Machine-tool condition monitoring with Gaussian mixture models-based dynamic probabilistic clustering.
Saved in:
| Title: | Machine-tool condition monitoring with Gaussian mixture models-based dynamic probabilistic clustering. |
|---|---|
| Authors: | Diaz-Rozo, Javier1,2 (AUTHOR) jdiaz@ainguraiiot.com, Bielza, Concha2 (AUTHOR), Larrañaga, Pedro2 (AUTHOR) |
| Source: | Engineering Applications of Artificial Intelligence. Mar2020, Vol. 89, pN.PAG-N.PAG. 1p. |
| Subjects: | Gaussian mixture models, Spindles (Machine tools), Monitoring of machinery, Machine learning, Artificial intelligence |
| Abstract: | The combination of artificial intelligence with data, computing power, and new algorithms can provide important tools for solving engineering problems, such as machine-tool condition monitoring. However, many of these problems require algorithms that can perform in highly dynamic scenarios where the data streams have extremely high sampling rates from different types of variables. The unsupervised learning algorithm based on Gaussian mixture models called Gaussian-based dynamic probabilistic clustering (GDPC) is one of these tools. However, this algorithm may have major limitations if a large amount of concept drifts associated with transients occurs within the data stream. GDPC becomes unstable under these conditions, so we propose a new algorithm called GDPC+ to increase its robustness. GDPC+ represents an important improvement because we introduce: (a) automatic selection of the number of mixture components based on the Bayesian information criterion (BIC), and (b) concept drift transition stabilization based on Cauchy–Schwarz divergence integrated with the Dickey–Fuller test. Thus, GDPC+ can perform better in highly dynamic scenarios than GDPC in terms of the number of false positives. The behavior of GDPC+ was investigated using random synthetic data streams and in a real data stream-based condition monitoring obtained from a machine-tool that produces engine crankshafts at high speed. We found that the initial temporal window size can be used to adapt the algorithm to different analytical requirements. The clustering results were also investigated by induction of the rules generated by the repeated incremental pruning to produce error reduction (RIPPER) algorithm in order to provide insights from the underlying monitored process and its associated concept drifts. [ABSTRACT FROM AUTHOR] |
| Copyright of Engineering Applications of Artificial Intelligence is the property of Pergamon Press - An Imprint of Elsevier Science 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 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 141609625 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Machine-tool condition monitoring with Gaussian mixture models-based dynamic probabilistic clustering. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Diaz-Rozo%2C+Javier%22">Diaz-Rozo, Javier</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> jdiaz@ainguraiiot.com</i><br /><searchLink fieldCode="AR" term="%22Bielza%2C+Concha%22">Bielza, Concha</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Larrañaga%2C+Pedro%22">Larrañaga, Pedro</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Engineering+Applications+of+Artificial+Intelligence%22">Engineering Applications of Artificial Intelligence</searchLink>. Mar2020, Vol. 89, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Gaussian+mixture+models%22">Gaussian mixture models</searchLink><br /><searchLink fieldCode="DE" term="%22Spindles+%28Machine+tools%29%22">Spindles (Machine tools)</searchLink><br /><searchLink fieldCode="DE" term="%22Monitoring+of+machinery%22">Monitoring of machinery</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The combination of artificial intelligence with data, computing power, and new algorithms can provide important tools for solving engineering problems, such as machine-tool condition monitoring. However, many of these problems require algorithms that can perform in highly dynamic scenarios where the data streams have extremely high sampling rates from different types of variables. The unsupervised learning algorithm based on Gaussian mixture models called Gaussian-based dynamic probabilistic clustering (GDPC) is one of these tools. However, this algorithm may have major limitations if a large amount of concept drifts associated with transients occurs within the data stream. GDPC becomes unstable under these conditions, so we propose a new algorithm called GDPC+ to increase its robustness. GDPC+ represents an important improvement because we introduce: (a) automatic selection of the number of mixture components based on the Bayesian information criterion (BIC), and (b) concept drift transition stabilization based on Cauchy–Schwarz divergence integrated with the Dickey–Fuller test. Thus, GDPC+ can perform better in highly dynamic scenarios than GDPC in terms of the number of false positives. The behavior of GDPC+ was investigated using random synthetic data streams and in a real data stream-based condition monitoring obtained from a machine-tool that produces engine crankshafts at high speed. We found that the initial temporal window size can be used to adapt the algorithm to different analytical requirements. The clustering results were also investigated by induction of the rules generated by the repeated incremental pruning to produce error reduction (RIPPER) algorithm in order to provide insights from the underlying monitored process and its associated concept drifts. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Engineering Applications of Artificial Intelligence is the property of Pergamon Press - An Imprint of Elsevier Science 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.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=141609625 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.engappai.2019.103434 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Gaussian mixture models Type: general – SubjectFull: Spindles (Machine tools) Type: general – SubjectFull: Monitoring of machinery Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Artificial intelligence Type: general Titles: – TitleFull: Machine-tool condition monitoring with Gaussian mixture models-based dynamic probabilistic clustering. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Diaz-Rozo, Javier – PersonEntity: Name: NameFull: Bielza, Concha – PersonEntity: Name: NameFull: Larrañaga, Pedro IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2020 Type: published Y: 2020 Identifiers: – Type: issn-print Value: 09521976 Numbering: – Type: volume Value: 89 Titles: – TitleFull: Engineering Applications of Artificial Intelligence Type: main |
| ResultId | 1 |
