Review of Big Data Analysis Technology for Power Equipment State
Haihua Ge, J.X. Ding, Yufang Dong · 2023
This paper provides an overview of the various aspects of statistical analysis on big data in the monitoring of power equipment conditions. The paper mainly focuses on three perspectives. Firstly, it introduces the holistic framework of power equipment condition monitoring, including modules such as real-time monitoring systems, data collection, data transmission, and the processing and interpretation of data. Secondly, it examines the current status of data fusion techniques within the context of extensive data in power equipment and outlines the significant challenges in this domain. Finally, the document evaluates technology for diagnosing equipment faults based on extensive data and outlines prospective paths for advancement in the realm of deep learning for fault diagnosis and prediction. In summary, this paper provides a valuable insight into the current progress of extensive data analysis technology in power equipment condition monitoring. It also provides meaningful insights into the future directions of this field.