ASTM-D7684-11 Compliant Computer Aided Interactive Wear Debris Particle Analysis for On-Site Condition Monitoring, Diagnostics and Prognostics

2017 ◽  
Author(s):  
Violet Leavers
Keyword(s):  
Condition Monitoring ◽  
Wear Debris ◽  
Particle Analysis ◽  
Site Condition ◽  
Debris Particle ◽  
Computer Aided

Establishment of the Accuracy and Consistency of Using Automatic Image Analysis to Classify Wear Debris Particles

2005 ◽  
Author(s):  
V. F. Leavers ◽  
M. D. Hanlon
Keyword(s):  
Image Analysis ◽  
Wear Debris ◽  
Failure Modes ◽  
Software Tool ◽  
Particle Analysis ◽  
Automatic Image Analysis ◽  
Debris Particle ◽  
Debris Particles ◽  
Particle Images ◽  
Engine Components

Wear debris particle analysis is an equipment health monitoring technique used to identify possible failure modes in various engine components. One of the first stages in the analysis involves the examination under a microscope of particles collected from the component’s lubrication system on magnetic drain plugs and filters. However, the subjectivity of technicians’ judgements means that diagnosis may not be consistent between technicians. A software tool capable of automatically classifying the images of wear debris particles has been developed and tested using an 800-image database. It is shown that using automatic image analysis for the classification of wear debris particle images is more consistent, accurate and informative when compared to the classifications assigned by wear debris experts.

Online condition monitoring of misaligned meshing gears using wear debris and oil quality sensors

2018 ◽  
Vol 70 (4) ◽  
pp. 645-655 ◽  
Author(s):  
Paras Kumar ◽  
Harish Hirani ◽  
Atul Kumar Agrawal
Keyword(s):  
Condition Monitoring ◽  
Wear Debris ◽  
Oil Quality ◽  
Spur Gear ◽  
Metallic Particle ◽  
Sem Images ◽  
Content Type ◽  
Moisture Sensor ◽  
Laser Alignment ◽  
Online Sensors

Purpose This paper aims to investigate the effect of misalignment on wear of spur gears and on oil degradation using online sensors. Design/methodology/approach The misalignment effect on gears is created through a self-alignment bearing, and is measured using laser alignment system. Several online sensors such as Fe-concentration sensor, moisture sensor, oil condition sensor, oil temperature sensor and metallic particle sensor are installed in the gear test rig to monitor lubricant quality and wear debris in real time to assess gearbox failure. Findings Offset and angular misalignments are detected in both vertical and horizontal planes. The failure of misaligned gear is observed at both the ends and on both the surfaces of the gear teeth. Larger-size ferrous and non-ferrous particles are traced by metallic particle sensor due to gear and seal wear caused by misalignment. Scanning electron microscope (SEM) images examine chuck, spherical and flat platelet particles, and confirm the presence of fatigue (pitting) and adhesion (scuffing) wear mechanism. Energy-dispersive X-ray spectroscopy analysis of SEM particles traces carbon (C) and iron (Fe) elements due to gear failure. Originality/value Gear misalignment is one of the major causes of gearbox failure and the lubricant analysis is as important as wear debris analysis. A reliable online gearbox condition monitoring system is developed by integrating wear and oil analyses for misaligned spur gear pair in contact.

A novel method for the prediction of functional biological activity of polyethylene wear debris

2001 ◽  
Vol 215 (2) ◽  
pp. 127-132 ◽  
Author(s):  
J Fisher ◽  
J Bell ◽  
P S M Barbour ◽  
J L Tipper ◽  
J B Mattews ◽  
...  
Keyword(s):  
Biological Activity ◽  
Wear Debris ◽  
Polyethylene Wear ◽  
Particle Analysis ◽  
Wear Volume ◽  
Volumetric Concentration ◽  
Particle Size Range ◽  
Comparative Performance

The comparative performance of artificial hip joints has been extensively investigated in vitro through measurements of wear volumes. in vivo a major cause of long-term failure is wear-debris-induced osteolysis. These adverse biological reactions are not simply dependent on wear volume, but are also controlled by the size and volumetric concentration of the debris. A novel model is presented which predicts functional biological activity; this is determined by integrating the product of the biological activity function and the volumetric concentration function with the wear volume over the whole particle size range. This model combines conventional wear volume measurements with particle analysis and the output from in vitro cell culture studies to provide a new indicator of osteolytic potential. The application of the model is demonstrated through comparison of the functional biological activity of wear debris from polyethylene acetabular cups articulating under three different conditions in a hip joint simulator.

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.

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