Abstract: Aiming at the problems of multiple features, high noise and long fault diagnosis time in analog circuit fault diagnosis, an analog circuit fault diagnosis model based on H-DELM is proposed. The architecture unit of the model is a deep extreme learning machine (DELM-AE) with double random hidden layers. Two random hidden layers are used to encode features and one output layer is used to decode features. The H-DELM model is constructed by stacking DELM-AE in hierarchical structure. Because DELM-AE can represent the features and the output is the same as the original input information, H-DELM can copy the original input data as much as possible, and then learn more expressive and compact features. Finally, the verification is carried out by two circuits: quadruple operational amplifier double-order high-pass filter and two-stage four-op-amp biquad lowpass filter. The experimental results show that the model is feasible in analog circuit fault diagnosis. Compared with other model, it is proved that the proposed model has high robustness, the classification speed is about 1s, and the accuracy rate of fault classification can reach 100%.

Key words: analog circuit, fault diagnosis, feature extraction, H-DELM model, automatic encoder, feature representation