Working Conditions of a Track Layer Bulldozer Excavating a Deep Cut.

被引:0
|
作者
Besser, Dietmar [1 ]
Winkelmann, Ralf [1 ]
机构
[1] Bergakademie Freiberg, Freiberg, East Ger, Bergakademie Freiberg, Freiberg, East Ger
来源
Neue Bergbautechnik | 1985年 / 15卷 / 07期
关键词
EARTHMOVING MACHINERY - Excavators - EXCAVATION - Mathematical Models - MINING MACHINERY - Performance;
D O I
暂无
中图分类号
学科分类号
摘要
The article presents a numerical analysis of the performance of a track layer bulldozer excavating a deep cut.
引用
收藏
页码:269 / 273
相关论文
共 50 条
  • [31] A New Deep Transfer Learning Method for Bearing Fault Diagnosis Under Different Working Conditions
    Zhu, Jun
    Chen, Nan
    Shen, Changqing
    IEEE SENSORS JOURNAL, 2020, 20 (15) : 8394 - 8402
  • [32] Fault Diagnosis under Variable Working Conditions Based on STFT and Transfer Deep Residual Network
    Du, Yan
    Wang, Aiming
    Wang, Shuai
    He, Baomei
    Meng, Guoying
    SHOCK AND VIBRATION, 2020, 2020
  • [33] Deep Transfer Learning-Based Fault Diagnosis for Gearbox under Complex Working Conditions
    Wan, Zitong
    Yang, Rui
    Huang, Mengjie
    SHOCK AND VIBRATION, 2020, 2020
  • [34] Deep dynamic adaptation network: a deep transfer learning framework for rolling bearing fault diagnosis under variable working conditions
    Huoyao Xu
    Jie Liu
    Xiangyu Peng
    Junlang Wang
    Chaoming He
    Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2023, 45
  • [35] Deep dynamic adaptation network: a deep transfer learning framework for rolling bearing fault diagnosis under variable working conditions
    Xu, Huoyao
    Liu, Jie
    Peng, Xiangyu
    Wang, Junlang
    He, Chaoming
    JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING, 2023, 45 (01)
  • [36] Firing characteristics of deep layer neurons in prefrontal cortex in rats performing spatial working memory tasks
    Jung, MW
    Qin, YL
    McNaughton, BL
    Barnes, CA
    CEREBRAL CORTEX, 1998, 8 (05) : 437 - 450
  • [37] Cross-Layer Alignment Network With Norm Constraints for Fault Diagnosis Under Varying Working Conditions
    Yang, Xilin
    Yuan, Xianfeng
    Ye, Tianyi
    Xu, Qingyang
    Song, Yong
    Jin, Jiong
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2023, 72
  • [38] Weighted Entropy Minimization Based Deep Conditional Adversarial Diagnosis Approach Under Variable Working Conditions
    She, Daoming
    Jia, Minping
    Pecht, Michael G.
    IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2021, 26 (05) : 2440 - 2450
  • [39] Fault diagnosis method of rolling bearings under varying working conditions based on deep feature transfer
    Kang, Shouqiang
    Qiao, Chunyang
    Wang, Yujing
    Wang, Qingyan
    Hu, Mingwu
    Mikulovich, V. I.
    JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY, 2020, 34 (11) : 4383 - 4391
  • [40] Deep imbalanced domain adaptation for transfer learning fault diagnosis of bearings under multiple working conditions
    Ding, Yifei
    Jia, Minping
    Zhuang, Jichao
    Cao, Yudong
    Zhao, Xiaoli
    Lee, Chi-Guhn
    RELIABILITY ENGINEERING & SYSTEM SAFETY, 2023, 230