Wear particle shape and edge detail analysis

Laghari, Mohammad Shakeel; Ahmed, Faheem; Aziz, Junaid

doi:10.1109/iccae.2010.5451985

Cited by 15 publications

(7 citation statements)

References 4 publications

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“…Laghari et al (2020) have classified four particle shapes by using the Histogram of Oriented Gradients, and other shape attributes including extent, eccentricity, major and minor axis length, centroid distance, and equiv diameter. Laghari et al (2010) have derived, processed, and stored quantitative data of wear particle shape and edge details analysis by implementing an interactive image analysis system. The stored information is analyzed to process a systematic morphological analysis of wear debris.…”

Section: Related Workmentioning

confidence: 99%

Identifying Wear Particles With Their Shape Attribute

2022

Proceedings of the 16th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing (CGV

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Wear particles are produced in all oil-lubricated machines when mechanical parts come in contact. These particles are generated in differing sizes, altering quantities, with specific composition, and morphology. The particles when examined by experts provide vital information concerning machine health to operate and maintain safety, efficiency, and economy. As condition monitoring becomes increasingly important and needs to be carried out regularly, a custom-designed software system is developed to investigate the wear particles by using computer vision and image processing techniques. Identifying abnormal wear at an earlier stage avoids wearing failure modes of components that may lead to the breakdown of the machinery. Conventional methods are used to diagnose wear conditions, however; the use of six morphological attributes of particle size, shape, edge details, color, surface texture, and thickness ratio is the basis of constructing an image analysis system to strengthen wear judgments. The particle shape is one of the most important attributes, which is investigated in this paper. Various parameters are used to extract shape features to identify six wear particle types including severe sliding wear, cutting edge, non-metallic, fatigue, water, and fiber particles. Particle identification is based on eight geometric features which are extracted by the image analysis system and MATLAB code. Tests are conducted to achieve 90% accuracy.

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Section: Related Workmentioning

confidence: 99%

Identifying Wear Particles With Their Shape Attribute

2022

Proceedings of the 16th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing (CGV

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“…The BRB of each level in the BBRB model represents expert-domain knowledge. On the ith (i = 1, 2) level, the kth rule is defined as follows [24]:…”

Section: Brb Inference 221 Brbmentioning

confidence: 99%

“…ω k indicates the degree to which the kth rule is activated. If ω k = 0, the kth rule will not be activated [24].…”

Section: Rule Inference With Evidential Reasoning (Er) Algo-mentioning

confidence: 99%

“…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]. Moreover, most wear mode identification models have been developed on the basis of data-driven methods, such as ANNs and SVMs.…”

Section: Introductionmentioning

confidence: 99%

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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.

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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.

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“…This threshold is called threshold above. There are many variants including threshold below, which makes the pixel values less than or equal to t the foreground, threshold in side which is given a lower threshold and a upper threshold and selected pixel whose values are between the two as foreground, and threshold outside, which is the opposite of threshold inside [9].…”

Section: Disadvantage Of Debris Analysismentioning

confidence: 99%

Vision base Tool Monitoring System for a Reconfigure Micro Factory System

Pant¹,

Sunori²,

Gupta³

2012

IJCA

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During the past decade, new sensing technologies, such as are optical measurement, image processing (edge detection, pattern matching, x-ray image processing), acceleration sensor, vibration sensor, inductive loops, laser range scanners, computer vision sensors have been greatly enhanced and applied to the Intelligent in Automatic Tool Wear Monitoring System (ATWMS) area. On-line tool monitoring system may realize substantial cost saving through image processing. Currently, tool wear monitoring performed by vibration, inductive and debris sensors. Oil debris sensor is a popular measurement device used to collect oil condition data. This sensor generates an output signature with the passage of a metallic particle through the oil return lines. However, the signal measured through the oil debris sensor is severely tainted by various noises, e.g., the background noise present as well as the interferences caused by the vibrations of the structure where the sensor is mounted. These interferences affect the performance of the health assessment unit considerably. As such this paper focuses on the tool condition monitoring direct by metal chips using image processing technology.

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Wear particle shape and edge detail analysis

Cited by 15 publications

References 4 publications

Identifying Wear Particles With Their Shape Attribute

Identifying Wear Particles With Their Shape Attribute

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

Vision base Tool Monitoring System for a Reconfigure Micro Factory System

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