An experiment on wear particle's texture analysis and identification by using deterministic tourist walk algorithm
Purpose - This study aims to use a deterministic tourist walk to build a system that can identify wear particles. Wear particles provide detailed information about the wear processes taking place between mechanical components. Identification of the type of wear particles by image processing and patt...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Hong Liu [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Industrial lubrication and tribology - Droitwich, Worcs. : Peterson, 1967, 67(2015), 6, Seite 582 |
|---|---|
| Übergeordnetes Werk: |
volume:67 ; year:2015 ; number:6 ; pages:582 |
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| 520 | |a Purpose - This study aims to use a deterministic tourist walk to build a system that can identify wear particles. Wear particles provide detailed information about the wear processes taking place between mechanical components. Identification of the type of wear particles by image processing and pattern recognition is key to effective online monitoring algorithm. There are three kinds of particles that are particularly difficult to distinguish: severe sliding wear particles, fatigue spall particles and laminar particles. Design/methodology/approach - In this study, an identification method is tested using the deterministic tourist walking (DTW) method. This study examined whether this algorithm can be used in particle identification. If it does, can it outperform the traditional texture analysis methods such as Discrete wavelet transform or co-occurrence matrix. Different parameters such as walk's memory size, size of image samples, different inputting vectors and different classifiers were compared. Findings - The DTW algorithm showed promising result compared to traditional texture extraction methods: discrete wavelet transform and co-occurrence matrix. The DTW method offers a higher identification accuracy and a simple feature vector. A conclusion can be drawn that the DTW method is suited for particle identification and can be put into practical use in condition monitoring systems. Originality/value - This paper combined DTW algorithm with wear particle identification problem. | ||
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An experiment on wear particle's texture analysis and identification by using deterministic tourist walk algorithm |
| abstract |
Purpose - This study aims to use a deterministic tourist walk to build a system that can identify wear particles. Wear particles provide detailed information about the wear processes taking place between mechanical components. Identification of the type of wear particles by image processing and pattern recognition is key to effective online monitoring algorithm. There are three kinds of particles that are particularly difficult to distinguish: severe sliding wear particles, fatigue spall particles and laminar particles. Design/methodology/approach - In this study, an identification method is tested using the deterministic tourist walking (DTW) method. This study examined whether this algorithm can be used in particle identification. If it does, can it outperform the traditional texture analysis methods such as Discrete wavelet transform or co-occurrence matrix. Different parameters such as walk's memory size, size of image samples, different inputting vectors and different classifiers were compared. Findings - The DTW algorithm showed promising result compared to traditional texture extraction methods: discrete wavelet transform and co-occurrence matrix. The DTW method offers a higher identification accuracy and a simple feature vector. A conclusion can be drawn that the DTW method is suited for particle identification and can be put into practical use in condition monitoring systems. Originality/value - This paper combined DTW algorithm with wear particle identification problem. |
| abstractGer |
Purpose - This study aims to use a deterministic tourist walk to build a system that can identify wear particles. Wear particles provide detailed information about the wear processes taking place between mechanical components. Identification of the type of wear particles by image processing and pattern recognition is key to effective online monitoring algorithm. There are three kinds of particles that are particularly difficult to distinguish: severe sliding wear particles, fatigue spall particles and laminar particles. Design/methodology/approach - In this study, an identification method is tested using the deterministic tourist walking (DTW) method. This study examined whether this algorithm can be used in particle identification. If it does, can it outperform the traditional texture analysis methods such as Discrete wavelet transform or co-occurrence matrix. Different parameters such as walk's memory size, size of image samples, different inputting vectors and different classifiers were compared. Findings - The DTW algorithm showed promising result compared to traditional texture extraction methods: discrete wavelet transform and co-occurrence matrix. The DTW method offers a higher identification accuracy and a simple feature vector. A conclusion can be drawn that the DTW method is suited for particle identification and can be put into practical use in condition monitoring systems. Originality/value - This paper combined DTW algorithm with wear particle identification problem. |
| abstract_unstemmed |
Purpose - This study aims to use a deterministic tourist walk to build a system that can identify wear particles. Wear particles provide detailed information about the wear processes taking place between mechanical components. Identification of the type of wear particles by image processing and pattern recognition is key to effective online monitoring algorithm. There are three kinds of particles that are particularly difficult to distinguish: severe sliding wear particles, fatigue spall particles and laminar particles. Design/methodology/approach - In this study, an identification method is tested using the deterministic tourist walking (DTW) method. This study examined whether this algorithm can be used in particle identification. If it does, can it outperform the traditional texture analysis methods such as Discrete wavelet transform or co-occurrence matrix. Different parameters such as walk's memory size, size of image samples, different inputting vectors and different classifiers were compared. Findings - The DTW algorithm showed promising result compared to traditional texture extraction methods: discrete wavelet transform and co-occurrence matrix. The DTW method offers a higher identification accuracy and a simple feature vector. A conclusion can be drawn that the DTW method is suited for particle identification and can be put into practical use in condition monitoring systems. Originality/value - This paper combined DTW algorithm with wear particle identification problem. |
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GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-CHE SSG-OLC-PHA GBV_ILN_30 GBV_ILN_70 GBV_ILN_2006 |
| container_issue |
6 |
| title_short |
An experiment on wear particle's texture analysis and identification by using deterministic tourist walk algorithm |
| url |
http://search.proquest.com/docview/1710209217 |
| remote_bool |
false |
| author2 |
Haijun Wei Lidui Wei Jingming Li Zhiyuan Yang |
| author2Str |
Haijun Wei Lidui Wei Jingming Li Zhiyuan Yang |
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| up_date |
2024-07-03T19:19:16.168Z |
| _version_ |
1803586745058459649 |
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7.4001417 |