IDENTIFICATION OF MACHINE FAULT CONDITIONS

被引:0
|
作者
Tkac, Jozef [1 ]
Macalak, Jaroslav [1 ]
机构
[1] Univ Trencin, Fac Mechatron, Trencin 91150, Slovakia
关键词
Identification; vibration analysis;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Vibration analysis has been used for the identification of machine fault conditions. The specific characteristics of the vibration spectrum are associated with common fault conditions. The spectral components reflect the rotational frequency in the spectrum and indicate the degree of imbalance and fault conditions. This paper demonstrates the presence of a machine defect and an identification of a vibration feature.
引用
收藏
页码:51 / 58
页数:8
相关论文
共 50 条
  • [31] Cable incipient fault identification using restricted Boltzmann machine and stacked autoencoder
    Wang, Ying
    Lu, Hong
    Xiao, Xianyong
    Yang, Xiaomei
    Zhang, Wenhai
    IET GENERATION TRANSMISSION & DISTRIBUTION, 2020, 14 (07) : 1242 - 1250
  • [32] Fault Identification & Classification in an Interconnected Power System Network using Machine Learning
    Shrinivas, Makizh G.
    Saravanan, Akhil
    Pradeep, Gaajula Vishnu
    Bharathvaj, S.
    Thampatty, K. C. Sindhu
    PROCEEDINGS OF 2021 5TH INTERNATIONAL CONFERENCE ON CONDITION ASSESSMENT TECHNIQUES IN ELECTRICAL SYSTEMS (IEEE CATCON 2021), 2021, : 66 - 71
  • [33] Simulation of blind identification with ARMA model and its application to machine fault diagnosis
    Department of Precision Instruments and Mechanology, Tsinghua University, Beijing 100084, China
    不详
    不详
    J Vib Shock, 2006, 1 (122-125):
  • [34] Fault Identification and Relay Protection of Hybrid Microgrid Using Blockchain and Machine Learning
    Liu, Yan
    Gu, Yali
    Yang, Di
    Wang, Jingmin
    IETE JOURNAL OF RESEARCH, 2023, 69 (11)
  • [35] Experimental and theoretical application of fault identification measures of accuracy in rotating machine diagnostics
    Vania, A
    Pennacchi, P
    MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2004, 18 (02) : 329 - 352
  • [36] A New Algorithm for Busbar Fault Zone Identification Using Relevance Vector Machine
    Chothani, Nilesh G.
    Bhalja, Bhavesh R.
    ELECTRIC POWER COMPONENTS AND SYSTEMS, 2016, 44 (02) : 193 - 205
  • [37] Early Fault Detection of Machine Tools Based on Deep Learning and Dynamic Identification
    Luo, Bo
    Wang, Haoting
    Liu, Hongqi
    Li, Bin
    Peng, Fangyu
    IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2019, 66 (01) : 509 - 518
  • [38] Fault Identification of Power Transformers Using Proximal Support Vector Machine (PSVM)
    Malik, Hasmat
    Mishra, Sukumar
    2014 IEEE 6TH INDIA INTERNATIONAL CONFERENCE ON POWER ELECTRONICS (IICPE), 2014,
  • [39] New fault zone identification scheme for busbar using support vector machine
    Chothani, N. G.
    Bhalja, B. R.
    Parikh, U. B.
    IET GENERATION TRANSMISSION & DISTRIBUTION, 2011, 5 (10) : 1073 - 1079
  • [40] BEARING FAULT COMPONENT IDENTIFICATION USING INFORMATION GAIN AND MACHINE LEARNING ALGORITHMS
    Vinay, Vakharia
    Kumar, Gupta Vijay
    Kumar, Kankar Pavan
    STRUCTURAL HEALTH MONITORING AND INSPECTION OF ADVANCED MATERIALS, AEROSPACE, AND CIVIL INFRASTRUCTURE 2015, 2015, 9437