An improved method of moving wear particle image analysis for wear monitoring.
Saved in:
| Title: | An improved method of moving wear particle image analysis for wear monitoring. |
|---|---|
| Authors: | Liu, Jing1 (AUTHOR) hf2016210482@mail.hfut.edu.cn, Wang, Shuo1 (AUTHOR) shuo.wang@mail.xjtu.edu.cn, Wu, Tonghai1 (AUTHOR) tonghai.wu@mail.xjtu.edu.cn |
| Source: | Industrial Lubrication & Tribology. 2025, Vol. 77 Issue 9, p1607-1617. 11p. |
| Subjects: | Particle analysis, Feature extraction, Image processing, Lubricating oils, Detection algorithms, Engineering systems, Multisensor data fusion |
| Abstract: | Purpose: The purpose of this study is to provide a method for detecting moving wear particles by integrating spatiotemporal features, aiming to achieve fast and accurate localization and feature extraction of particles of various sizes in complex background images. Design/methodology/approach: Moving particle analysis provides comprehensive information for characterizing the wear condition of mechanical systems. However, the detection accuracy of this promising technique is constrained by complex backgrounds. To address this, a two-step detection methodology is proposed by integrating the temporal and spatial features, involving a Gaussian mixture model-based coarse localization module for initial particle contour estimation and a Distance Regularized Level Set Evolution method-based precise detection strategy for boundary refinement. With this model, misdetections resulting from the background complexity can be corrected. Furthermore, morphological and statistical features are precisely extracted by matching and tracking detected multiview particle images across consecutive frames. Findings: For verification, the proposed model is tested on fifteen samples of lubricant oil obtained from simulation experiments and industrial robot reduction gearboxes. The results demonstrate that the proposed method effectively detected moving particles from lubricant oil, with improvements in the accuracy of particle feature extraction from 70.2% to 89.5% compared to traditional methods. Originality/value: Compared to traditional methods that rely solely on temporal or spatial information, the proposed method enhances the accuracy of particle localization and feature extraction. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2025-0022/ [ABSTRACT FROM AUTHOR] |
| Copyright of Industrial Lubrication & Tribology is the property of Emerald Publishing Limited 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: 189060619 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: An improved method of moving wear particle image analysis for wear monitoring. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liu%2C+Jing%22">Liu, Jing</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> hf2016210482@mail.hfut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Shuo%22">Wang, Shuo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> shuo.wang@mail.xjtu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wu%2C+Tonghai%22">Wu, Tonghai</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> tonghai.wu@mail.xjtu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Industrial+Lubrication+%26+Tribology%22">Industrial Lubrication & Tribology</searchLink>. 2025, Vol. 77 Issue 9, p1607-1617. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Particle+analysis%22">Particle analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Lubricating+oils%22">Lubricating oils</searchLink><br /><searchLink fieldCode="DE" term="%22Detection+algorithms%22">Detection algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Engineering+systems%22">Engineering systems</searchLink><br /><searchLink fieldCode="DE" term="%22Multisensor+data+fusion%22">Multisensor data fusion</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Purpose: The purpose of this study is to provide a method for detecting moving wear particles by integrating spatiotemporal features, aiming to achieve fast and accurate localization and feature extraction of particles of various sizes in complex background images. Design/methodology/approach: Moving particle analysis provides comprehensive information for characterizing the wear condition of mechanical systems. However, the detection accuracy of this promising technique is constrained by complex backgrounds. To address this, a two-step detection methodology is proposed by integrating the temporal and spatial features, involving a Gaussian mixture model-based coarse localization module for initial particle contour estimation and a Distance Regularized Level Set Evolution method-based precise detection strategy for boundary refinement. With this model, misdetections resulting from the background complexity can be corrected. Furthermore, morphological and statistical features are precisely extracted by matching and tracking detected multiview particle images across consecutive frames. Findings: For verification, the proposed model is tested on fifteen samples of lubricant oil obtained from simulation experiments and industrial robot reduction gearboxes. The results demonstrate that the proposed method effectively detected moving particles from lubricant oil, with improvements in the accuracy of particle feature extraction from 70.2% to 89.5% compared to traditional methods. Originality/value: Compared to traditional methods that rely solely on temporal or spatial information, the proposed method enhances the accuracy of particle localization and feature extraction. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2025-0022/ [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Industrial Lubrication & Tribology is the property of Emerald Publishing Limited 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=189060619 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1108/ILT-01-2025-0022 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 1607 Subjects: – SubjectFull: Particle analysis Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Image processing Type: general – SubjectFull: Lubricating oils Type: general – SubjectFull: Detection algorithms Type: general – SubjectFull: Engineering systems Type: general – SubjectFull: Multisensor data fusion Type: general Titles: – TitleFull: An improved method of moving wear particle image analysis for wear monitoring. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liu, Jing – PersonEntity: Name: NameFull: Wang, Shuo – PersonEntity: Name: NameFull: Wu, Tonghai IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 00368792 Numbering: – Type: volume Value: 77 – Type: issue Value: 9 Titles: – TitleFull: Industrial Lubrication & Tribology Type: main |
| ResultId | 1 |
