Challenges and Opportunities in Vibrometry-Based Online Condition Monitoring of Mineral Processing Equipment

被引:0
|
作者
Mishra, S. [1 ]
Majumder, Arun Kumar [2 ]
机构
[1] Indian Sch Mines, Indian Inst Technol, Dept Fuel Minerals & Met Engn, Dhanbad 826004, India
[2] Indian Inst Technol, Dept Min Engn, Kharagpur 721302, India
关键词
Vibrometry; Condition monitoring; Mineral processing equipment; Signal processing; Vibration monitoring; VIBRATION SIGNAL; BALL MILL; LINER WEAR; PERFORMANCE; PARAMETERS; TURBULENCE; CHARGE; STATE;
D O I
10.1007/s42461-024-01139-3
中图分类号
TF [冶金工业];
学科分类号
0806 ;
摘要
Online condition monitoring of mineral processing equipment is essential for optimizing productivity and minimizing plant downtime. This article explores the potential of vibrometry-based online condition monitoring to enhance the reliability and performance of mineral processing equipment. Through a comprehensive literature review, we identify significant challenges, such as sensor placement, noise interference, and signal interpretation, and propose potential solutions to each equipment. Through a case study, we demonstrate how using empirical mode decomposition and power spectral density, vibrometry effectively detects roping in two design configurations of hydrocyclone, addressing noise filtering and vibration data interpretation challenges. Considering this, we propose future research towards advancements in sensor technology and sophisticated data interpretation algorithms for the application of vibrometry in other mineral processing equipment under its significant benefits. We believe this article will enable readers to envision solutions for overcoming challenges and developing a robust vibrometry-based monitoring system for mineral processing equipment.
引用
收藏
页码:3475 / 3489
页数:15
相关论文
共 50 条
  • [21] Research on Artificial Intelligence-based Condition Monitoring Technology for Electricity Metering Equipment
    Huang, Rui
    Nong, Weizheng
    Chen, Xiao
    Du, Xiao
    Liu, Zhiyuan
    PROCEEDINGS OF 2024 INTERNATIONAL CONFERENCE ON POWER ELECTRONICS AND ARTIFICIAL INTELLIGENCE, PEAI 2024, 2024, : 24 - 28
  • [22] High-voltage equipment condition monitoring and diagnosis system based on information fusion
    Li, Yongwei
    Wang, Zhenyu
    Han, Xingde
    Li, Yalun
    NEURAL COMPUTING & APPLICATIONS, 2009, 18 (05): : 447 - 453
  • [23] Computer Vision based Automatic Power Equipment Condition Monitoring and Maintenance: A Brief Review
    Liang, Haiyang
    Li, Zhongwei
    Liu, Chunying
    Yang, Jian
    Zhang, Yuhan
    2020 19TH INTERNATIONAL SYMPOSIUM ON DISTRIBUTED COMPUTING AND APPLICATIONS FOR BUSINESS ENGINEERING AND SCIENCE (DCABES 2020), 2020, : 142 - 145
  • [24] Condition Monitoring of Industrial Equipment Based on Multi-Variables State Estimate Technique
    Long, Dongteng
    Zheng, Heng
    Hong, Feng
    APPLIED SCIENCES-BASEL, 2020, 10 (16):
  • [25] Application of Signal Processing for Motor Condition Monitoring Based on Filtered-Signals and Eliminated-Signals
    Treetrong, Juggrapong
    APPLIED MATERIALS AND ELECTRONICS ENGINEERING, PTS 1-2, 2012, 378-379 : 557 - 560
  • [26] Gordian technique research on condition-based maintenance (CBM) condition monitoring and fault diagnosis model of aeronautic equipment
    Jiang, Wei-Wei
    Yin, He
    Yan, Jin
    Hou, Xue-Qiao
    Zhang, Liang
    DESIGN, MANUFACTURING AND MECHATRONICS (ICDMM 2015), 2016, : 1255 - 1261
  • [27] VIBROCHANGE—a development system for condition monitoring based on advanced techniques of signal processing
    Dorel Aiordachioaie
    Theodor D. Popescu
    The International Journal of Advanced Manufacturing Technology, 2019, 105 : 919 - 936
  • [28] IIoT-Based Approach to Industrial Equipment Condition Monitoring:Wireless Technology and Use Cases
    Abdullin, Vildan V.
    Shnayder, Dmitry A.
    Khasanov, Aleksey R.
    Tselikanov, Danila F.
    2020 GLOBAL SMART INDUSTRY CONFERENCE (GLOSIC), 2020, : 399 - 406
  • [29] An Improved Correlation-Based Anomaly Detection Approach for Condition Monitoring Data of Industrial Equipment
    Zhong, Shisheng
    Luo, Hui
    Lin, Lin
    Fu, Xuyun
    2016 IEEE INTERNATIONAL CONFERENCE ON PROGNOSTICS AND HEALTH MANAGEMENT (ICPHM), 2016,
  • [30] Research on Multi-Dimensional Analysis Method of Power Equipment Condition Monitoring Based on OLAP
    Xi, Li
    Wang Hongkai
    Li Jinhu
    Pei Xubin
    Yu Zhanpeng
    2019 4TH IEEE INTERNATIONAL CONFERENCE ON BIG DATA ANALYTICS (ICBDA 2019), 2019, : 384 - 388