2014
DOI: 10.1007/s11249-014-0340-1
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3D Surface Characterizations of Wear Particles Generated from Lubricated Regular Concave Cylinder Liners

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Cited by 16 publications
(11 citation statements)
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“…Ville and Né lias (1999) conducted the experiments using several typical particles to study their influence on rolling contact. The threedimensional morphology of the debris particles was extracted in an effort to determine the operating conditions of the equipment (Guo et al, 2014;Rao et al, 2019;Yuan et al, 2007). Vibration and acoustic emission are common tools for monitoring contaminated bearings.…”
Section: Introductionmentioning
confidence: 99%
“…Ville and Né lias (1999) conducted the experiments using several typical particles to study their influence on rolling contact. The threedimensional morphology of the debris particles was extracted in an effort to determine the operating conditions of the equipment (Guo et al, 2014;Rao et al, 2019;Yuan et al, 2007). Vibration and acoustic emission are common tools for monitoring contaminated bearings.…”
Section: Introductionmentioning
confidence: 99%
“…The excessive wear of some core parts of machineries, especially for large-scale mechanical equipment, may lead to a poor mechanical performance, which in turn causes enormous economic losses. Therefore, for online monitoring of the wear condition of machineries in order to prevent serious malfunctions, the wear particle detector has demonstrated its value [1,2,3]. To date, wear particle detectors with different physical principles, including optics, ultrasonics, electronics, and imaging, have been proposed, and the characteristics of the various kinds of sensors are listed in Reference [4].…”
Section: Introductionmentioning
confidence: 99%
“…Classification of wear particles is an important task in wear particle analysis, and high identification rates are conducive to reliably monitor the machine condition and objectively assess the wear mechanism [25]. For instance, Stachowiak et al developed an automated classification system based on surface textures and shape features of the wear particles, and the results showed that the texture-based classification system was efficient and accurate [26].…”
Section: Introductionmentioning
confidence: 99%