An information fusion method based on deep learning and fuzzy discount-weighting for target intention recognition

2022 ◽  
Vol 109 ◽  
pp. 104610
Author(s):  
Zhuo Zhang ◽  
Hongfei Wang ◽  
Jie Geng ◽  
Wen Jiang ◽  
Xinyang Deng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Information Fusion ◽  
Fusion Method ◽  
Intention Recognition

scholarly journals Rolling Bearing Fault Diagnosis Based on Deep Learning and Autoencoder Information Fusion

Symmetry ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 13
Author(s):  
Jianpeng Ma ◽  
Chengwei Li ◽  
Guangzhu Zhang
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Rolling Bearing ◽  
Fusion Method ◽  
Rolling Bearings ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Evolution Analysis ◽  
Comparison Results

The multisource information fusion technique is currently one of the common methods for rolling bearing fault diagnosis. However, the current research rarely fuses information from the data of different sensors. At the same time, the dispersion itself in the VAE method has asymmetric characteristics, which can enhance the robustness of the system. Therefore, in this paper, the information fusion method of the variational autoencoder (VAE) and random forest (RF) methods are targeted for subsequent lifetime evolution analysis. This fusion method achieves, for the first time, the simultaneous monitoring of acceleration signals, weak magnetic signals and temperature signals of rolling bearings, thus improving the fault diagnosis capability and laying the foundation for subsequent life evolution analysis and the study of the fault–slip correlation. Drawing on the experimental procedure of the CWRU’s rolling bearing dataset, the proposed VAERF technique was evaluated by conducting inner ring fault diagnosis experiments on the experimental platform of the self-research project. The proposed method exhibits the best performance compared to other point-to-point algorithms, achieving a classification rate of 98.19%. The comparison results further demonstrate that the deep learning fusion of weak magnetic and vibration signals can improve the fault diagnosis of rolling bearings.

Fault Classification of Hydroelectric Generating Unit Based on Improved Evidence Theory

2014 ◽  
Vol 7 (1) ◽  
pp. 78-83 ◽  
Author(s):  
Jiatang Cheng ◽  
Li Ai ◽  
Zhimei Duan ◽  
Yan Xiong
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Evidence Theory ◽  
Fault Classification ◽  
Fusion Method ◽  
Inconsistent Result ◽  
High Conflict

Aiming at the problem of the conventional vibration fault diagnosis technology with inconsistent result of a hydroelectric generating unit, an information fusion method was proposed based on the improved evidence theory. In this algorithm, the original evidence was amended by the credibility factor, and then the synthesis rule of standard evidence theory was utilized to carry out information fusion. The results show that the proposed method can obtain any definitive conclusion even if there is high conflict evidence in the synthesis evidence process, and may avoid the divergent phenomenon when the consistent evidence is fused, and is suitable for the fault classification of hydroelectric generating unit.

Hybrid multitask multi-information fusion deep learning for household short-term load forecasting

2021 ◽  
pp. 1-1
Author(s):  
Lianjie Jiang ◽  
Xinli Wang ◽  
Wei Li ◽  
Lei Wang ◽  
Xiaohong Yin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Information Fusion ◽  
Load Forecasting ◽  
Short Term ◽  
Short Term Load Forecasting

A multi-source data collection and information fusion method for distribution network based on IOT protocol

2021 ◽  
Vol 651 (2) ◽  
pp. 022076
Author(s):  
Yongxiang Cai ◽  
Xiaobing Xiao ◽  
Hao Tian ◽  
Yu Fu ◽  
Peng Wu ◽  
...  
Keyword(s):  
Data Collection ◽  
Information Fusion ◽  
Distribution Network ◽  
Fusion Method ◽  
Source Data

Using Z-number to measure the reliability of new information fusion method and its application in pattern recognition

2021 ◽  
pp. 107658
Author(s):  
Ye Tian ◽  
Xiangjun Mi ◽  
Huizi Cui ◽  
Pengdan Zhang ◽  
Bingyi Kang
Keyword(s):  
Pattern Recognition ◽  
Information Fusion ◽  
Fusion Method ◽  
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