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Optimized Mask-RCNN model for particle chain segmentation based on improved online ferrograph sensor

Friction 2024年第6期12卷 1194-1213页

作者： Shuo WANG Miao WAN Tonghai WU Zichen BAI Kunpeng WANG Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System Xi’an Jiaotong UniversityXi’an 710049China

Ferrograph-based wear debris analysis(WDA)provides significant information for wear fault analysis of mechanical equipment.After decades of offline application,this conventional technology is being driven by the online ferrograph sensor for real-time wear state monitoring.However,online ferrography has been greatly limited by the low imaging quality and segmentation accuracy of particle chains when analyzing degraded lubricant oils in practical applications.To address this issue,an integrated optimization method is developed that focuses on two aspects:the structural re-design of the online ferrograph sensor and the intelligent segmentation of particle chains.For enhancing the imaging quality of wear particles,the magnetic pole of the online ferrograph sensor is optimized to enable the imaging system directly observe wear particles without penetrating oils.Furthermore,a light source simulation model is established based on the light intensity distribution theory,and the LED installation parameters are determined for particle illumination uniformity in the online ferrograph sensor.On this basis,a Mask-RCNN-based segmentation model of particle chains is constructed by specifically establishing the region of interest(ROI)generation layer and the ROI align layer for the irregular particle morphology.With these measures,a new online ferrograph sensor is designed to enhance the image acquisition and information extraction of wear particles.For verification,the developed sensor is tested to collect particle images from different degraded oils,and the images are further handled with the Mask-RCNN-based model for particle feature extraction.Experimental results reveal that the optimized online ferrography can capture clear particle images even in highly-degraded lubricant oils,and the illumination uniformity reaches 90%in its imaging field.Most importantly,the statistical accuracy of wear particles has been improved from 67.2%to 94.1%.

关键词： wear particle analysis online ferrography particle image acquisition particle chain segmentation

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Dimensional Description of On-line wear Debris Images for wear Characterization

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Chinese Journal of Mechanical Engineering 2014年第6期27卷 1280-1286页

作者： WU Tonghai PENG Yeping DU Ying WANG Junqun Key Laboratory of Modern Design and Rotor Bearing System of Ministry Xi'an Jiaotong University School of Mechanical and Manufacturing Engineering The University of New South WalesSydney 2052Australia

As one of the most wear monitoring indicator, dimensional feature of individual particles has been studied mostly focusing on off-line analytical ferrograph. Recent development in on-line wear monitoring with wear debris images shows that merely wear debris concentration has been extracted from on-line ferrograph images. It remains a bottleneck of obtaining the dimension of on-line particles due to the low resolution, high contamination and particle’s chain pattern of an on-line image sample. In this work, statistical dimension of wear debris in on-line ferrograph images is investigated. A two-step procedure is proposed as follows. First, an on-line ferrograph image is decomposed into four component images with different frequencies. By doing this, the size of each component image is reduced by one fourth, which will increase the efficiency of subsequent processing. The low-frequency image is used for extracting the area of wear debris, and the high-frequency image is adopted for extracting contour. Second, a statistical equivalent circle dimension is constructed by equaling the overall wear debris in the image into equivalent circles referring to the extracted total area and premeter of overall wear debris. The equivalent circle dimension, reflecting the statistical dimension of larger wear debris in an on-line image, is verified by manual measurement. Consequently, two preliminary applications are carried out in gasoline engine bench tests of durability and running-in. Evidently, the equivalent circle dimension, together with the previously developed concentration index, index of particle coverage area （IPCA）, show good performances in characterizing engine wear conditions. The proposed dimensional indicator provides a new statistical feature of on-line wear particles for on-line wear monitoring. The new dimensional feature conveys profound information about wear severity.

关键词： wear particle analysis feature extraction digital image processing

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A　Development　of　A　Computer　aided　Ferrographic　System

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International Journal of Plant Engineering and Management 1996年第1期1卷 42-48页

作者： Yan Xinping Department of Marine Mechanical Engineering, Wuhan Transportation University Wuhan 430063, P.R. ChinaHu Zhengyi School of Electronics and Information Processing,Wuhan University Wuhan 430072, P.R. China

In this paper the authors suggest a computer aided system for processing data and images of ferrographic analysis by the authors. The system consists of seven modules and five databases. There is a typical wear particle library in the system. Its applications state that the analytical speed increases with this system and more information can be obtained by using this system.

关键词： Ferrography wear particle analysis data processing computer application

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