Mechanical fault diagnosis of gas-insulated switchgear based on saliency feature of auditory brainstem response under noise background

Abstract

The mechanical fault of gas-insulated switchgear (GIS) seriously threatens the security of the power grid. Recently, acoustic-based fault diagnosis methods, which have the advantage of non-contact measurement, have been applied to the GIS mechanical fault diagnosis, but vulnerable to the interference of the background noise. To improve the capacity of the acoustic-based GIS fault diagnosis under noise background, by simulating the sound feature extraction ability and anti-noise ability of human auditory system, a novel GIS mechanical fault diagnosis method based on saliency feature of auditory brainstem response (SFABR) is proposed. First, an auditory saliency model, which considers both the auditory periphery and the auditory nerve center was constructed by combining the deep auditory model and the saliency model. After processing GIS emitted acoustic signal, the auditory brainstem response (ABR) was obtained, and the saliency features of the ABR were extracted to obtain the SFABR. Then, the characteristic frequency distribution of the auditory saliency model was adjusted to make it more suitable for the spectral characteristics of the GIS sound signal. Finally, the SFABR was mapped to a two-dimensional CNN to train a model for GIS mechanical fault diagnosis. This method simulates the process of auditory response extraction and the selection effect of auditory attention on sound elements. 110kV three-phase GIS fault simulation experiment shows that for GIS mechanical faults, the diagnosis method based on SFABR can obtain 96.1% fault identification accuracy. In different noise environments, compared with the traditional acoustic-based fault diagnosis methods, this method has stronger anti-noise performance, and can more effectively realize the identification of GIS mechanical failure types. In future research, the method can be further extended to fault diagnosis of more types of power equipment.

Gas-insulated switch-gear mechanical fault detection based on acoustic feature analysis using a multi-state pre-trained neural network

“…The cross-entropy loss is used to evaluate the proposed method during training as given by equation (19).…”

“…manually extracting features, many researchers have begun to utilize deep learning techniques to develop GIS mechanical fault diagnosis methods. To improve the performance of acoustic signals based GIS mechanical fault diagnosis methods in noisy environments, Ji et al [19] proposed a novel method that combines SFABR features with the deep Resnet34 model, and achieved 96.1% of the diagnostic accuracy. Zhuang et al [20] proposed a novel GIS fault diagnosis method based on convolutional neural network and bidirectional long and short-term memory network.…”