## 1 Introduction  
Effective detection technology focusing on the operating state of high-voltage electrical equipment is an important means to maintain the safe operation of the power grid [1]. As a key control and protection device, gas-insulated switchgear (GIS) plays an important role in high-voltage electrical equipment [2]. With the development of artificial intelligence technology, intelligent mechanical fault-detection methods have been successfully applied to various kinds of mechanical equipment [3; 4; 5], such as gear boxes and wind turbines among others. At the same time, related technical methods have been extended to the field of high-voltage circuit-breaker state detection, and have achieved phased results [6; 7; 8]. Therefore, we believe the establishment of intelligent, information-based, automated GIS mechanical defect-detection systems will become an inevitable trend in the development of high-voltage switchgear. At present, in the field of GIS state detection, experts from various countries are devoted to the study of characterization, detection methods, and prevention techniques of partial discharges in GIS. A real-time measurement system for partial discharge defect strength based on phase decomposition was developed by using an ultra-high frequency sensor [9]. A partial discharge sensor diagnosis method based on a silicon photomultiplier (SiPM) was proposed [10]; it promotes the partial discharge process of optical detection from laboratory simulations to practical applications. Literature [11] analyzes the spectral characteristics of vibration at different discharge levels, and proposes a partial discharge defect-detection technology based on vibration information. Based on effective measurement, some scholars have carried out in-depth research on feature-extraction technology of discharge information [12; 13; 14]. As in the literature [12; 13], the short-time Fourier transformation method is used to describe the local time-frequency characteristics of discharge. Based on discrete wavelet transformation, [14] eliminated the capacitive component interference in a phase-resolved partial discharge (PRPD) signal of a GIS isolator, and improved the sensitivity of GIS partial discharge integration. In [15], by introducing the membership weight function and the k-means clustering method, the difference between the noise signal and the discharge fault are well recognized. Additionally, the GIS partial discharge fault is accurately detected. The works [16; 17] adopt a fuzzy decision tree and the neural network model realizes the diagnosis of GIS partial discharge faults and obtains good diagnostic accuracy. As the number of neural network training layers increases and the error back-propagation optimization method improves, the application of deep-learning diagnostic methods that do not depend on the artificial prior feature-extraction process is widely expected. The literature in [18; 19] respectively uses a recurrent neural network (RNN) and the stacked self-encoder model to realize self-learning characteristics of the partial discharge process and the intelligent identification of fault types. It can be seen from the above literature that GIS partial discharge detection has achieved fruitful results. However, there are few studies on GIS mechanical fault detection. Literature [20] combined with the finite element simulation software ANSYS, analyzed the electromagnetic characteristics of GIS transients, and provided the theoretical basis of GIS mechanical defect-detection technology based on vibration information. Then [21] proposed the GIS mechanical fault feature-extraction method based on voiceprints. A GIS mechanical state detection method was proposed in [22] based on vibration information, and formed an analysis system for abnormal vibration. Literature [23; 24] based on GIS vibration information, respectively, using the fuzzy C means algorithm and the K-L divergence theory together implements the mechanical fault-detection process.