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    Wide Range Measurement of Lubricant Film Thickness Based on Ultrasonic Reflection Coefficient Phase Spectrum 

    Source: Journal of Tribology:;2019:;volume( 141 ):;issue: 003:;page 31702
    Author(s): Dou, Pan; Wu, Tonghai; Luo, Zhaopeng
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: The ultrasonic technique is very effective in measuring lubricant film thickness in a noninvasive manner. To estimate the film thickness with reflection signals, two main ultrasonic models are often applied in cases of ...
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    Ferrograph Analysis With Improved Particle Segmentation and Classification Methods 

    Source: Journal of Computing and Information Science in Engineering:;2020:;volume( 020 ):;issue: 002:;page 021001-1
    Author(s): Wang, Shuo; Wu, Tonghai; Wang, Kunpeng; Sarkodie-Gyan, Thompson
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Ferrograph analysis has been adopted over decades for determining the root causes of on-going wear faults. After decades of manual operation, this traditional technique is being driven by intelligent algorithms for automatic ...
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    Optimum Color and Contrast Enhancement for Online Ferrography Image Restoration 

    Source: Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems:;2019:;volume ( 002 ):;issue: 003:;page 31003
    Author(s): Yang, Lingfeng; Wu, Tonghai; Wang, Kunpeng; Wu, Hongkun; Kwok, Ngaiming
    Publisher: American Society of Mechanical Engineers (ASME)
    Abstract: Online ferrography, because of its nondestructive and real-time capability, has been increasingly applied in monitoring machine wear states. However, online ferrography images are usually degraded as a result of undesirable ...
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    Knowledge-Guided Convolutional Neural Network Model for Similar Three-Dimensional Wear Debris Identification With Small Number of Samples 

    Source: Journal of Tribology:;2023:;volume( 145 ):;issue: 009:;page 91105-1
    Author(s): Wang, Shuo; Shao, Tao; Wu, Tonghai; Sarkodie-Gyan, Thompson; Lei, Yaguo
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Wear debris analysis (WDA) enables the provision of essential information towards the monitoring of machine fault diagnosis and the analysis of wear mechanism. However, this experience-based technology has not yet been ...
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian