OILPAS - Online Imaging of Liquid-Particle-Suspensions - How to Prevent a Sudden Engine Breakdown

Schomann, Lars; Matz, Gerhard; Röbken, Nils; Krause, Sven

doi:10.4271/2010-01-1528

Cited by 3 publications

(5 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the used system, it is possible to detect particles in the size range from 1 to 500 μm. In the work of Schomann et al, the performance of the OILPAS system was demonstrated using a simplified test set‐up consisting of a measurement beaker, some oil and the OILPAS system. In order to validate the particle size estimation of the system, iron powder with an average particle size of 4 μm was dispersed homogeneously in fresh lubricating oil.…”

Section: Methodsmentioning

confidence: 99%

“…For the online detection and characterisation of wear particles in the lubricating oil, the online imaging of particle suspensions (OILPAS) system from Innosiris GmbH was used, which was first introduced by Schomann et al The working principle is based on an optical method, which is schematically illustrated in Figure . A continuous flow of lubricating oil is drawn from the outlet at the bottom of the oil chamber, directed through a thin transparent flow cell, and then returned back at the top of the oil chamber.…”

Section: Methodsmentioning

confidence: 99%

“…However, optical techniques using LED or laser beam in combination with a charge‐coupled device (CCD) sensor have shown some promising potential . With this method, light is directed through a flow cell containing the lubricating oil and imaged onto a CCD sensor.…”

Section: Introductionmentioning

confidence: 99%

“…[5][6][7] In recent years, much effort was put into the development of in-line or on-line solutions operating in real time and into the automation of the workflow, avoiding frequent oil sampling and making the application as well as the analysis more straightforward. [8][9][10][11] Still, there is no universal measurement technique providing all relevant wear debris characteristics for effectively monitoring lubricated systems. 6 However, optical techniques using LED or laser beam in combination with a charge-coupled device (CCD) sensor have shown some promising potential.…”

Section: Introductionmentioning

confidence: 99%

“…6 However, optical techniques using LED or laser beam in combination with a charge-coupled device (CCD) sensor have shown some promising potential. 8,10,12,13 With this method, light is directed through a flow cell containing the lubricating oil and imaged onto a CCD sensor. When particles pass through the flow cell and interrupt the light beam, a decrease of incident light is registered.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Characterising the friction and wear behaviour of lubricated metal‐metal pairings with an optical online particle detection system

Krasmik

Röbken²,

Martin³

et al. 2017

Lubrication Science

Self Cite

View full text Add to dashboard Cite

In this study, a simple ball‐on‐prism test set‐up is combined with an optical online wear particle detection system. The set‐up is used to study the friction and wear characteristics of some exemplary oil lubricated metal‐metal pairings. In addition to the time‐resolved evolutions of the coefficient of friction and the volumetric wear, the number of wear particles, the particle sizes and the particle shapes are obtained during the test by using an online particle detection system. The number of small (<5 μm) wear particles increases steadily during the tests, while the number of larger (>10 μm) particles only increases during the running‐in period and remains almost constant afterwards. Simultaneous drops in the curves for the coefficient of friction and the trend curves for the number of wear particles indicate a possible interrelation. The individual curves for the different particle sizes suggest that mainly small particles (<5 μm) contribute to that relation. © 2017 John Wiley & Sons, Ltd.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Characterising the friction and wear behaviour of lubricated metal‐metal pairings with an optical online particle detection system

Krasmik

Röbken²,

Martin³

et al. 2017

Lubrication Science

Self Cite

View full text Add to dashboard Cite

show abstract

Online monitoring of oil wear debris image based on CNN

Wang

Zuo

Liu

et al. 2022

Mechanics & Industry

View full text Add to dashboard Cite

Image monitoring of oil wear particles is currently only applicable to microflows and is susceptible to bubble interference. This paper develops an optical oil-monitoring system that can be used for large-diameter pipes with high flow rates. A shallow and wide observation cell with an equivalent diameter of Φ5 mm is designed to allow a theoretical maximum monitoring flow rate of about 8 L/min, which is a significant improvement over current image monitoring of generally less than Φ2 mm pipes. A low-magnification (0.8X – 5X) stereoscopic microscope head is used to improve the field of view and depth of field, and a high-speed camera is used to increase the flow range that can be monitored. A set of experimental platforms is also constructed to produce bubbles and wear particles separately. Images of the wear particles and bubbles are then collected for subsequent training and verification of image classification algorithms. A motion object extraction algorithm based on background differences and the Otsu method is used to extract debris and bubble images, and a convolutional neural network (CNN) algorithm is used to distinguish between bubbles and debris. Compared with the traditional morphological feature extraction method, histogram of oriented gradient (HOG) feature extraction method, k-nearest neighbor (KNN) classification algorithm, and support vector machine (SVM) classification algorithm, the CNN algorithm eliminates the tedious process of feature extraction and selection, and has better classification results. The experimental results show that the system can effectively collect wear particle and bubble images and classify them, and the classification accuracy can reach 91.8%.

show abstract

Development of an On-Line System for Oil Void Fraction Measurements

Makita

Ikeda

2018

SAE Technical Paper Series

View full text Add to dashboard Cite

OILPAS - Online Imaging of Liquid-Particle-Suspensions - How to Prevent a Sudden Engine Breakdown

Cited by 3 publications

References 1 publication

Characterising the friction and wear behaviour of lubricated metal‐metal pairings with an optical online particle detection system

Characterising the friction and wear behaviour of lubricated metal‐metal pairings with an optical online particle detection system

Online monitoring of oil wear debris image based on CNN

Development of an On-Line System for Oil Void Fraction Measurements

Contact Info

Product

Resources

About