Open AccessResearch on Multi-Source Heterogeneous Data Fusion Technology of New Energy Vehicles Under the New Four Modernizations
Author(s) -
Fan Zhang,
Yang Jing,
Chuan Sun,
Xin Guo,
Wan Tiantian
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1865/2/022034
Subject(s) - domain (mathematical analysis) , fault (geology) , computer science , sensor fusion , energy (signal processing) , new energy , focus (optics) , feature (linguistics) , development (topology) , systems engineering , data mining , real time computing , engineering , artificial intelligence , mechanical engineering , mathematical analysis , linguistics , statistics , physics , philosophy , mathematics , optics , seismology , geology
In order to better adapt to the development trend of the city, new energy vehicles are widely used to fit the development concept of green transportation. As the focus of smart city development, big data is indispensable for its application in new energy vehicle guarantees, especially in maintenance and machinery guarantees. In the operation and maintenance of new energy vehicles, the application of multi-source heterogeneous data technology is extremely important. Based on this research background, the paper introduces and constructs the new energy vehicle fault feature vector of the new energy vehicle, gives a multi-source time domain frequency domain data fusion new energy vehicle fault diagnosis method, and uses the neural network to give the basic probability distribution. The evidence theory fuses the signals of each sensor to get the diagnosis result.