage is machine learning (ML). This paper presents the state of the art of ML methods and their applications in structural damage and prediction. Popular ML methods are identified and the performance and future trends are discussed.

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