A class incremental learning approach based on autoencoder without manual feature extraction for rail vehicle fault diagnosis

被引:2
作者
Kang, Jinlong [1 ]
Liu, Zhiliang [1 ,2 ]
Sun, Wenjun [1 ]
Zuo, Ming J. [1 ,3 ]
Qin, Yong [2 ]
机构
[1] Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Chengdu 611731, Sichuan, Peoples R China
[2] Beijing Jiaotong Univ, State Key Lab Rail Traff Control & Safety, Beijing 100044, Peoples R China
[3] Univ Alberta, Dept Mech Engn, Edmonton, AB T6G 1H9, Canada
来源
2018 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM-CHONGQING 2018) | 2018年
基金
加拿大自然科学与工程研究理事会; 中国国家自然科学基金;
关键词
fault diagnosis; bearing; incremental learning; autoencoder; rail vehicle; automatic feature extraction; ROTATING MACHINERY; CLASSIFICATION;
D O I
10.1109/PHM-Chongqing.2018.00014
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Traditional intelligent diagnosis methods and current popular deep learning based diagnosis methods basically adopt the approach of batch learning, which may waste time and computing resources since they need to discard the previous learned model and retrain a new model based on the newly added data and prior data. Moreover, manual feature extraction is often a necessary step for intelligent diagnosis, and such a process relies much on prior knowledge. To solve the above mentioned problems, this paper proposes a fault diagnosis method based on class incremental learning without manual feature extraction. Based on denoising autoencoder, the proposed method obtains the autoencoders using the raw data acquired for each health state. In the class incremental learning process, only the autoencoder of new health state need to be trained while the former trained autoencoders are retained. Test data is classified according to the minimal reconstruction error calculated through the autoencoders. At the end of this paper, the proposed method is validated through vibration data of rolling bearings for rail vehicle. The experimental results show that the presented method is effective.
引用
收藏
页码:45 / 49
页数:5
相关论文
共 17 条
  • [1] Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine
    Abbasion, S.
    Rafsanjani, A.
    Farshidianfar, A.
    Irani, N.
    [J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2007, 21 (07) : 2933 - 2945
  • [2] Cyclic spectral analysis of rolling-element bearing signals: Facts and fictions
    Antoni, J.
    [J]. JOURNAL OF SOUND AND VIBRATION, 2007, 304 (3-5) : 497 - 529
  • [3] LIBSVM: A Library for Support Vector Machines
    Chang, Chih-Chung
    Lin, Chih-Jen
    [J]. ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY, 2011, 2 (03)
  • [4] Geng X., 2009, Encyclopedia of Biometrics, P731, DOI DOI 10.1007/978-0-387-73003-5_304
  • [5] Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data
    Jia, Feng
    Lei, Yaguo
    Lin, Jing
    Zhou, Xin
    Lu, Na
    [J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2016, 72-73 : 303 - 315
  • [6] Multi-class classification via heterogeneous ensemble of one-class classifiers
    Kang, Seokho
    Cho, Sungzoon
    Rang, Pilsung
    [J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2015, 43 : 35 - 43
  • [7] Diversity measures for one-class classifier ensembles
    Krawczyk, Bartosz
    Wozniak, Michal
    [J]. NEUROCOMPUTING, 2014, 126 : 36 - 44
  • [8] An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data
    Lei, Yaguo
    Jia, Feng
    Lin, Jing
    Xing, Saibo
    Ding, Steven X.
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2016, 63 (05) : 3137 - 3147
  • [9] Neural-network-based motor rolling bearing fault diagnosis
    Li, B
    Chow, MY
    Tipsuwan, Y
    Hung, JC
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2000, 47 (05) : 1060 - 1069
  • [10] Artificial intelligence for fault diagnosis of rotating machinery: A review
    Liu, Ruonan
    Yang, Boyuan
    Zio, Enrico
    Chen, Xuefeng
    [J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2018, 108 : 33 - 47