Recognition of complex abnormal vibration voiceprint of power transmission and transformation equipment in cold environment
The transmission equipment is equipped with a vibration signal measurement system;The vibration characteristics un-der different mechanical and electrical fault conditions can be analyzed to determine the relationship between OLTC conditions and vi-bration signals.However,low-temperature environments can lead to embrittlement of equipment materials and solidification of lubri-cants,changing the vibration and voiceprint characteristics of the equipment,and increasing the complexity of identifying abnormal vi-bration voiceprints.Propose a voiceprint recognition method for complex abnormal vibrations of transmission equipment in cold envi-ronments.Combining Empirical Mode Decomposition(EMD)with Stein's unbiased estimate(SURE)to collect vibration voiceprint signals of power transmission and transformation equipment,and denoise them;Divide the denoised voiceprint signal into multiple voiceprint segments and convert them into spectrograms.In the feature extraction stage,the normal spectrogram is used as input,and a Long Short Term Memory(LSTM)network is used for training to classify the input spectrogram voiceprint samples of power trans-mission and transformation equipment,and determine abnormal samples to achieve accurate recognition of abnormal vibration voice-print of power transmission and transformation equipment.The experimental results show that the proposed method can accurately i-dentify abnormal vibration voiceprints of power transmission and transformation equipment,with recognition rates of over 96％and rec-ognition time of 94.47 ms.
cold environmentpower transmission and transformation equipmentabnormal vibrationvoiceprint recognition
万方数据
维普
