LAUSR

Abstract Liquid level of internal silicone oil is about the safety operation of high voltage porcelain bushing type (PBT) terminals, and its regular inspection can effectively prevent the security risks caused by oil leaks. In this paper, a deep learning detection method based on local wavelet features and unsupervised feature fusion is proposed to quantitatively detect the internal liquid level of high voltage PBT terminals. Firstly, ultrasonic guided wave signals are divided into many segments by a sliding window and all segments are transformed into wavelet domain to catch the local time-frequency information. After preliminary selection according to the monotonic trends, the features are fed into a deep learning method named auto-encoder networks for unsupervised feature fusion. Finally, a two-layer neural network is employed to regress the liquid level based on fused features. The experiments were conducted in different liquid level, and detection data are divided into model training set and testing set. The mean detection error of proposed method is only 0.034 m when the accuracy of training set is 0.1 m, and 0.0543 m when the accuracy of training set is 0.2 m. In liquid level detection experiments, proposed method also shows good robustness in limited training samples condition and low label accuracy condition, and better detection performance than PCA and STFT method. Experimental results demonstrate that proposed method can directly diagnosis the internal liquid level in PBT terminals and provide an effective maintenance policy.