Application of Information Fusion in Fault Diagnosis of Electronic Products

With the continuous development of the electronic industry, the electronic system is becoming more and more complex. Improving the reliability of electronic products has become an important means to ensure economic growth and social benefits; therefore, the fault diagnosis of electronic system has become the focus of scholars in the field of electronics. In this paper, the general method of data collection for fault diagnosis of electronic products is analyzed based on the data feedback from distributed sensors. In order to fuse the statistical properties of data and the energy properties of data, combining the fusion algorithm of D-S evidence theory, the classification results of LDA feature extraction and wavelet energy spectrum entropy feature vector extraction are fused, compared with the single algorithm, the fusion algorithm has better recognition.