Fault diagnosis of mechanical equipment in high energy consumption industries in China: A review

被引:40
|
作者
Sun, Yongjian [1 ]
Wang, Jian [1 ]
Wang, Xiaohong [1 ]
机构
[1] Univ Jinan, Sch Elect Engn, Jinan, Shandong, Peoples R China
关键词
Building materials machinery; Data acquisition; Feature extraction; Fault diagnosis; EMPIRICAL MODE DECOMPOSITION; ROLLING ELEMENT BEARING; ROTATING MACHINERY; DETECT FAULTS; SOUND FIELD; ENTROPY; TIME; EXTRACTION; TRANSFORM; ALGORITHM;
D O I
10.1016/j.ymssp.2022.109833
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
Building materials machinery equipment play an important role in the production of cement, brick and tile, glass and other building materials, which are high energy consumption industries. Due to advanced sensors, continuous improvement of signal acquisition technologies and increasing data storage space, a large amount of data can be used by scholars, which makes data -based fault diagnosis gradually studied by more and more scholars. With increasing amount of data, new challenges are as follows: there is very little data that can really be used; the research on compound fault diagnosis and weak fault diagnosis is still not mature; the diagnosis accuracy of variable speed components is low. These problems restrict the further development of fault diagnosis. In this paper, the characteristics of fault diagnosis of building materials equipment are first expounded, the principles and characteristics of main building materials equipment, signal classification, sensor selection and error correction are briefly introduced, then the research status are discussed, the existing difficulties and challenges are summarized, and the potential development directions and trends in this field are given.
引用
收藏
页数:33
相关论文
共 50 条
  • [1] Review of spectrum analysis in fault diagnosis for mechanical equipment
    Wang, Zihan
    Wang, Jian
    Sun, Yongjian
    ENGINEERING RESEARCH EXPRESS, 2023, 5 (04):
  • [2] Role of entropy in fault diagnosis of mechanical equipment: a review
    Wang, Zihan
    Sun, Yongjian
    ENGINEERING RESEARCH EXPRESS, 2023, 5 (03):
  • [3] WSNs-Based Mechanical Equipment State Monitoring and Fault Diagnosis in China
    Huang, Jianfeng
    Chen, Guohua
    Shu, Lei
    Zhang, Qinghua
    Wu, Xiaoling
    INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS, 2015,
  • [4] Research on output and energy consumption of high carbon embodied industries in China
    Yun, Zhang, 1600, CAFET INNOVA Technical Society, 1-2-18/103, Mohini Mansion, Gagan Mahal Road,, Domalguda, Hyderabad, 500029, India (07):
  • [5] Role of image feature enhancement in intelligent fault diagnosis for mechanical equipment: A review
    Sun, Yongjian
    Wang, Wei
    Engineering Failure Analysis, 2024, 156
  • [6] Role of image feature enhancement in intelligent fault diagnosis for mechanical equipment: A review
    Sun, Yongjian
    Wang, Wei
    ENGINEERING FAILURE ANALYSIS, 2024, 156
  • [7] Image deep learning in fault diagnosis of mechanical equipment
    Wang, Chuanhao
    Sun, Yongjian
    Wang, Xiaohong
    JOURNAL OF INTELLIGENT MANUFACTURING, 2024, 35 (06) : 2475 - 2515
  • [8] Servitisation of Fault Diagnosis for Mechanical Equipment in Cloud Manufacturing
    Yan, Junwei
    Liu, Quan
    Xu, Wenjun
    Duc Truong Pham
    Ji, Chunqian
    PROCEEDINGS 2015 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN), 2015, : 586 - 590
  • [9] Fault Diagnosis of Mechanical Equipment Based on Data Visualization
    Li, Guang
    Li, Maolin
    Liu, Dan
    Xu, Guanghua
    Zhou, Shiming
    2017 10TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, BIOMEDICAL ENGINEERING AND INFORMATICS (CISP-BMEI), 2017,
  • [10] A Method for Fault Diagnosis of Mechanical Equipment: Iterative Fast Kurtogram
    Deng, Baosong
    Yu, Gang
    PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON COMPUTER ENGINEERING AND NETWORKS, VOL II, CENET 2023, 2024, 1126 : 457 - 467