Fault diagnosis on material handling system using feature selection and data mining techniques.
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| Title: | Fault diagnosis on material handling system using feature selection and data mining techniques. |
|---|---|
| Authors: | Demetgul, M.1 mdemetgul@marmara.edu.tr, Yildiz, K.2, Taskin, S.3, Tansel, I. N.4, Yazicioglu, O.5 |
| Source: | Measurement (02632241). Sep2014, Vol. 55, p15-24. 10p. |
| Subjects: | Debugging, Data mining, Feature selection, Manufacturing processes, Performance evaluation, Algorithms |
| Abstract: | The material handling systems are one of the key components of the most modern manufacturing systems. The sensory signals of material handling systems are nonlinear and have unique characteristics. It is very difficult to encode and classify these signals by using multipurpose methods. In this study, performances of multiple generic methods were studied for the diagnostic of the pneumatic systems of the material handling systems. Diffusion Map (DM), Local Linear Embedding (LLE) and AutoEncoder (AE) algorithms were used for future extraction. Encoded signals were classified by using the Gustafson-Kessel (GK) and k-medoids algorithms. The accuracy of the estimations was better than 90% when the LLE was used with GK algorithm. [ABSTRACT FROM AUTHOR] |
| Copyright of Measurement (02632241) is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 97190117 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.measurement.2014.04.037 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 15 Subjects: – SubjectFull: Debugging Type: general – SubjectFull: Data mining Type: general – SubjectFull: Feature selection Type: general – SubjectFull: Manufacturing processes Type: general – SubjectFull: Performance evaluation Type: general – SubjectFull: Algorithms Type: general Titles: – TitleFull: Fault diagnosis on material handling system using feature selection and data mining techniques. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Demetgul, M. – PersonEntity: Name: NameFull: Yildiz, K. – PersonEntity: Name: NameFull: Taskin, S. – PersonEntity: Name: NameFull: Tansel, I. N. – PersonEntity: Name: NameFull: Yazicioglu, O. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2014 Type: published Y: 2014 Identifiers: – Type: issn-print Value: 02632241 Numbering: – Type: volume Value: 55 Titles: – TitleFull: Measurement (02632241) Type: main |
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