Evaluation of product performance degradation based on outlier detection

被引：0
作者：
Wang Y. [1 ]
Li Y. [1 ]
Zhao Z. [1 ]
机构：
[1] Department of Industrial Engineering, School of Mines, China University of Mining and Technology, Xuzhou
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2021年 / 27卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Correlation of performance parameters; Degradation assessment; Mechanical product; Neighborhood rough set; Outlier detection;
D O I：
10.13196/j.cims.2021.04.008
中图分类号：
学科分类号：
摘要：
Product performance degradation assessment is the key stage of product health operation and residual life assessment. An assessment approach of product performance degradation based on outlier detection was proposed to achieve fast and real-time assessment. The correlation between performance monitoring parameters was determined based on the neighborhood rough set theory and functional and structural relevance. Then, the degradation index of performance parameters was constructed according to the knowledge contained in the correlations among them. Considering that the performance state representation of product function module was impacted by multiple performance parameters, the performance parameters were weighted by principal component analysis. The degradation score of a product function module was obtained by considering multiple performance parameters. Taking the wind turbine as an example, the effectiveness of the proposed approach was verified by comparative analysis. © 2021, Editorial Department of CIMS. All right reserved.
引用
收藏
页码：1040 / 1051
页数：11
相关论文
共 32 条
  • [11] MA Meng, CHEN Xuefeng, ZHANG Xiaoli, Et al., Locally linear embedding on Grassmann manifold for performance degradation assessment of bearings, IEEE Transactions on Reliability, 66, 2, pp. 467-477, (2017)
  • [12] LAPIRA E, BRISSET D, ARDAKANI H D, Et al., Wind turbine performance assessment using multi-regime modeling approach, Renewable Energy, 45, pp. 86-95, (2012)
  • [13] WANG Zheng, MA Hongzhan, CHEN Hansi, Et al., Performance degradation assessment of rolling bearing based on convolutional neural network and deep long-short term memory network, International Journal of Production Research, 58, 13, pp. 3931-3943, (2019)
  • [14] DENG Chao, WANG Yuanhang, WU Jun, Et al., Performance degradation modelling and health state evaluation for mechanical and electrical equipment, Computer Integrated Manufacturing Systems, 24, 9, pp. 2279-2287, (2018)
  • [15] ZHANG Ying, WANG Anchen, ZUO Hongfu, Roller bearing performance degradation assessment based on fusion of multiple features of electrostatic sensors, Sensors, 19, 4, (2019)
  • [16] DU Haitao, CHU Xuening, MA Hongzhan, Product degradation risk analysis based on performance data, Machinery Design & Manufacture, 8, pp. 258-260, (2019)
  • [17] WEI Pangshi, WANG Hongjun, Evaluation method of spindle performance degradation based on VMD and random forests, The Journal of Engineering, 23, pp. 8862-8866, (2019)
  • [18] JIANG Feng, CHEN Yunming, Outlier detection based on granular computing and rough set theory, Proceedings of the International Conference on Rough Sets & Current Trends in Computing, (2008)
  • [19] LIU Jing, DENG Huifang, Outlier detection on uncertain data based on local information, Knowledge-Based Systems, 51, pp. 60-71, (2013)
  • [20] YUAN Zhong, ZHANG Xianyong, FENG Shan, Hybrid data-driven outlier detection based on neighborhood information entropy and its developmental measures, Expert Systems with Applications, 112, pp. 243-257, (2018)
1234