An experiment on wear particle’s texture analysis and identification by using deterministic tourist walk algorithm

Hong, Liu; Wei, Haijun; Wei, Lidui; Li, Jingming; Yang, Zhiyuan

doi:10.1108/ilt-01-2015-0008

Cited by 12 publications

(4 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The deterministic tourist walk-based approach has been used to build a system for the identification of wear particles [106]. Wear particles give information on the wear processes taking place between mechanical components.…”

Section: ) Examples Of Applicationsmentioning

confidence: 99%

Texture Feature Extraction Methods: A Survey

2019

View full text Add to dashboard Cite

Texture analysis is used in a very broad range of fields and applications, from texture classification (e.g., for remote sensing) to segmentation (e.g., in biomedical imaging), passing through image synthesis or pattern recognition (e.g., for image inpainting). For each of these image processing procedures, first, it is necessary to extract-from raw images-meaningful features that describe the texture properties. Various feature extraction methods have been proposed in the last decades. Each of them has its advantages and limitations: performances of some of them are not modified by translation, rotation, affine, and perspective transform; others have a low computational complexity; others, again, are easy to implement; and so on. This paper provides a comprehensive survey of the texture feature extraction methods. The latter are categorized into seven classes: statistical approaches, structural approaches, transform-based approaches, model-based approaches, graph-based approaches, learning-based approaches, and entropybased approaches. For each method in these seven classes, we present the concept, the advantages, and the drawbacks and give examples of application. This survey allows us to identify two classes of methods that, particularly, deserve attention in the future, as their performances seem interesting, but their thorough study is not performed yet.

show abstract

Section: ) Examples Of Applicationsmentioning

confidence: 99%

Texture Feature Extraction Methods: A Survey

2019

View full text Add to dashboard Cite

show abstract

“…Wang [20] used principal component analysis to optimize the characteristic parameters of wear particles and then distinguish wear particles with Grey relational analysis. Other methods have also been used in this area, such as classification and regression trees [11], deterministic tourist walks [21], the AdaBoost algorithm [22], and extreme learning machines [23]. To the best of our knowledge, most identification models for wear particles are based on 2D characteristics, and very few researches have used 3D characteristics to identify categories of wear particles [24].…”

Section: Introductionmentioning

confidence: 99%

Intelligent wear mode identification system for marine diesel engines based on multi-level belief rule base methodology

Yan

Cui

et al. 2017

Meas. Sci. Technol.

View full text Add to dashboard Cite

Wear faults are among the chief causes of main-engine damage, significantly influencing the secure and economical operation of ships. It is difficult for engineers to utilize multi-source information to identify wear modes, so an intelligent wear mode identification model needs to be developed to assist engineers in diagnosing wear faults in diesel engines. For this purpose, a multi-level belief rule base (BBRB) system is proposed in this paper. The BBRB system consists of two-level belief rule bases, and the 2D and 3D characteristics of wear particles are used as antecedent attributes on each level. Quantitative and qualitative wear information with uncertainties can be processed simultaneously by the BBRB system. In order to enhance the efficiency of the BBRB, the silhouette value is adopted to determine referential points and the fuzzy c-means clustering algorithm is used to transform input wear information into belief degrees. In addition, the initial parameters of the BBRB system are constructed on the basis of expert-domain knowledge and then optimized by the genetic algorithm to ensure the robustness of the system. To verify the validity of the BBRB system, experimental data acquired from real-world diesel engines are analyzed. Five-fold cross-validation is conducted on the experimental data and the BBRB is compared with the other four models in the cross-validation. In addition, a verification dataset containing different wear particles is used to highlight the effectiveness of the BBRB system in wear mode identification. The verification results demonstrate that the proposed BBRB is effective and efficient for wear mode identification with better performance and stability than competing systems.

show abstract

“…The parameters that define wear particles such as their quantity, shape, and size reflect the wear modes, wear mechanisms, and severity associated with their generation [4]. Wear debris contained in the lubrication oil carry detailed and important information about the condition of the machine [5]. The particle characteristics are sufficiently specific so that the operating wear modes within the machine may be determined, allowing prediction of the imminent behavior of the machine [6,7].…”

Section: Introductionmentioning

confidence: 99%

“…Most of the work focused on how to identify wear particles [1]. It is reported that antcolony algorithm [9], deterministic tourist walking [5], and fuzzy -means algorithm [10] can be used to identify wear particles. Also, in [11], attempts had been made to identify adhesive fatigue and abrasive particles using area, perimeter, and elongation parameters; study shows that these simple parameters can be effective for certain types of wear particles such as abrasive particles and sphere like fatigue particles.…”

Section: Introductionmentioning

confidence: 99%

The Segmentation of Wear Particles Images UsingJ-Segmentation Algorithm

Hong

Wei

et al. 2016

Advances in Tribology

Self Cite

View full text Add to dashboard Cite

This study aims to use a JSEG algorithm to segment the wear particle’s image. Wear particles provide detailed information about the wear processes taking place between mechanical components. Autosegmentation of their images is key to intelligent classification system. This study examined whether this algorithm can be used in particles’ image segmentation. Different scales have been tested. Compared with traditional thresholding along with edge detector, the JSEG algorithm showed promising result. It offers a relatively higher accuracy and can be used on color image instead of gray image with little computing complexity. A conclusion can be drawn that the JSEG method is suited for imaged wear particle segmentation and can be put into practical use in wear particle’s identification system.

show abstract

An experiment on wear particle’s texture analysis and identification by using deterministic tourist walk algorithm

Cited by 12 publications

References 20 publications

Texture Feature Extraction Methods: A Survey

Texture Feature Extraction Methods: A Survey

Intelligent wear mode identification system for marine diesel engines based on multi-level belief rule base methodology

The Segmentation of Wear Particles Images UsingJ-Segmentation Algorithm

Contact Info

Product

Resources

About