Fault Intelligent Diagnosis for Distribution Box in Hot Rolling Based on Depthwise Separable Convolution and Bi-LSTM

被引:0
|
作者
Guo, Yonglin [1 ]
Zhou, Di [1 ]
Chen, Huimin [1 ]
Yue, Xiaoli [1 ]
Cheng, Yuyu [2 ]
机构
[1] Donghua Univ, Sch Mech Engn, Shanghai 200051, Peoples R China
[2] Donghua Univ, Sch Informat Sci & Technol, Shanghai 200051, Peoples R China
关键词
fault diagnosis; finishing mill; distribution box; depthwise separable convolution; Bi-LSTM; NEURAL-NETWORKS;
D O I
10.3390/pr12091999
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
The finishing mill is a critical link in the hot rolling process, influencing the final product's quality, and even economic efficiency. The distribution box of the finishing mill plays a vital role in power transmission and distribution. However, harsh operating conditions can frequently lead to distribution box damage and even failure. To diagnose faults in the distribution box promptly, a fault diagnosis network model is constructed in this paper. This model combines depthwise separable convolution and Bi-LSTM. Depthwise separable convolution and Bi-LSTM can extract both spatial and temporal features from signals. This structure enables comprehensive feature extraction and fully utilizes signal information. To verify the diagnostic capability of the model, five types of data are collected and used: the pitting of tooth flank, flat-headed sleeve tooth crack, gear surface crack, gear tooth surface spalling, and normal conditions. The model achieves an accuracy of 97.46% and incorporates a lightweight design, which enhances computational efficiency. Furthermore, the model maintains approximately 90% accuracy under three noise conditions. Based on these results, the proposed model can effectively diagnose faults in the distribution box, and reduce downtime in engineering.
引用
收藏
页数:17
相关论文
共 50 条
  • [1] An unsupervised transfer learning bearing fault diagnosis method based on depthwise separable convolution
    Li, Xueyi
    Yuan, Peng
    Wang, Xiangkai
    Li, Daiyou
    Xie, Zhijie
    Kong, Xiangwei
    MEASUREMENT SCIENCE AND TECHNOLOGY, 2023, 34 (09)
  • [2] Improved Depthwise Separable Convolution for Transfer Learning in Fault Diagnosis
    Xu, Hai
    Xiao, Yongchang
    Sun, Kun
    Cui, Lingli
    IEEE SENSORS JOURNAL, 2024, 24 (20) : 33606 - 33613
  • [3] Research on Fault Diagnosis of Highway Bi-LSTM Based on Attention Mechanism
    Li, Xueyi
    Su, Kaiyu
    He, Qiushi
    Wang, Xiangkai
    Xie, Zhijie
    EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY, 2023, 25 (02):
  • [4] Large Model for Rotating Machine Fault Diagnosis Based on a Dense Connection Network With Depthwise Separable Convolution
    Qin, Yi
    Zhang, Taisheng
    Qian, Quan
    Mao, Yongfang
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2024, 73 : 1 - 12
  • [5] The Development of Bi-LSTM Based on Fault Diagnosis Scheme in MVDC System
    Lim, Jae-Sung
    Cho, Haesong
    Kwon, Dohoon
    Hong, Junho
    ENERGIES, 2024, 17 (18)
  • [6] Bi-LSTM fault diagnosis method for rolling bearings based on segmented interception AR spectrum analysis and information fusion
    Zhong, Cheng
    Wang, Jie-Sheng
    Liu, Yu
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2023, 44 (05) : 8493 - 8519
  • [7] Intelligent fault diagnosis of hydroelectric units based on radar maps and improved GoogleNet by depthwise separate convolution
    Wang, Yunhe
    Zou, Yidong
    Hu, Wenqing
    Chen, Jinbao
    Xiao, Zhihuai
    MEASUREMENT SCIENCE AND TECHNOLOGY, 2024, 35 (02)
  • [8] Research on intelligent forecasts of fl ight actions based on the implemented bi-LSTM
    Hua, Xin
    Yang, Xuejie
    PEERJ COMPUTER SCIENCE, 2024, 10
  • [9] A Fault diagnosis method for planetary gearboxes based on Bi-LSTM and feature screening by two-sample z test
    Zhang, Ke
    Cao, Shenying
    Yang, Jiuwen
    Zhou, Gan
    Yin, Zhifeng
    Wang, Lu
    PROCEEDINGS OF THE 33RD CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2021), 2021, : 7130 - 7137
  • [10] Intelligent fault diagnosis of rolling bearings based on LSTM with large margin nearest neighbor algorithm
    Aljemely, Anas H.
    Xuan, Jianping
    Al-Azzawi, Osama
    Jawad, Farqad K. J.
    NEURAL COMPUTING & APPLICATIONS, 2022, 34 (22) : 19401 - 19421