中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
机构
采集方式
内容类型
发表日期
学科主题
筛选

浏览/检索结果: 共6条,第1-6条 帮助

条数/页: 排序方式:
Geodynamic evolution of the offshore Indus Basin Pakistan: the western Indian Plate Passive Continental Margin 期刊论文  OAI收割
GEOPHYSICAL JOURNAL INTERNATIONAL, 2019, 卷号: 217, 期号: 2, 页码: 1366-1386
作者:  
Khan, Majid;  Liu, Yike
  |  收藏  |  浏览/下载:84/0  |  提交时间:2019/06/24
The research on the movable solid materials under seepage flow effect in debris-flow source area 会议论文  OAI收割
Golden, CO, June 10, 2019 - June 13, 2019
作者:  
Yang, Shun;  Ou, Guo-qiang;  Pan, Hua-li;  Xie, Zhong-sheng;  Yang, Dong-xu
  |  收藏  |  浏览/下载:25/0  |  提交时间:2020/03/10
A bearing outer raceway fault detection method in induction motors based on instantaneous frequency of the stator current 期刊论文  OAI收割
IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, 2018, 卷号: 13, 期号: 3, 页码: 510-516
作者:  
Zhang JL(张吉龙);  Song XJ(宋向金);  Hu JT(胡静涛);  Zhu HY(祝洪宇)
  |  收藏  |  浏览/下载:53/0  |  提交时间:2018/02/24
A detection method for bearing faults using null space pursuit and S transform 期刊论文  OAI收割
SIGNAL PROCESSING, 2014, 卷号: 96, 页码: 80-89
作者:  
Zhu, De;  Gao, Qingwei;  Sun, Dong;  Lu, Yixiang;  Peng, Silong
收藏  |  浏览/下载:43/0  |  提交时间:2015/08/12
A 3-d viscoelastoplastic model for simulating long-term slip on non-planar faults 期刊论文  iSwitch采集
Geophysical journal international, 2009, 卷号: 176, 期号: 1, 页码: 293-306
作者:  
Li, Qingsong;  Liu, Mian;  Zhang, Huai
收藏  |  浏览/下载:25/0  |  提交时间:2019/05/10
Abrupt sensor fault diagnosis based on wavelet network (EI CONFERENCE) 会议论文  OAI收割
2006 IEEE International Conference on Information Acquisition, ICIA 2006, August 20, 2006 - August 23, 2006, Weihai, Shandong, China
作者:  
Li W.;  Li W.;  Zhang H.;  Zhang H.
收藏  |  浏览/下载:18/0  |  提交时间:2013/03/25
The possible faults of a sensor may be classified as abrupt (sudden) faults and incipient (slowly developing) faults. This paper focuses on the abrupt faults of a sensor. Due to the limited number of scales  a single wavelet amplitude map has not enough scales to describe all details of the signal. The sampling grid in the scale direction is rather sparse  Some of the fault information will be leaked under such sparse grid. To make up for the deficiency of scalar orthogonal wavelet transform in the application of abrupt fault diagnosis  multiwavelet packets transform was introduced into the field of abrupt fault diagnosis. The distribution differences of the signal energy on decomposed multiwavelet scales of the signal before and after the fault occurring are extracted as the fault feature and used as the input of multi-dimensional wavelet network. A new model-free diagnostic method for isolating abrupt sensor faults is developed based on a proposed algorithm of multi-dimensional wavelet network constructing. The method has been proved to be quite effective in the detection of sensor abrupt fault. 2006 IEEE.