Research on On-line Data Prediction of Electrical Equipment Based on Wavelet Analysis and Data Fusion

Abstract

In the process of fault analysis and diagnosis of electrical equipment, it is of great significance to use wavelet transform to detect the fault time of fault signal. In order to overcome this deficiency, wavelet analysis can decompose mixed signals with different frequencies into block signals with different frequency components, which can effectively separate signal from noise, extract features and diagnose faults. According to the virtual instrument solution, this paper comprehensively processes various multi-source fault data information through data fusion technology. In fault pattern recognition, how to make full use of the advantages of various diagnosis methods, combine each other and learn from each other is of great significance to improve the resolution of fault pattern and enhance the comprehensiveness and reliability of fault diagnosis. Data fusion technology is a new frontier discipline to study multi-source information processing and analysis methods. In military, information processing and other fields, the application of multi-sensor data fusion technology to fault diagnosis has attracted more and more attention. This paper discusses the application characteristics of electrical equipment based on wavelet analysis and data fusion, and gives the latest research results and development trend.