Three-dimensional reconstruction of wear particle surface based on photometric stereo

被引:37
作者
Wang, Shuo [1 ,2 ]
Wu, Tonghai [1 ,2 ]
Yang, Lingfeng [1 ,2 ]
Kwok, Ngaiming [3 ]
Sarkodie-Gyan, Thompson [4 ]
机构
[1] Xi An Jiao Tong Univ, Sch Mech Engn, Xian 710049, Shaanxi, Peoples R China
[2] Xi An Jiao Tong Univ, Key Lab Educ, Minist Modern Design & Rotor Bearing Syst, Xian 710049, Shaanxi, Peoples R China
[3] Univ New South Wales, Sch Mech & Mfg Engn, Sydney, NSW 2052, Australia
[4] Univ Texas El Paso, Dept Elect & Comp Engn, Lab Ind Metrol & Automat, El Paso, TX 79968 USA
基金
美国国家科学基金会;
关键词
Ferrography; Wear particle analysis; Photometric stereo; Three-dimensional reconstruction; SELECTION METHOD; IMAGE; FEATURES; CLASSIFICATION; TOPOGRAPHIES;
D O I
10.1016/j.measurement.2018.10.032
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Ferrography has emerged as a significant candidate for the provision of relevant information for the determination of the status of machine wear. However, this conventional methodology provides only marginal results due to its inability to provide 3-dimensinal (3D) surface information. The authors of this paper have developed a methodology based on photometric-stereo towards the enhancement of the capabilities of ferrography. This enhanced and innovative methodology consists of three main components, the multi-illumination image acquisition, the wear particle extraction, and the 3D surface reconstruction. The methodology ensures the reliable and efficient extraction of the wear particles surface topographies for further feature-based wear particle identification. The performance of this methodology has been compared with results observed from the laser scanning confocal microscopy. The outcome of this comparison has depicted that the methodology involving this new low-cost ferrography system exhibits very high accuracy for the 3D surface feature extraction of wear particles. (C) 2018 Elsevier Ltd. All rights reserved.
引用
收藏
页码:350 / 360
页数:11
相关论文
共 30 条
[11]   Motion-Blurred Particle Image Restoration for On-Line Wear Monitoring [J].
Peng, Yeping ;
Wu, Tonghai ;
Wang, Shuo ;
Kwok, Ngaiming ;
Peng, Zhongxiao .
SENSORS, 2015, 15 (04) :8173-8191
[12]   Wear particle analysis - utilization of quantitative computer image analysis: A review [J].
Raadnui, S .
TRIBOLOGY INTERNATIONAL, 2005, 38 (10) :871-878
[13]   PICTURE THRESHOLDING USING AN ITERATIVE SELECTION METHOD [J].
RIDLER, TW ;
CALVARD, S .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS, 1978, 8 (08) :630-632
[14]   Ferrography - then and now [J].
Roylance, BJ .
TRIBOLOGY INTERNATIONAL, 2005, 38 (10) :857-862
[15]   Threshold selection using Renyi's entropy [J].
Sahoo, P ;
Wilkins, C ;
Yeager, J .
PATTERN RECOGNITION, 1997, 30 (01) :71-84
[16]   Automated classification of wear particles based on their surface texture and shape features [J].
Stachowla, Gwidon P. ;
Stachowiak, Gwidon W. ;
Podsladlo, Pawel .
TRIBOLOGY INTERNATIONAL, 2008, 41 (01) :34-43
[17]   A new approach to numerical characterisation of wear particle surfaces in three-dimensions for wear study [J].
Tian, Y. ;
Wang, J. ;
Peng, Z. ;
Jiang, X. .
WEAR, 2012, 282 :59-68
[18]   A non-reference evaluation method for edge detection of wear particles in ferrograph images [J].
Wang, Jingqiu ;
Bi, Ju ;
Wang, Lianjun ;
Wang, Xiaolei .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2018, 100 :863-876
[19]   Investigation of the nano-mechanical properties and surface topographies of wear particles and human knee cartilages [J].
Wang, Meiling ;
Peng, Zhongxiao .
WEAR, 2015, 324 :74-79
[20]   Modeling Wear State Evolution Using Real-Time Wear Debris Features [J].
Wang, Shuo ;
Wu, Tonghai ;
Wu, Hongkun ;
Kwok, Ngaiming .
TRIBOLOGY TRANSACTIONS, 2017, 60 (06) :1022-1032