机构

• 自动化研究所 [3]
• 沈阳自动化研究所 [2]
• 力学研究所 [1]
• 长春光学精密机械与物... [1]
• 过程工程研究所 [1]
• 计算机网络信息中心 [1]
• 更多

采集方式

• OAI收割 [9]
• iSwitch采集 [1]

内容类型

• 期刊论文 [9]
• 会议论文 [1]

发表日期

• 2022 [2]
• 2021 [2]
• 2020 [2]
• 2019 [1]
• 2018 [1]
• 2011 [1]
• 更多

学科主题

筛选

浏览/检索结果: 共10条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序作者升序作者降序 Research on Electric Breakdown Fault Diagnosis Model of Transformer Insulated Oil Based on Fluorescent Double-Color Ratio 期刊论文  OAI收割 SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 卷号: 42 作者:  Zhao Yue;  Ma Feng-xiang;  Wang An-jing;  Li Da-cheng;  Song Yu-mei   |  收藏  |  浏览/下载：35/0  |  提交时间：2022/12/23 Transformer insulated oil  Fluorescence spectra  Double-color ratio detection  Fault diagnosis model   Generalization on Unseen Domains via Model-Agnostic Learning for Intelligent Fault Diagnosis 期刊论文  OAI收割 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2022, 卷号: 71, 页码: 11 作者:  Wang, Huanjie;  Bai, Xiwei;  Wang, Sihan;  Tan, Jie;  Liu, Chengbao   |  收藏  |  浏览/下载：41/0  |  提交时间：2022/06/06 Fault diagnosis  Data models  Task analysis  Representation learning  Adaptation models  Training data  Training  Convolutional neural network (CNN)  data-driven fault diagnosis  domain generalization (DG)  model-agnostic learning  rolling bearing   Model-free fault diagnosis for autonomous underwater vehicles using sequence Convolutional Neural Network 期刊论文  OAI收割 Ocean Engineering, 2021, 卷号: 232, 页码: 1-11 作者:  Ji DX(冀大雄);  Yao, Xin;  Li S(李硕);  Tang YG(唐元贵);  Tian Y(田宇)   |  收藏  |  浏览/下载：66/0  |  提交时间：2021/06/01 Autonomous Underwater Vehicles (AUVs)  Convolutional Neural Network (CNN)  Fault diagnosis  Global feature  Model-free   Autonomous underwater vehicle fault diagnosis dataset 期刊论文  OAI收割 Data in Brief, 2021, 卷号: 39, 页码: 1-6 作者:  Ji DX(冀大雄);  Yao, Xin;  Li S(李硕);  Tang YG(唐元贵);  Tian Y(田宇)   |  收藏  |  浏览/下载：133/0  |  提交时间：2021/10/30 Autonomous underwater vehicles (AUV)  Fault diagnosis  Fault type  Model-free  State data   Transfer learning for process fault diagnosis: Knowledge transfer from simulation to physical processes 期刊论文  OAI收割 COMPUTERS & CHEMICAL ENGINEERING, 2020, 卷号: 139, 页码: 10 作者:  Li, Weijun;  Gu, Sai;  Zhang, Xiangping;  Chen, Tao   |  收藏  |  浏览/下载：38/0  |  提交时间：2020/09/22 Fault diagnosis  Transfer learning  Model-process mismatch  Deep learning  Computer simulation  Domain adaptation   Locally Linear Back-propagation Based Contribution for Nonlinear Process Fault Diagnosis 期刊论文  OAI收割 IEEE/CAA Journal of Automatica Sinica, 2020, 卷号: 7, 期号: 3, 页码: 764-775 作者:  Jinchuan Qian;  Li Jiang;  Zhihuan Song   |  收藏  |  浏览/下载：25/0  |  提交时间：2021/03/11 Auto-encoder (AE)  deep learning  fault diagnosis  locally linear model  nonlinear process  reconstruction based contribution (RBC)   An Ensemble Convolutional Neural Networks for Bearing Fault Diagnosis Using Multi-Sensor Data 期刊论文  OAI收割 Sensors, 2019, 卷号: 19, 期号: 23, 页码: 5300 作者:  Yang Liu（刘洋）;  Xunshi Yan;  Zhang CA(张陈安);  Wen Liu （刘文）;  Liu Y(刘洋)   |  收藏  |  浏览/下载：104/0  |  提交时间：2020/01/02 Rotating Machinery  Fault Diagnosis  Multi-sensor Fusion  Convolutional Neural Network  Ensemble Model   A hybrid feature model and deep learning based fault diagnosis for unmanned aerial vehicle sensors 期刊论文  OAI收割 Neurocomputing, 2018, 卷号: 319, 期号: 2018, 页码: 155-163 作者:  Guo, Dingfei;  Zhong, Maiying   |  收藏  |  浏览/下载：27/0  |  提交时间：2022/04/06 Model based fault diagnosis  Deep learning  Short-time fourier transform  Convolutional neural network  UAV sensors   A dfsm-based protocol conformance testing and diagnosing method 期刊论文  iSwitch采集 Informatica, 2011, 卷号: 22, 期号: 3, 页码: 447-469 作者:  Zhang, Xinchang;  Yang, Meihong;  Geng, Guanggang;  Luo, Wanming 收藏  |  浏览/下载：39/0  |  提交时间：2019/05/09 Protocol conformance testing  Fault detection  Fault diagnosis  Dfsm model   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：23/0  |  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.