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

作者：Haitao Ji, Houguang Liu, Jie Wang, Guogang Yuan, Jianhua Yang, Shanguo Yang · 发表于：Measurement Science and Technology · 年份：2023 · DOI：10.1088/1361-6501/acfbf0 · 被引用次数：11 · 研究领域：Machine Fault Diagnosis Techniques、High voltage insulation and dielectric phenomena、Structural Health Monitoring Techniques

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. The 110 kV three-phase GIS fault simulation experiment shows that for GIS mech...