@article{4308,
  author = {Run Ma},
  title = {Analysis of Electrical Equipment Information Detection and Diagnosis Based on Multiple Information Integration},
  journal = {Journal of Electronic Systems},
  year = {2024},
  volume = {14},
  number = {4},
  doi = {https://doi.org/10.6025/jes/2024/14/4/124-131},
  url = {https://www.dline.info/jes/fulltext/v14n4/jesv14n4_2.pdf},
  abstract = {detection based on multi-information integration. By integrating and analyzing
information from multiple sources of electrical equipment, the accuracy and efficiency
of equipment fault detection and diagnosis can be effectively improved. In detecting
and diagnosing electrical equipment information, multiple sources of information
are usually involved, including equipment operation data, sensor data, historical
maintenance records, etc. This information has different forms and characteristics,
so it needs to be integrated and comprehensively analyzed to fully explore the useful
information within it. This article proposes a method for detecting, diagnosing and
analyzing information on electrical equipment based on multi-information integration.
This method first preprocesses and integrates information from different sources,
then utilises machine learning and data mining techniques to analyze and mine the
information. Among them, special attention is paid to the complementarity of
information and fusion methods to extract valuable features and patterns from
different information fully.},
}