Classification of Wear Debris Using Weighted Fuzzy Cluster Method

2010 ◽  
Vol 33 ◽  
pp. 70-73
Author(s):  
Dai Qiang Peng ◽  
Feng Xu
Keyword(s):  
Wear Debris ◽  
Wear Particle ◽  
Fuzzy Cluster ◽  
Cluster Method ◽  
Wear Particles ◽  
Machine Condition ◽  
Machine Condition Monitoring ◽  
Fuzzy Cluster Method ◽  
The Membership Function

The analysis and identification of wear particles for machine condition monitoring is usually very time-consuming by experienced inspectors. In order to remedy the limitation, automation of the analysis procedure appears to be necessary. A novel weighted fuzzy c-means algorithm for wear particle classification is proposed in this paper. The algorithm uses the variation of the pixel intensities of a region to choose strong resembling area. Then, the spatial relationships of the membership function are constructed to regulate the pixel membership obtained from the FCM object function. Finally, wear debris are classified based on the fuzzy membership. The example shows that the method is briefly and effectively.

The Study of Three-Dimensional Analysis Techniques and Automatic Classification Systems for Wear Particles

1999 ◽  
Vol 121 (1) ◽  
pp. 169-176 ◽  
Author(s):  
Z. Peng ◽  
T. B. Kirk
Keyword(s):  
Image Analysis ◽  
Wear Debris ◽  
Three Dimensional ◽  
Wear Particle ◽  
Automatic Classification ◽  
Classification Systems ◽  
Image Analysis System ◽  
Wear Particles ◽  
Machine Condition ◽  
Analysis System

Although the morphology of wear debris generated in a machine has a direct relationship to wear processes and machine condition, studying wear particles for machine condition monitoring has not been widely applied in Industry as it is time consuming and requires certain expertise of analysts. To overcome these obstacles, automatic wear particle analysis and identification systems need to be developed. In this paper, laser scanning confocal microscopy has been used to obtain three-dimensional images of metallic wear particles. An analysis system has been developed and applied to study the boundary morphology and surface topography of the wear debris. After conducting the image analysis procedure and selecting critical criteria from dozens of available parameters, neural networks and grey systems have been investigated to classify unknown patterns using the numerical descriptors. It is demonstrated that the combination of the image analysis system and automatic classification systems has provided an automatic package for wear particle study which may be applied to industrial applications in the future.

Classification of precipitation series using fuzzy cluster method

2011 ◽  
Vol 32 (10) ◽  
pp. 1596-1603 ◽  
Author(s):  
Fatih Dikbas ◽  
Mahmut Firat ◽  
A. Cem Koc ◽  
Mahmud Gungor
Keyword(s):  
Fuzzy Cluster ◽  
Cluster Method ◽  
Precipitation Series ◽  
Fuzzy Cluster Method

A fuzzy cluster method for turbulent/non-turbulent interface detection

2021 ◽  
Vol 62 (4) ◽  
Author(s):  
Khaled Younes ◽  
Bradley Gibeau ◽  
Sina Ghaemi ◽  
Jean-Pierre Hickey
Keyword(s):  
Fuzzy Cluster ◽  
Cluster Method ◽  
Fuzzy Cluster Method

scholarly journals A simple method of suspending implant wear particles for more physiological modelling of cell-particle interactions in vitro

2021 ◽  
Author(s):  
Christine Poon
Keyword(s):  
Wear Debris ◽  
Culture Media ◽  
Polymeric Materials ◽  
Wear Particle ◽  
Physicochemical Characteristics ◽  
Wear Particles ◽  
Simple Method ◽  
Implant Wear

AbstractArthroplasty implants e.g. hip, knee, spinal disc sustain relatively high compressive loading and friction wear, which lead to the formation of wear particles or debris between articulating surfaces. Despite advances in orthopaedic materials and surface treatments, the production of wear debris from any part of a joint arthroplasty implant is currently unavoidable. Implant wear debris induces host immune responses and inflammation, which causes patient pain and ultimately implant failure through progressive inflammation-mediated osteolysis and implant loosening, where the severity and rate of periprosthetic osteolysis depends on the material and physicochemical characteristics of the wear particles. Evaluating the cytotoxicity of implant wear particles is important for regulatory approved clinical application of arthroplasty implants, as is the study of cell-particle response pathways. However, the wear particles of polymeric materials commonly used for arthroplasty implants tend to float when placed in culture media, which limits their contact with cell cultures. This study reports a simple means of suspending wear particles in liquid medium using sodium carboxymethyl cellulose (NaCMC) to provide a more realistic proxy of the interaction between cells and tissues to wear particles in vivo, which are free-floating in synovial fluid within the joint cavity. Low concentrations of NaCMC dissolved in culture medium were found to be effective for suspending polymeric wear particles. Such suspensions may be used as more physiologically-relevant means for testing cellular responses to implant wear debris, as well as studying the combinative effects of shear and wear particle abrasion on cells in a dynamic culture environments such as perfused tissue-on-chip devices.

Expert-Free Wear Particle Morphology Analysis (Keynote)

2005 ◽  
Author(s):  
G. W. Stachowiak
Keyword(s):  
Condition Monitoring ◽  
Classification System ◽  
Particle Morphology ◽  
Wear Particle ◽  
Particle Analysis ◽  
Machine Condition ◽  
Wear Particle Analysis ◽  
Analysis Process ◽  
Machine Condition Monitoring ◽  
Morphology Analysis

Since the early 1970s wear particles have been used as indicators of the health status of industrial machinery. Their quantity, size and morphology was utilized in machine condition monitoring to diagnose and predict the likelihood or the cause of machine failure. In particular, the wear particle morphology was found useful as it contains the vast wealth of information about the wear processes involved in particle formation, and the wear severity. However, the application of wear particle morphology analysis in machine condition monitoring has limitations. This is due to the fact that the process largely depends on the experience of the technicians conducting the analysis. Research efforts are therefore directed towards making the whole wear particle analysis process experts-free, i.e. automated. To achieve that a detailed database of wear particle morphologies, generated under different operating conditions and with different materials for sliding pairs, must be assembled. Next, the reliable and accurate methods allowing for the description of 3-D wear particle morphology must be found. Multiscale and nonstationary characteristics of wear particle surface topographies must be accounted for. Finally, a reliable wear particle classification system must be developed. This classification system must be reliable and robust hence the selection of appropriate classifiers becomes a critical issue. It is hoped that the system, once fully developed, would eliminate the need for experts in wear particle analysis and make the whole analysis process less time consuming, cheaper and more reliable. In this presentation it is shown how the problems leading towards the development of such system are gradually overcome. Also, the recent advances towards the development of expert-free wear particle morphology system for the application in machine condition monitoring are presented.

Modelling of wear debris in planetary gear drive

2019 ◽  
Vol 71 (2) ◽  
pp. 199-204
Author(s):  
Rakesh Ranjan ◽  
Subrata Kumar Ghosh ◽  
Manoj Kumar
Keyword(s):  
Probability Distribution ◽  
Aspect Ratio ◽  
Wear Debris ◽  
Planetary Gear ◽  
Wear Particle ◽  
Distribution Model ◽  
Wear Particles ◽  
Slab Caster ◽  
Content Type ◽  
Exponential Probability

Purpose The probability distribution of major length and aspect ratio (major length/minor length) of wear debris collected from gear oil used in planetary gear drive were analysed and modelled. The paper aims to find an appropriate probability distribution model to forecast the kind of wear particles at different running hour of the machine. Design/methodology/approach Used gear oil of the planetary gear box of a slab caster was drained out and charged with a fresh oil of grade (EP-460). Six chronological oil samples were collected at different time interval between 480 and 1,992 h of machine running. The oil samples were filtered to separate wear particles, and microscopic study of wear debris was carried out at 100X magnification. Statistical modelling of wear debris distribution was done using Weibull and exponential probability distribution model. A comparison was studied among actual, Weibull and exponential probability distribution of major length and aspect ratio of wear particles. Findings Distribution of major length of wear particle was found to be closer to the exponential probability density function, whereas Weibull probability density function fitted better to distribution of aspect ratio of wear particle. Originality/value The potential of the developed model can be used to analyse the distribution of major length and aspect ratio of wear debris present in planetary gear box of slab caster machine.

Classification of Wear Particles Based on Qualitative Morphological Features

2005 ◽  
Author(s):  
R. H. Chang ◽  
Hosung Kong ◽  
Eui-Sung Yoon ◽  
Dong-Hoon Choi
Keyword(s):  
Image Recognition ◽  
Morphological Feature ◽  
Wear Debris ◽  
Morphological Features ◽  
Multidimensional Space ◽  
Semantic Features ◽  
Wear Particles ◽  
Wear Mode ◽  
Wear Monitoring

Wear debris morphology is closely related to the wear mode and mechanism occurred. Image recognition of wear particles is, therefore, a powerful tool in wear monitoring. An algorithm of classification of wear particles is proposed based on qualitative morphological features. The standard classes are presented as a set of vectors of coded ratings. Descriptions of the standards are based on the knowledge-base of experts. A distance between the particle and the standard classes in the multidimensional space of features showed rating of the similarity. The classification of particles is determined by identifying the closet standard. The coding of the semantic features of the morphological feature of wear particles was demonstrated to be useful for classification with statistical methods. The results showed that the presented method was satisfactory in solving practical problems.

Quantitative wear particle analysis for osteoarthritis assessment

2017 ◽  
Vol 231 (12) ◽  
pp. 1116-1126
Author(s):  
Meizhai Guo ◽  
Megan S Lord ◽  
Zhongxiao Peng
Keyword(s):  
Wear Debris ◽  
Wear Particle ◽  
Degradation Process ◽  
Cartilage Degradation ◽  
Degenerative Joint Disease ◽  
Joint Disease ◽  
Particle Analysis ◽  
Wear Particles ◽  
Wear Particle Analysis ◽  
Invasive Method

Osteoarthritis is a degenerative joint disease that affects millions of people worldwide. The aims of this study were (1) to quantitatively characterise the boundary and surface features of wear particles present in the synovial fluid of patients, (2) to select key numerical parameters that describe distinctive particle features and enable osteoarthritis assessment and (3) to develop a model to assess osteoarthritis conditions using comprehensive wear debris information. Discriminant analysis was used to statistically group particles based on differences in their numerical parameters. The analysis methods agreed with the clinical osteoarthritis grades in 63%, 50% and 61% of particles for no osteoarthritis, mild osteoarthritis and severe osteoarthritis, respectively. This study has revealed particle features specific to different osteoarthritis grades and provided further understanding of the cartilage degradation process through wear particle analysis – the technique that has the potential to be developed as an objective and minimally invasive method for osteoarthritis diagnosis.

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