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第 1 期                    杨智宏, 等: 基于Mask R-CNN网络的磨损颗粒智能识别与应用                                  113


                     0  39  1  1  0  0  0  0  0  0                 depending on debris micrology shape analysis[J]. Journal of Basic
                                                  35
                       0  27  0  0  1  0  0  0  0
                     1                                             Science and Engineering, 2000, 8(4): 431–437 (in Chinese) [李艳军,
                       0  0  32  0  0  0  0  0  0  30
                     2                                             左洪福, 吴振锋. 基于磨粒显微形态分析的发动机磨损状态监测
                       0  0  0  25  0  0  0  0  0  25
                     5 True label  4  0 0  2 0  2 0  1 0  30  25  1 0  0 1  0 1  20  431–437]. doi: 10.3969/j.issn.1005-0930.2000.04.013.
                     3                                             与 故 障 诊 断 技 术 [J].  应 用 基 础 与 工 程 科 学 学 报 ,  2000,  8(4):
                                     2
                                  0
                       0  0  0  1  0  1  37  0  0  15          [  7  ]  Zhou  Xincong,  Xiao  Hanliang,  Yan  Xinping,  et  al.  A  new
                     6                            10               comprehensive feature parameter for wear particle image analysis[J].
                       0  0  0  0  0  0  0  35  0
                     7                                             Tribology, 2002, 22(2): 138–141 (in Chinese) [周新聪, 萧汉梁, 严
                                                  5
                       0  0  0  0  0  0  0  1  32
                     8                                             新平, 等. 一种新的磨粒图像特征参数[J]. 摩擦学学报, 2002,
                                                  0
                       0  1  2  3  4  5  6  7  8                   22(2): 138–141]. doi: 10.3321/j.issn:1004-0595.2002.02.014.
                             Predicted label
                                                               [  8  ]  Fan  Hongwei,  Ding  Xiao,  Gao  Shuoqi,  et  al.  Abrasive  particle

                 Fig. 9    Confusion matrix for the prediction result
                                                                   feature  extraction  in  ferrography  based  on  binary  correction
                              of wear index
                       图 9    磨损指数预测的混淆矩阵                          ofinverse grayscale image[J]. Lubrication Engineering, 2019, 44(6):
                                                                   66–71 (in Chinese) [樊红卫, 丁骁, 高烁琪, 等. 基于反相灰度图二
            现磨粒的像素级识别,通过对识别结果处理可数字化                                值化修正的铁谱图像磨粒特征提取[J]. 润滑与密封, 2019, 44(6):
            表征出每个颗粒的具体面积和尺寸,解决了磨粒分析                                66–71]. doi: 10.3969/j.issn.0254-0150.2019.06.010.
                                                               [  9  ]  Kong Xiangxing, Shao Tao. Wear debris material recognition based
            中难定量的问题,对磨粒分析的智能化、自动化有实
                                                                   on color feature extraction[J]. Lubrication Engineering, 2020, 45(5):
            际的指导意义.
                                                                   79–85 (in Chinese) [孔祥兴, 邵涛. 基于颜色特征提取的磨粒材质
            参 考 文 献                                                识别研究[J]. 润滑与密封, 2020, 45(5): 79–85]. doi: 10.3969/j.issn.
                                                                   0254-0150.2020.05.013.
            [  1  ]  Feng  Wei,  Li  Qiuqiu,  He  Shizhong.  Method  and  application  of
                                                               [10]  Peng Yeping, Wu Tonghai, Cao Guangzhong, et al. A hybrid search-
                 particle classification based on analytical ferrography[J]. Lubrication
                                                                   tree  discriminant  technique  for  multivariate  wear  debris
                 Engineering, 2015(12): 125–130 (in Chinese) [冯伟, 李秋秋, 贺石
                                                                   classification[J]. Wear, 2017, 392/393: 152–158. doi: 10.1016/j.wear.
                 中. 基于铁谱分析的颗粒分类识别方法与应用[J]. 润滑与密封,
                                                                   2017.09.022.
                 2015(12): 125–130]. doi: 10.3969/j.issn.0254-0150.2015.12.024.
                                                               [11]  Peerawatt  Nunthavarawong.  Comparative  study  on  wear  particle
            [  2  ]  Liang  Hua,  Yang  Mingzhong.  Wear  particle  identification  expert
                                                                   colour classifications using various machine learning algorithms[J].
                 system based on neural network [C] / / China Society of Mechanical
                                                                   Applied  Mechanics  and  Materials,  2014,  619:  347–351.  doi:
                 Engineering  94  National  Ferrography  Technology  Conference,
                                                                   10.4028/www.scientific.net/AMM.619.347.
                 1994(in Chinese) [梁华, 杨明忠. 基于神经网络的磨粒识别专家
                                                               [12]  LeCun  Y,  Bengio  Y,  Hinton  G.  Deep  learning[J].  Nature,  2015,
                 系统[C]//中国机械工程学会94全国铁谱技术会议, 1994].
                                                                   521(7553): 436–444. doi: 10.1038/nature14539.
            [  3  ]  Wang  Weihua,  Yin  Yonghui,  Wang  Chengtao.  Wear  debris
                                                               [13]  Zheng  Yuanpan,  Li  Guangyang,  Li  Ye.  Survey  of  Application  of
                 recognition  system  based  on  radius  basis  function  network[J].
                                                                   Deep Learning in Image Recognition[J]. Computer Engineering and
                 Tribology, 2003, (4): 77–80 (in Chinese) [王伟华, 殷勇辉, 王成焘.  Applications, 2019, 55(12): 20–36 (in Chinese) [郑远攀, 李广阳, 李
                 基于径向基函数神经网络的磨粒识别系统[J]. 摩擦学学报, 2003,               晔. 深度学习在图像识别中的应用研究综述[J]. 计算机工程与应
                 (4): 77–80]. doi: 10.3321/j.issn:1004-0595.2003.04.017.  用, 2019, 55(12): 20–36]. doi: 10.3778/j.issn.1002-8331.1903-0031.
            [  4  ]  Shi  Hong,  Zhang  Shuai,  Li  Ang.  Research  on  wear  particle  [14]  An Chao, Wei Haijun, Liu Hong, et al. Intelligent identification of
                 recognition  based  on  self-adapting  support  vector  machine[J].
                                                                   ferrographic wear particles based on convolution neural network[J].
                 Science  Technology  and  Engineering,  2012,  12(32):  8543–8546,  Modern Manufacturing Engineering, 2019, 7: 111–114 (in Chinese)
                 8552 (in Chinese) [石宏, 张帅, 李昂. 基于自适应支持向量机的磨       [安超, 魏海军, 刘竑, 等. 基于卷积神经网络的铁谱磨粒智能识别
                 粒识别技术研究[J]. 科学技术与工程, 2012, 12(32): 8543–8546,     研究[J]. 现代制造工程, 2019, 7: 111–114]. doi: 10.16731/j.cnki.1671-
                 8552]. doi: 10.3969/j.issn.1671-1815.2012.32.013.  3133.2019.07.018.
            [  5  ]  Cui Hai, Kang Jianli. Research on wear debris recognition algorithm  [15]  Peng Y, Cai J, Wu T, et al. A hybrid convolutional neural network
                 based  on  IFNN[J].  Journal  of  Zhejiang  Water  Conservancy  and  for intelligent wear particle classification[J]. Tribology International,
                 Hydropower College, 2016, 28(3): 77–80 (in Chinese) [崔海, 康剑  2019, 138: 166–173. doi: 10.1016/j.triboint.2019.05.029.
                 莉. 基于片相似各项异性扩散的BP神经网络的磨粒识别研究[J].              [16]  Wang  S,  Wu  T  H,  Shao  T,  et  al.  Integrated  model  of  BP  neural
                 浙江水利水电学院学报, 2016, 28(3): 77–80]. doi: 10.3969/j.issn.  network  and  CNN  algorithm  for  automatic  wear  debris
                 1008-536X.2016.03.016.                            classification[J].  Wear,  2019,  426-427:  1761–1770.  doi:  10.1016/
            [  6  ]  Li Yanjun, Zuo Hongfu, Wu Zhenfeng. Failure of engine diagnostics  j.wear.2018.12.087.
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