Research on 3D reconstruction method of wear particle dynamic image based on multi contour space mapping

Abstract Aiming at the problems of current image monitoring methods of lubrication oil wear particles, this paper designs and builds a dynamic monitoring system for oil wear particles based on microfluidic microscopic images. A contour-based 3D reconstruction method of debris particle images is proposed. The image sequences of rotating wear particles tracked by a single target are used as data, and the contour of the wear particle is extracted and the data is stored. The minimum area external rectangle method is used to correct the rotation of the particle images for the problem of deflection. And an algorithm based on cylindrical coordinate space conversion is used to convert the discrete contour point data into three-dimensional space. Complete the 3D model reconstruction of microfluidic wear particles. The ability to analyse wear particles in oil online monitoring technology is improved, which also shows new ideas for wear status monitoring and fault diagnosis technology.

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A Partial Hierarchical Model for Online Low-Resolution Wear Particle Images Classification

Computational Intelligence and Neuroscience ◽

10.1155/2021/6630247 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Xuxu Guo ◽

Rui Tan ◽

Mingyang Yang ◽

Xinrong He ◽

Jia Guo ◽

...

Keyword(s):

Neural Network ◽

Sliding Wear ◽

Super Resolution ◽

Wear Particle ◽

Support Vector ◽

Wear Particles ◽

The Third ◽

Online Recognition ◽

New Feature ◽

Particle Images

Wear particle image analysis is an effective method to detect wear condition of mechanical devices. However, the recognition accuracy and recognition efficiency for online wear particle automatic recognition are always mutual restricted because the online wear particle images have almost no texture information and lack clarity. Especially for confusing fatigue wear particles and sliding wear particles, the online recognition is a challenging task. Based on this requirement, a super-resolution reconstruct technique and partial hierarchical convolutional neural network, SR-PHnet, is proposed to classify wear particles in one step. The structure of this network is composed by three modules, one is super-resolution layer module, the second is convolutional neural network classifier module, and the third is support vector machine (SVM) classifier module. The classification result of the second module is partial input to the third module for precision classification of fatigue and sliding particles. In addition, a new feature of radial edge factor (REF) is put forward to target fatigue and sliding wear particles. The test result shows that the new feature has the capability to distinguish fatigue and sliding particles well and time saving. The comparison experiments of the convolution neural network (CNN) method, support vector machine method (SVM) with and without REF feature, and integrated model of back-propagation (BP) and CNN are produced. The comparison results show that the online recognition speed and online recognition rate of the proposed SR-PHnet model in this paper are both improved markedly, especially for fatigue and sliding wear particles.

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Application of numerical descriptors to the characterization of wear particles obtained from joint replacements

Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine ◽

10.1243/0954411961534620 ◽

1997 ◽

Vol 211 (1) ◽

pp. 1-10 ◽

Cited By ~ 15

Author(s):

G W Stachowiak ◽

G B Stachowiak ◽

P Campbell

Keyword(s):

Image Analysis ◽

Hip Prosthesis ◽

Knee Prosthesis ◽

Particle Morphology ◽

Wear Particle ◽

Wear Particles ◽

Joint Replacements ◽

Particle Images ◽

Ultra High Molecular Weight

The application of image analysis techniques to the characterization of wear particles from failed joint replacements has been described. Wear particles were extracted from periprosthetic tissues collected during revision surgery. Chemical digestive methods were used to separate the wear particles from the biological soft material. The particles isolated were examined by optical and scanning electron microscopy. Digitized particle images were analysed on a Macintosh computer by a specially developed software and by the image analysis program ‘Prism’. The following numerical descriptors were used to characterize the particles: particle size, boundary fractal dimension and shape parameters such as form factor, roundness, convexity and aspect ratio. Elemental composition of the particles was determined by energy dispersive X-ray spectroscopy. Three selected types of wear particles were analysed and compared: titanium (Ti)-based and calcium (Ca)-based particles from a hip prosthesis and ultra-high molecular weight polyethylene (UHMWPE) particles from a knee prosthesis. The particles exhibited significantly different sizes and their shape numerical descriptors were also different. From the results obtained it appears that computer image analysis of wear particle morphology can be employed to determine the wear processes occurring in the joints. In some cases, the condition of the joint can also be assessed based on this analysis.

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Comparative Study on Wear Particle Colour Classifications Using Various Machine Learning Algorithms

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.619.347 ◽

2014 ◽

Vol 619 ◽

pp. 347-351 ◽

Cited By ~ 1

Author(s):

Peerawatt Nunthavarawong

Keyword(s):

Machine Learning ◽

Copper Alloys ◽

Learning Algorithms ◽

Wear Particle ◽

Machine Learning Algorithms ◽

Wear Particles ◽

Meta Learning ◽

And Function ◽

Particle Images ◽

Worn Surface

Wear particle assessment is one of state-of-the-art in used lubricant analyses. There are three categories, i.e. shape analysis, surface texture analysis, and particle colour analysis. Especially, an analysis of wear particle colour can be induced to identify the material type from the worn surface when surface failures of component occurred in the most commonly used lubricants. The quantification of wear particle colours is essential, which is readily extracted by the image processing. However, the colours of wear particles are often unrecognised by visual examination of the human, although these are probably indicated via RGB colours by personal computer. This article therefore aims to determine the quantitative colour descriptors of wear particles, and to find out the suitable method to classify the particle colour. In present work, the colours of wear particle images were separated with combined HSI and L*a*b* colour models, and were then classified by using machine learning algorithms as a decision-making tool. These tools consist of the Bayesian classifiers, Tree classifiers, Rule classifiers, Lazy classifiers, Meta-learning classifiers, and Function classifiers. By comparing in their tools, the function classifier tool was performed to accurately distinguish the heated metals, steel particles, dark and red oxides, and copper alloys, resulting in more reliable examination than that of the other tools.

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Research on 3D Reconstruction Method Based on Temporal Fourier Transform Profilometry

2020 2nd International Conference on Information Technology and Computer Application (ITCA) ◽

10.1109/itca52113.2020.00150 ◽

2020 ◽

Author(s):

Liao Min ◽

Luo weifeng

Keyword(s):

Fourier Transform ◽

3D Reconstruction ◽

Reconstruction Method ◽

Fourier Transform Profilometry

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Automatic Reconstruction of Multi-Level Indoor Spaces from Point Cloud and Trajectory

Sensors ◽

10.3390/s21103493 ◽

2021 ◽

Vol 21 (10) ◽

pp. 3493

Author(s):

Gahyeon Lim ◽

Nakju Doh

Keyword(s):

Point Cloud ◽

Dimensional Space ◽

Three Dimensional ◽

Reconstruction Method ◽

Complex Environments ◽

Indoor Space ◽

Multi Level ◽

Level Building ◽

Indoor Spaces ◽

Data Term

Remarkable progress in the development of modeling methods for indoor spaces has been made in recent years with a focus on the reconstruction of complex environments, such as multi-room and multi-level buildings. Existing methods represent indoor structure models as a combination of several sub-spaces, which are constructed by room segmentation or horizontal slicing approach that divide the multi-room or multi-level building environments into several segments. In this study, we propose an automatic reconstruction method of multi-level indoor spaces with unique models, including inter-room and inter-floor connections from point cloud and trajectory. We construct structural points from registered point cloud and extract piece-wise planar segments from the structural points. Then, a three-dimensional space decomposition is conducted and water-tight meshes are generated with energy minimization using graph cut algorithm. The data term of the energy function is expressed as a difference in visibility between each decomposed space and trajectory. The proposed method allows modeling of indoor spaces in complex environments, such as multi-room, room-less, and multi-level buildings. The performance of the proposed approach is evaluated for seven indoor space datasets.

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3D RECONSTRUCTION OF BREAST CANCER FROM MAMMOGRAMS USING VOLUME RENDERING TECHNIQUES

Jurnal Teknologi ◽

10.11113/jt.v75.4978 ◽

2015 ◽

Vol 75 (2) ◽

Author(s):

Ho Wei Yong ◽

Abdullah Bade ◽

Rajesh Kumar Muniandy

Keyword(s):

Breast Cancer ◽

3D Reconstruction ◽

Computational Cost ◽

Reconstruction Method ◽

Cancer Model ◽

Volume Reconstruction ◽

The Past ◽

Breast Cancer Model ◽

Reconstruction Software ◽

To Come

Over the past thirty years, a number of researchers have investigated on 3D organ reconstruction from medical images and there are a few 3D reconstruction software available on the market. However, not many researcheshave focused on3D reconstruction of breast cancer’s tumours. Due to the method complexity, most 3D breast cancer’s tumours reconstruction were done based on MRI slices dataeven though mammogram is the current clinical practice for breast cancer screening. Therefore, this research will investigate the process of creating a method that will be able to reconstruct 3D breast cancer’s tumours from mammograms effectively. Several steps were proposed for this research which includes data acquisition, volume reconstruction, andvolume rendering. The expected output from this research is the 3D breast cancer’s tumours model that is generated from correctly registered mammograms. The main purpose of this research is to come up with a 3D reconstruction method that can produce good breast cancer model from mammograms while using minimal computational cost.

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