Attribute clustering using rough set theory for feature selection in fault severity classification of rotating machinery

Pacheco, Fannia; Cerrada, Mariela; Sánchez, René–Vinicio; Cabrera, Diego; Li, Chuan; Oliveira, José Valente de

doi:10.1016/j.eswa.2016.11.024

Cited by 93 publications

(24 citation statements)

References 44 publications

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“…When dealing with text-related tasks, redundancy information is generally useless and even worse, it might decrease the efficiency of task execution. There exist many representative redundancy information reduction methods such as PCA [36], SVD [37], LSI [38], etc. The principle of PCA is to transform multiple attributes into a few primary attributes, which can reflect the information of original data effectively.…”

Section: Redundancy Information Reductionmentioning

confidence: 99%

A Data Leakage Prevention Method Based on the Reduction of Confidential and Context Terms for Smart Mobile Devices

Tian

Qiu

et al. 2018

Wireless Communications and Mobile Computing

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Early data leakage protection methods for smart mobile devices usually focus on confidential terms and their context, which truly prevent some kinds of data leakage events. However, with the high dimensionality and redundancy of text data, it is difficult to detect the documents which contain confidential contents accurately. Our approach updates cluster graph structure based on CBDLP (Data Leakage Protection Based on Context) model by computing the importance of confidential terms and the terms within the range of their context. By applying CBDLP with pruning procedure which has been validated, we further remove the redundancy terms and noise terms. Actually, not only can confidential terms be accurately detected but also the sophisticated rephrased confidential contents are detected during the experiments.

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Section: Redundancy Information Reductionmentioning

confidence: 99%

A Data Leakage Prevention Method Based on the Reduction of Confidential and Context Terms for Smart Mobile Devices

Tian

Qiu

et al. 2018

Wireless Communications and Mobile Computing

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“…Other clustering methods based on rough sets have been proposed recently by Cai and Verbeek [43] , Hamidzadeh et al [123] , Shi et al [271] and Pacheco et al [217] .…”

Section: Rough Set-based Clusteringmentioning

confidence: 99%

Informational Paradigm, management of uncertainty and theoretical formalisms in the clustering framework: A review

D’Urso

2017

Information Sciences

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Evidential clustering or credal clustering or Belief clustering Credal Sets (CSs) theory, Dempster-2004 Denoeux, Masson [ 98 ] 1967 Shafer (DS) theory 2008 Denoeux, Masson [190] 1976 Dempster, [90] , Shafer [268] 1997 Cozman [68] Credibilistic clustering Credibilistic (Cred) theory 2007 Zhou et al. [ 350 ] 2004 Liu [174] 2014 Niakan et al. [216] 2015 Zhou et al. [348] Type-2 fuzzy clustering Type-2 Fuzzy Sets (T2FSs) theory 2007 Hwang and Rhee [359] (interval Type-2 fuzzy clustering) 1975 Zadeh [353] 2009 Min et al. [198] (interval Type-2 possibilistic clustering) 2012 Linda and Manic [166] (Type-2 fuzzy clustering or general Type-2 fuzzy clustering) 2014 Ji et al. [134] (interval Type-2 fuzzy possibilistic clustering) Neutrosophic clustering Neutrosophic Sets (NSs) theory 2012 Shan et al. [269] 1998 Smarandache [274] 2015 Guo and Ş engür [119] Hesitant fuzzy clustering Hesitant Fuzzy Sets (HFSs) theory 2013 Chen et al. [55] 2009 Torra and Narukawa [290] Interval-based fuzzy clustering Interval Sets (ISs) theory 2015 Silva et al. [273] 1966 Moore [211] 2015 Picture fuzzy clustering 2013 Picture Fuzzy Sets (PFSs) theory Son [277] Cuong and Kreinovich [73]

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“…Most of these algorithms were applied to fault diagnosis of rotating machinery. For instance, Yuwono et al [9] combined particle clustering with a Hidden Markov Model (HMM) for bearing fault diagnosis; Pacheco et al [10] classified gear fault severities using rough set theory. These researches have provided effective clustering applications related to machine fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A Fast Multi-tasking Solution: NMF-Theoretic Co-clustering for Gear Fault Diagnosis under Variable Working Conditions

Shen

Chen

et al. 2020

Chin. J. Mech. Eng.

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Most gear fault diagnosis (GFD) approaches suffer from inefficiency when facing with multiple varying working conditions at the same time. In this paper, a non-negative matrix factorization (NMF)-theoretic co-clustering strategy is proposed specially to classify more than one task at the same time using the high dimension matrix, aiming to offer a fast multi-tasking solution. The short-time Fourier transform (STFT) is first used to obtain the time-frequency features from the gear vibration signal. Then, the optimal clustering numbers are estimated using the Bayesian information criterion (BIC) theory, which possesses the simultaneous assessment capability, compared with traditional validity indexes. Subsequently, the classical/modified NMF-based co-clustering methods are carried out to obtain the classification results in both row and column tasks. Finally, the parameters involved in BIC and NMF algorithms are determined using the gradient ascent (GA) strategy in order to achieve reliable diagnostic results. The Spectra Quest's Drivetrain Dynamics Simulator gear data sets were analyzed to verify the effectiveness of the proposed approach.

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Attribute clustering using rough set theory for feature selection in fault severity classification of rotating machinery

Cited by 93 publications

References 44 publications

A Data Leakage Prevention Method Based on the Reduction of Confidential and Context Terms for Smart Mobile Devices

A Data Leakage Prevention Method Based on the Reduction of Confidential and Context Terms for Smart Mobile Devices

Informational Paradigm, management of uncertainty and theoretical formalisms in the clustering framework: A review

A Fast Multi-tasking Solution: NMF-Theoretic Co-clustering for Gear Fault Diagnosis under Variable Working Conditions

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