Engine wear fault diagnosis based on improved semi-supervised fuzzy c-means clustering

被引:8
|
作者
Xu, Chao [1 ]
Zhang, Peilin [1 ]
Ren, Guoquan [1 ]
Fu, Jianping [1 ]
机构
[1] Department 1st, Ordnance Engineering College, Shijiazhuang 050003, China
关键词
Regression analysis - Failure analysis - Matrix algebra - Sampling - Fault detection - Fuzzy clustering;
D O I
10.3901/JME.2011.17.055
中图分类号
学科分类号
摘要
A improved semi-supervised fuzzy c-means clustering algorithm (ISS-FCM) is proposed to diagnose engine wear faults with small oil samples. An optimized objective function, which is defined through introducing average distance measure between unlabeled samples and training samples with weighting values, is used to conduct the clustering process. To avoid local extrema originating from initialing partition matrix randomly, the training samples are utilized in partition matrix initialing work. By reason that engine wear condition can not be effectively characterized by original oil data with unobvious cluster trendency, Autoregression (AR) model is used to abstract the residual variance features from oil data. The atomic emission spectrometric oil data of engine bench test are analyzed with the proposed method. The cylinder scoring and bushing ablating faults are diagnosed successfully. Experimental results demonstrate the validity of the presented method in the field of engine wear fault diagnosis. © 2011 Journal of Mechanical Engineering.
引用
收藏
页码:55 / 60
相关论文
共 50 条
  • [31] Effects of Semi-supervised Learning on Rough Membership C-Means Clustering
    Shimizu, Takeaki
    Ubukata, Seiki
    Notsu, Akira
    Honda, Katsuhiro
    [J]. 2019 INTERNATIONAL CONFERENCE ON FUZZY THEORY AND ITS APPLICATIONS (IFUZZY), 2019, : 15 - 20
  • [32] Effects of Semi-supervised Learning on Rough Set-Based C-Means Clustering
    Ubukata, Seiki
    Shimizu, Takeaki
    Notsu, Akira
    Honda, Katsuhiro
    [J]. 2018 INTERNATIONAL CONFERENCE ON FUZZY THEORY AND ITS APPLICATIONS (IFUZZY), 2018, : 12 - 17
  • [33] Semi-Supervised Hard and Fuzzy c-Means with Assignment Prototype Term
    Hamasuna, Yukihiro
    Endo, Yasunori
    [J]. MODELING DECISIONS FOR ARTIFICIAL INTELLIGENCE, MDAI 2014, 2014, 8825 : 135 - 144
  • [34] Text Categorization using the Semi-Supervised Fuzzy c-Means Algorithm
    Benkhalifa, M
    Bensaid, A
    [J]. 18TH INTERNATIONAL CONFERENCE OF THE NORTH AMERICAN FUZZY INFORMATION PROCESSING SOCIETY - NAFIPS, 1999, : 561 - 565
  • [35] An Improved Fuzzy C-means Clustering Algorithm for Transformer Fault
    Tang, Songping
    Peng, Gang
    Zhong, Zhenxin
    [J]. 2016 CHINA INTERNATIONAL CONFERENCE ON ELECTRICITY DISTRIBUTION (CICED), 2016,
  • [36] A semi-supervised Intrusion Detection System using active learning SVM and fuzzy c-means clustering
    Kumari, Valli V.
    Varma, Ravi Kiran P.
    [J]. 2017 INTERNATIONAL CONFERENCE ON I-SMAC (IOT IN SOCIAL, MOBILE, ANALYTICS AND CLOUD) (I-SMAC), 2017, : 481 - 485
  • [37] Semi-supervised Method with Spatial Weights based Possibilistic Fuzzy c-Means Clustering for Land-cover Classification
    Dinh-Sinh Mai
    Long Thanh Ngo
    [J]. PROCEEDINGS OF 2018 5TH NAFOSTED CONFERENCE ON INFORMATION AND COMPUTER SCIENCE (NICS 2018), 2018, : 406 - 411
  • [38] Mechanical Fault Diagnosis Method Based on LMD Shannon Entropy and Improved Fuzzy C-means Clustering
    Dong, Shaojiang
    Xu, Xiangyang
    Luo, Jiayuan
    [J]. INTERNATIONAL JOURNAL OF ACOUSTICS AND VIBRATION, 2017, 22 (02): : 211 - 217
  • [39] Cutset-type Possibilistic C-means Clustering Algorithms Based on Semi-supervised Information
    Fan Jiulun
    Gao Mengfei
    Yu Haiyan
    Chen Binbin
    [J]. JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY, 2021, 43 (08) : 2378 - 2385
  • [40] Semi-supervised possibilistic c-means clustering algorithm based on feature weights for imbalanced data
    Yu, Haiyan
    Xu, Xiaoyu
    Li, Honglei
    Wu, Yuting
    Lei, Bo
    [J]. KNOWLEDGE-BASED SYSTEMS, 2024, 286