A Quantitative Model of Wear Loss Based on On-Line Visual Ferrograph

2013 ◽  
Vol 330 ◽  
pp. 338-345
Author(s):  
Chun Hui Wang ◽  
Wei Yuan ◽  
Guang Neng Dong ◽  
Jun Hong Mao
Keyword(s):  
Condition Monitoring ◽  
Testing Machine ◽  
Three Dimensional ◽  
Wear Particle ◽  
Quantitative Model ◽  
Wear Loss ◽  
Wear Particles ◽  
Coverage Area ◽  
Wear Process ◽  
On Line

On-line visual ferrograph (OLVF) is an efficient and real-time condition monitoring device. From the point of flow conservation, on the basis of the particle coverage area data collected by OLVF, this paper deduced two models about wear loss of the tribo-pairs in the wear process, one is general mathematical (GM) model including distribution impact factor of wear particle, and other simplified GM (SGM) model which does not contain the factor. The key factor affecting the accuracy of the two models is the three dimensional information of wear particles referring to particle area and thickness. This model using the disc and the ball whose materials were GCr15 were experimentally demonstrated on a pin-on-disc testing machine. And the OLVF was used to acquire the coverage area of the wear particles, which can reflect the wear loss. It shows that, in some cases, the approximate wear loss in the process was obtained on-line conveniently. Compared with experiment values derived from other wear measurement methods like weighing mass method and surface profilometry method, the SGM model can reflect tendency of wear loss about the tribo-pairs continuously. The deviations about wear loss by the model were discussed. Meanwhile, compared with the traditional means to compute the wear loss, this SGM model could be employed both for off-line analysis and on-line condition monitoring programs.

The Forecast for the Abrasive Wear of Metal Materials Aginst Plant Abrasive Based on the Grey Markov Model

2011 ◽  
Vol 130-134 ◽  
pp. 984-988
Author(s):  
Xiao Peng Huang ◽  
Jian Long Huang ◽  
Jing Feng Wu ◽  
Ke Ping Zhang
Keyword(s):  
Abrasive Wear ◽  
Testing Machine ◽  
The State ◽  
Wear Testing ◽  
Wear Loss ◽  
Grey Theory ◽  
Markovian Model ◽  
Wear Prediction ◽  
Wear Process ◽  
Metal Materials

Metal materials wear loss against plant abrasive of different wear process was obtained by simulation test on the abrasive wear testing machine. On the basis of it, the GM (1, 1) model is established by using grey theory. Then the state of experiment data is divided using markov chain, the state transition matrix is constructed, finally the grey markovian model is established and wear prediction of metal materials against plant abrasive is gained. The results indicate that wear prediction based on the grey markovian model is more precise than the GM (1, 1), model, relative error being only 1.13%

A prototype of on-line extraction and three-dimensional characterisation of wear particle features from video sequence

Wear ◽  
2016 ◽  
Vol 368-369 ◽  
pp. 314-325 ◽  
Author(s):  
Hongkun Wu ◽  
Ngai Ming Kwok ◽  
Sanchi Liu ◽  
Tonghai Wu ◽  
Zhongxiao Peng
Keyword(s):  
Video Sequence ◽  
Three Dimensional ◽  
Wear Particle ◽  
Line Extraction ◽  
On Line

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.

scholarly journals A Lubricating Oil Condition Monitoring System Based on Wear Particle Kinematic Analysis in Microfluid for Intelligent Aeroengine

Micromachines ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 748
Author(s):  
Zhenzhen Liu ◽  
Yan Liu ◽  
Hongfu Zuo ◽  
Han Wang ◽  
Hang Fei
Keyword(s):  
Condition Monitoring ◽  
Kinematic Analysis ◽  
Oil Quality ◽  
Particle Diameter ◽  
Wear Particle ◽  
Lubricating Oil ◽  
Wear Particles ◽  
Oil Viscosity ◽  
Monitoring Technology ◽  
Oil Monitoring

Lubricating oil monitoring technology is a commonly used method in aeroengine condition monitoring, which includes particle counting technology, as well as spectral and ferrography technology in offline monitoring. However, these technologies only analyze the characteristics of wear particles and rely on physical and chemical analysis techniques to monitor the oil quality. In order to further advance offline monitoring technology, this paper explores the potential role of differences in wear particle kinematic characteristics in recognizing changes in wear particle diameter and oil viscosity. Firstly, a kinematic force analysis of the wear particles in the microfluid was carried out. Accordingly, a microfluidic channel conducive to observing the movement characteristics of particles was designed. Then, the wear particle kinematic analysis system (WKAS) was designed and fabricated. Secondly, a real-time tracking velocity measurement algorithm was developed by using the Gaussian mixture model (GMM) and the blob-tracking algorithm. Lastly, the WKAS was applied to a pin–disc tester, and the experimental results show that there is a corresponding relationship between the velocity of the particles and their diameter and the oil viscosity. Therefore, WKAS provides a new research idea for intelligent aeroengine lubricating oil monitoring technology. Future work is needed to establish a quantitative relationship between wear particle velocity and particle diameter, density, and oil viscosity.

Oil condition monitoring for HEMM – a case study

2016 ◽  
Vol 68 (6) ◽  
pp. 718-722 ◽  
Author(s):  
Ashwani Kumar ◽  
Subrata Kumar Ghosh
Keyword(s):  
Condition Monitoring ◽  
Mining Industry ◽  
Chemical Properties ◽  
Iron Alloy ◽  
Cylinder Liner ◽  
Wear Particle ◽  
Particle Analysis ◽  
Contamination Level ◽  
Wear Particles ◽  
Content Type

Purpose The paper aims to monitor the condition of heavy Earth-moving machines (HEMMs) used in open cast mines by lube oil analysis. Design/methodology/approach Oil samples at periodic interval were collected from the HEMM engine (Model No: BEML BH50M). Ferrography and Field Emission Scanning Electron Microscopy have been used for the wear particle analysis present in oil samples. Viscosity analysis and Fourier transform infrared spectroscopy have been done to investigate the degradation in quality and changes as compared to the initial structural properties of the lubricants. Findings The results obtained indicates wear in cylinder liner and piston ring. Copper, cast iron, alloy steel and ferrous oxide have been found as rubbing wear particles and cutting wear particles. Contamination level has also been found to be increasing in consecutive older oil samples. Chemical properties degraded with usage time and variations in oxidation and soot level have also been observed in every sample. Practical implications The results will be very much useful to maintenance teams of mining industry for early prediction of any impending failure of the machines, for example, diesel dilution, severe wear of the piston or cylinder liner leading to seizure can be predicted. Originality/value The HEMMs are an important piece of equipment in coal mining. Proper condition monitoring of HEMM is required to reduce the break down and down time to increase production.

Study on the correlation between the running-in attractor and the wear particle group

2019 ◽  
Vol 72 (5) ◽  
pp. 681-686
Author(s):  
Cong Ding ◽  
Zhen-Yu Zhou ◽  
Zhi-Peng Yuan ◽  
Hua Zhu ◽  
Zhong-Yu Piao
Keyword(s):  
Correlation Dimension ◽  
Design Methodology ◽  
Wear Particle ◽  
Chord Length ◽  
Wear Particles ◽  
Dynamic Features ◽  
Dynamic Nature ◽  
Content Type ◽  
Wear Process ◽  
Particle Group

Purpose The purpose of this paper is to study the correlation between the dynamic features of the running-in attractor and the wear particle group, so as to characterize the running-in attractor by means of the wear particle group. Design/methodology/approach Wear particles are collected in phased wear experiments, and their dynamic features are investigated by the equivalent mean chord length L. Then, the correlation between the equivalent mean chord length L and the correlation dimension D of the running-in attractor is studied. Findings In the wear process, the equivalent means chord length L first decreases, then remains steady, and finally increases, this process agrees with the increase, stabilization and decrease of the correlation dimension D. Therefore, the wear particle group has a dynamic nature, which characterizes the formation, stabilization, and disappearance of a running-in attractor. Consequently, the dynamic characteristics and evolution of a running-in attractor can be revealed by the wear particle group. Originality/value The intrinsic relationship between the wear particle group and the running-in attractor is proved, and this is advantageous for further revealing the dynamic features of the running-in attractor and identifying the wear states.

INVESTIGATION INTO CHANGES IN COLLAGEN STRUCTURE OF ARTICULAR CARTILAGE AND WEAR PARTICLES OF KNEE JOINTS FOR OSTEOARTHRITIC WEAR ASSESSMENT

2009 ◽  
Vol 12 (03) ◽  
pp. 143-152 ◽  
Author(s):  
Zhongxiao Peng ◽  
Mark Fasiolo ◽  
Kane Hart
Keyword(s):  
Three Dimensional ◽  
Diagnostic Procedures ◽  
Collagen Matrix ◽  
Staining Technique ◽  
Wear Particle ◽  
Knee Joints ◽  
Wear Particles ◽  
Diagnosis And Prognosis ◽  
Wear And Tear ◽  
Positive Results

Our bodies deteriorate from wear and tear processes, which give rise to ache and pain. This degenerative process is medically referred to as osteoarthritis (OA) and it is estimated to affect a large portion of the population at some stage in their life. There is a need for research into new and improved techniques that might be developed into a schema to aid in the early diagnosis and prognosis of a patient's condition. This can be achieved by studying the morphology of the collagen fibers in the wear particles generated to gain an insight into the osteoarthritic condition exhibited by the joint. The study has been conducted in three phases. Firstly, an animal model has been used to generate samples of cartilage and wear particles for the study. A suitable staining technique has then been developed that allows the three-dimensional visualization and quantitative analysis of the structure of the collagen matrix of sheep cartilage and in wear particles. Finally, correlation of the changes in the collagen matrix as per OA severity has been studied. The study has identified key numerical parameters to characterize distinctive wear features of the cartilage and wear debris. A good correlation of the wear features of the cartilage and wear particle samples has been found. The positive results attained by this study suggest that with the aid of further research and development, it is distinctly possible to develop improved diagnostic procedures for clinical osteoarthritic assessment.

Effect of Surface Energy on the Wear Process

1964 ◽  
Vol 86 (2) ◽  
pp. 306-310 ◽  
Author(s):  
E. Rabinowicz ◽  
R. G. Foster
Keyword(s):  
Particle Size ◽  
Surface Energy ◽  
Wear Particle ◽  
Particle Sizes ◽  
Wear Particles ◽  
Wear Process ◽  
External Variables ◽  
Particle Measurement ◽  
Effect Of Surface ◽  
Correct Size

It was previously shown that the size of loose wear particles formed during the sliding of two materials is equal to 60,000 Wab/P, where Wab is the surface energy of adhesion and p the penetration hardness. Experimental results are presented which show that the experimental particle sizes obtained with a few materials do indeed obey the theoretical relationship, and that the particle size is, as predicted, almost independent of such external variables as speed, time, geometry, and load, provided the load is not too great. Indeed, if particles of the wrong size are fed into the system, then they tend to be broken down or built up until the correct size is reached. However, changes of atmosphere and the use of lubricants, which alter the energy of adhesion, do have a marked influence on wear-particle size, and this fact suggests a possible use of wear-particle measurement to rate boundary lubricants. Other surface interaction phenomena which are governed by the W/p ratio are discussed, and it is suggested that the surface roughness generated during sliding is a function of this ratio.

A microfluidic device for three-dimensional wear debris imaging in online condition monitoring

2016 ◽  
Vol 231 (8) ◽  
pp. 965-974 ◽  
Author(s):  
Yeping Peng ◽  
Tonghai Wu ◽  
Shuo Wang ◽  
Ying Du ◽  
Ngaiming Kwok ◽  
...  
Keyword(s):  
Condition Monitoring ◽  
Microfluidic Device ◽  
Wear Debris ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Morphological Characteristics ◽  
Channel Height ◽  
Wear Particles ◽  
Continuous Rotation ◽  
Cmos Sensor

Three-dimensional morphologies of wear particles are important information sources for machine condition assessment and fault diagnosis. However, existing three-dimensional image acquisition systems, such as laser scanning confocal microscopy and atomic force microscopy, cannot be directly applied in condition-based maintenance of machines. In order to automatically acquire three-dimensional information of wear debris for online condition monitoring, a microfluidic device consisting of an oil flow channel and a video imaging system is developed. This paper focuses on the control of particle motions. A microchannel is designed to ensure the continuous rotation of particles such that their three-dimensional features can be captured. The relationships between running torque and channel height and particle size are analysed to determine the channel height. An infinite fluid field is considered to make sure that the particles rotate around the same axis to capture 360 degree views. Based on this, the cross section of the microchannel is determined at 5 mm × 0.2 mm (height × width) to capture the wear debris under 200 µm. A CMOS sensor is used to image the particles in multiple views and then three-dimensional features of wear debris (e.g. thickness, height aspect ratio and sphericity) are obtained. Two experiments were carried out to evaluate the performances of the designed system. The results demonstrate that (1) the microfluidic device is effective in capturing multiple view images of wear particles various in sizes and shapes; (2) spatial morphological characteristics of wear particles can be constructed using a sequence of multi-view images.

Computational and Experimental Study of a Platinum Thin-Film Based Oil Condition and Contamination Sensor

2017 ◽  
Author(s):  
V. Sridhar ◽  
K. S. Chana ◽  
D. Singh
Keyword(s):  
Thin Film ◽  
Heat Transfer ◽  
Experimental Study ◽  
Condition Monitoring ◽  
Thermal Stresses ◽  
Three Dimensional ◽  
Aircraft Engine ◽  
Oil Contamination ◽  
On Line ◽  
Platinum Thin Film

Lubrication systems form an integral part of aircraft and automobiles. Failure of lubrication systems can occur due to contamination or degradation of oil which can lead to excessive wear and failure of rotating components. This leads to unnecessary downtime and increase in maintenance costs. Oil contamination occurs when metallic or non-metallic particles are produced due to wear of the machine components such as bearings, gears etc. and these particles may not be always captured by the filtering system that are already in the lubrication system. Hence, the particles can clog oil paths and accelerate the wear of moving parts. In addition to this, variations in thermal stresses causes oxidation and thereby degradation of the oil. Contamination can also be in the form of liquids such as water droplets or fuel from heat exchangers. Currently, on-line oil condition monitoring systems use sensors that are based on eddy current, optical, capacitive to detect contamination in oil for preventive maintenance especially for aircraft engine bearings, aviation gearboxes etc. These sensors have some major drawbacks: prone to surface contamination, non-linearity, insensitive to detect extremely small particulates or false detection such as trapped bubbles. A new sensor based on platinum thin film heat transfer gauges has been developed at the University of Oxford that works on the principle of measuring the change in thermal product of the material that is in contact. The sensor is able to detect any form of contamination in oil and can be used for both off-line and on-line condition monitoring. The sensor is found to be quite sensitive and can detect extremely small concentrations of contaminants of the order 0.01 % by volume. This paper presents a detailed computational and experimental study carried out to test contamination in oil at room temperatures. The three-dimensional, time-dependent, implicit numerical simulations were carried out using the commercial computational fluid dynamics package FLUENT®. The simulation incorporates conjugate heat transfer to obtain the heating curves of the sensor with and without contamination. This was necessary to understand the range of the sensor and also to study the variations in heat transfer from the sensor to the material that is in contact with the sensor, which otherwise would not have been possible through experiments. The numerical heating curves are then compared with experimentally obtained heating curves. The comparison showed that the numerical and experimental data to agree well and are within 1 %.

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