High-voltage circuit breaker fault voiceprint recognition based on prototype similar domain adaptive spectral morphological neural network

High-voltage circuit breakers will emit continuous vibration signals during operation. The signals contain a large number of pulses and fluctuations caused by faults. They are the main data source for evaluating the functioning condition for high-tension breaker switches. To aim for examine its vocal features in vibration acoustic signals of high-tension breaker switches in different operating states, a prototype similarity domain adaptive spectral morphological neural network (PSDA-SMNN) was proposed. First, the vibration signal is denoised using spectral morphology variational mode decomposition combined with fast singular value decomposition method. Secondly, the labeled data of a certain operational state serves utilized for this information origin area, while this untagged information from different operational states serves utilized for this training objective area, and the prototype network distance similarity is used to align the feature distribution between the domains; then, meta-training is used to the domain network undergoes internal supervised training, and the network in the target domain undergoes external unsupervised training using the virtual label backpropagation algorithm. Through internal and external loop training, the difference in feature distribution between domains is reduced, and unlabeled faults of high-tension breaker switches in varying operational states are recognized. Test outcomes indicate which the suggested framework is able to precisely identify the malfunction operational condition for high-tension breaker switches and detect common issues for high-tension breaker switches under various interference settings, having a classification precision of approximately 95%.