Research on Electronic Circuit Fault Diagnosis Method Based on SWT and DCNN-ELM

The increase in the complexity of modern electronic products has brought significant challenges to fault diagnosis circuits, and current methods have problems such as long identification time, inaccurate positioning, low diagnostic efficiency. In response these situations. This paper proposes a method for circuits combining synchronous synchrosqueezing wavelet transform (SWT), deep convolutional neural network (DCNN), extreme learning machine (ELM). First, original signal is noise-reduced converted into higher resolution two-dimensional time-frequency image using SWT. Then, improved optimized DCNN model used extract advanced features image, extracted are further input ELM classifier classification. Finally, validation performed by experiments. experimental results show that, compared with other methods, circuit based on SWT DCNN-ELM ensures accuracy while shortening significantly improving efficiency diagnosis.