Open Access
Issue
Mechanics & Industry
Volume 23, 2022
Article Number 9
Number of page(s) 15
DOI https://doi.org/10.1051/meca/2022006
Published online 14 June 2022
  1. X. Wu, Research on Intelligent Monitoring of Aeroengine Wear State Based on Oil Analysis. Master’s Thesis, Nanjing University of Aeronautics and Astronautics, Nanjing, China, 2012 [Google Scholar]
  2. C.A. Walford, Wind turbine reliability: understanding and minimizing wind turbine operation and maintenance costs, Sandia Natl. Laborat. 5, 1–27 (2006) [Google Scholar]
  3. R. Jia, B. Ma, C. Zheng, et al., Comprehensive improvement of the sensitivity and detectability of a large-aperture electromagnetic wear particle detector, Sensors 19, 3162–3180 (2019) [CrossRef] [Google Scholar]
  4. H. Xiao, X. Wang, H. Li, J. Luo, S. Feng, An inductive debris sensor for a large-diameter lubricating oil circuit based on a high-gradient magnetic field, Appl. Sci. 9, 1546–1558 (2019) [CrossRef] [Google Scholar]
  5. L. Han, S. Feng, G. Qiu, J. Luo, H. Xiao, J. Mao, Segmentation of online ferrograph images with strong interference based on uniform discrete curvelet transformation, Sensors 19, 1546–1559 (2019) [CrossRef] [Google Scholar]
  6. X. Zhu, C. Zhong, J. Zhe, Lubricating oil conditioning sensors for online machine health monitoring – a review, Tribol. Int. 109, 473–484 (2017) [CrossRef] [Google Scholar]
  7. L. Yan, W. ShiZhu, X. YouBai, Z. Fang, Advances in research on a multi-channel on-line ferrograph, Tribol. Int. 30, 279–282 (1997) [CrossRef] [Google Scholar]
  8. S. Feng, G. Qiu, J. Luo, L. Han, J. Mao, Y. Zhang, A wear debris segmentation method for direct reflection online visual ferrography, Sensors 19, 723–734 (2019) [CrossRef] [Google Scholar]
  9. J. Reintjes, J.E. Tucker, S.E. Thomas, A. Schultz, Lasernet fines wear debris analysis technology: application to mechanical fault detection, AIP Conf. Proc. 1590–1597 (2003) [CrossRef] [Google Scholar]
  10. W. Hong, S. Wang, M.M. Tomovic, H. Liu, X. Wang, A new debris sensor based on dual excitation sources for online debris monitoring, Measur. Sci. Technol. 26, 1–12 (2015) [Google Scholar]
  11. J. Zhe, F.K. Choy, S.V. Murali, M.A. Sarangi, R. Wilfong, Oil debris detection using capacitance and ultrasonic measurements, in 2007 International Joint Tribology Conference (IJTC 2007), California, U.S.A., 22–24 October 2007; pp. 113–115 [Google Scholar]
  12. M.R. Mauntz, J. Gegner, U. Kuipers, S. Klingau, A sensor system for online oil condition monitoring of operating components, Tribology 11, 305–321 (2013) [Google Scholar]
  13. S. Murali, A.V. Jagtiani, X. Xia, J. Carletta, J. Zhe, A microfluidic Coulter counting device for metal wear detection in lubrication oil, Rev. Sci. Instrum. 80, 1–3 (2009) [Google Scholar]
  14. L. Du, J. Zhe, An integrated ultrasonic–inductive pulse sensor for wear debris detection, Smart Mater. Struct. 22, 1–9 (2012) [Google Scholar]
  15. W. Hong, W. Cai, S. Wang, M.M. Tomovic, Mechanical wear debris feature, detection, and diagnosis: a review, Chin. J. Aeron. 31, 867–882 (2018) [CrossRef] [MathSciNet] [Google Scholar]
  16. C. Yao, J. Zhao, Q. Zhang, Research on contamination monitoring technology in hydraulic fluid, Lubric. Eng. 10, 196 (2006) [Google Scholar]
  17. X. Zhu, L. Du, J. Zhe, A 3 × 3 wear debris sensor array for real time lubricant oil conditioning monitoring using synchronized sampling, Mech. Syst. Signal Process 83, 296–304 (2017) [CrossRef] [Google Scholar]
  18. M. Lukas, D.P. Anderson, T. Sebok, D. Filicky, LaserNet Fines – a new tool for the oil analysis toolbox, Practis. Oil Anal. 8, 1–11 (2002) [Google Scholar]
  19. S. Lars, M. Gerhard, R. Nils, S. Krause, OILPAS – online imaging of liquid particle suspensions – how to prevent a sudden engine breakdown, SAE Int. J. Fuels Lubric. 3, 336–345 (2010) [CrossRef] [Google Scholar]
  20. Y. Zhang, J. Mao, Y. Xie, Engine wear monitoring with OLVF, Tribol. Trans. 54, 201–207 (2011) [CrossRef] [Google Scholar]
  21. T. Wu, J. Mao, J. Wang, Y. Xie, A new on-line visual ferrograph, Tribol. Trans. 52, 623–631 (2009) [CrossRef] [Google Scholar]
  22. W. Cao, W. Chen, G. Dong, J. Wu, Y. Xie, Wear condition monitoring and working pattern recognition of piston rings and cylinder liners using on-line visual ferrograph, Tribol. Trans. 57, 690–699 (2014) [CrossRef] [Google Scholar]
  23. H. Wu, T. Wu, Y. Peng, Z. Peng, Watershed-based morphological separation of wear debris chains for on-line ferrograph analysis, Tribol. Lett. 53, 411–420 (2014) [CrossRef] [Google Scholar]
  24. Y. Hao, X. Pan, Z. Yan, Q. Chang, B. Pan, Q. Ji, Y. Shen, Recognition for particles in lubricating oil based on micro-image method, Lubric. Eng. 4, 10–16 (2017) [Google Scholar]
  25. Y. Peng, T. Wu, S. Wang, Y. Du, N. Kwok, Z. Peng, A microfluidic device for three-dimensional wear debris imaging in online condition monitoring, Proc. Inst. Mech. Eng. J 231, 965–974 (2017) [CrossRef] [Google Scholar]
  26. E.J. Fernandez-Sanchez, J. Diaz, E. Ros, Background subtraction based on color and depth using active sensors, Sensors 13, 8895–8915 (2013) [CrossRef] [PubMed] [Google Scholar]
  27. Y. Peng, T. Wu, S. Wang, N. Kwok, Z. Peng, Motion-blurred particle image restoration for on-line wear monitoring, Sensors 15, 8173–8191 (2015) [CrossRef] [PubMed] [Google Scholar]
  28. N. Otsu, A threshold selection method from gray-level histograms, IEEE Trans. Syst. Man Cybern. 9, 62–66 (1979) [CrossRef] [Google Scholar]
  29. M. Wu, L. Chen, Image recognition based on deep learning. 2015 Chinese Automation Congress (CAC2015), Wuhan, China, 27-29 November 2015; pp. 542–546 [Google Scholar]
  30. Z. Wu, H. Zuo, L. Guo, Debris micro-morphology analysis based on AI techniques, Chin. J. Aeron. 14, 30–36 (2001) [Google Scholar]
  31. Z. Wu, Research on Engine Wear Fault Diagnosis Technology Based on Debris Particle Analysis and Information Fusion. PhD Thesis, Nanjing University of Aeronautics and Astronautics, Nanjing, China, 2002 [Google Scholar]
  32. N. Dalal, B. Triggs, Histograms of oriented gradients for human detection. 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR 2005), California, U.S.A., 20-25 June 2005, pp. 886–893. [CrossRef] [Google Scholar]
  33. B. Heisele, T. Serre, S. Prentice, T. Poggio, Hierarchical classification and feature reduction for fast face detection with support vector machines, Pattern Recogn. 36, 2007–2017 (2003) [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.