Fault Diagnosis of the Gyratory Crusher Based on Fast Entropy Multilevel Variational Mode Decomposition

被引:3
|
作者
Wu, Fengbiao [1 ,2 ]
Ma, Lifeng [1 ]
Zhang, Qianqian [3 ]
Zhao, Guanghui [1 ]
Liu, Pengtao [1 ]
机构
[1] Taiyuan Univ Sci & Technol, Coll Mech Engn, Taiyuan 030024, Peoples R China
[2] Shanxi Inst Energy, Taiyuan 030006, Peoples R China
[3] Shanxi Univ, Sch Automat & Software, Taiyuan 030006, Shanxi, Peoples R China
关键词
All Open Access; Gold;
D O I
10.1155/2021/5704271
中图分类号
O42 [声学];
学科分类号
070206 ; 082403 ;
摘要
Gyratory crusher is a kind of commonly used mining machinery. Because of its heavy workload and complex working environment, it is prone to failure and low reliability. In order to solve this problem, this paper proposes a fault diagnosis method of the gyratory crusher based on fast entropy multistage VMD, which is used to quickly and accurately find the possible fault problems of the gyratory crusher. This method mainly extracts the vibration signal by combining fast entropy and variational mode decomposition, so as to analyze the components of the vibration signal. Among them, fast entropy is used to quickly determine the number of modes in the signal spectrum and the bandwidth occupied by the modes. The extracted parameters can be converted into the input parameters of VMD. VMD can accurately extract the modal components in the signal by inputting the number of modes and related parameters. Due to the differences between modes, using the same parameters to extract the modes often leads to inaccurate results. Therefore, the concept of multilevel VMD is proposed. The parameters of different modes are determined by fast entropy. The modes in the signals are separated and extracted with different parameters so that different signal modes can be accurately extracted. In order to verify the accuracy of the method, this paper uses the data collected from the rotary crusher to test, and the results show that the proposed FE method can quickly and effectively extract the fault components in the vibration signal.
引用
收藏
页数:10
相关论文
共 50 条
  • [31] The Fault Diagnosis of Rolling Bearing Based on Variational Mode Decomposition and Iterative Random Forest
    Qin, Xiwen
    Guo, Jiajing
    Dong, Xiaogang
    Guo, Yu
    SHOCK AND VIBRATION, 2020, 2020
  • [32] A Broken Rotor Bar Fault Diagnosis Approach Based on Singular Value Decomposition and Variational Mode Decomposition
    Zou, Dan
    Ge, Xinglai
    2019 IEEE TRANSPORTATION ELECTRIFICATION CONFERENCE AND EXPO, ASIA-PACIFIC (ITEC ASIA-PACIFIC 2019): NEW PARADIGM SHIFT, SUSTAINABLE E-MOBILITY, 2019, : 248 - 253
  • [33] A dichotomy-based variational mode decomposition method for rotating machinery fault diagnosis
    Zheng, Xu
    Zhou, Quan
    Zho, Nan
    Liu, Ruijun
    Hao, Zhiyong
    Qiu, Yi
    MEASUREMENT SCIENCE AND TECHNOLOGY, 2020, 31 (01)
  • [34] Rolling Bearing Fault Diagnosis Based on Successive Variational Mode Decomposition and the EP Index
    Guo, Yuanjing
    Yang, Youdong
    Jiang, Shaofei
    Jin, Xiaohang
    Wei, Yanding
    SENSORS, 2022, 22 (10)
  • [35] Fault diagnosis of power grid based on variational mode decomposition and convolutional neural network
    Zhang, Qian
    Ma, Wenhao
    Li, Guoli
    Ding, Jinjin
    Xie, Min
    ELECTRIC POWER SYSTEMS RESEARCH, 2022, 208
  • [36] Fault diagnosis of bearings in nuclear power plants based on improved variational mode decomposition
    Zhu, Shaomin
    Xia, Hong
    Wang, Zhichao
    Peng, Binsen
    Jiang, Yingying
    Zhang, Jiyu
    Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University, 2021, 42 (10): : 1550 - 1556
  • [37] Mechanical fault diagnosis based on variational mode decomposition combined with deep transfer learning
    Shi J.
    Wu X.
    Liu X.
    Liu T.
    Wu, Xing (xwu@kust.edu.cn), 1600, Chinese Society of Agricultural Engineering (36): : 129 - 137
  • [38] Rolling mill bearings fault diagnosis based on improved multivariate variational mode decomposition and multivariate composite multiscale weighted permutation entropy
    Zhao, Chen
    Sun, Jianliang
    Lin, Shuilin
    Peng, Yan
    MEASUREMENT, 2022, 195
  • [39] Fault Diagnosis of a Wind Turbine Gearbox Based on Improved Variational Mode Algorithm and Information Entropy
    Zhang, Fan
    Sun, Wenlei
    Wang, Hongwei
    Xu, Tiantian
    ENTROPY, 2021, 23 (07)
  • [40] Automated Variational Nonlinear Chirp Mode Decomposition for Bearing Fault Diagnosis
    Dubey, Rahul
    Sharma, Rishi Raj
    Upadhyay, Abhay
    Pachori, Ram Bilas
    IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2023, 19 (11) : 10873 - 10882