## 1 Introduction  
Switchgear play an important role in power systems as widely used electrical equipment for circuit isolation and protection against overloads and system faults [1]. During long-term operation, various defects in a switchgear inevitably occur. Studies have shown that the defects of a switchgear mainly include insulation defects, mechanical defects, and overheating defects [2]. Insulation defects are usually accompanied by PD, including corona discharge, suspension discharge, and internal discharge [3]. Mechanical defects include conductor contact loosening, shield loosening, and bolt loosening [4]. Either type of defect can lead to power system failures and cause serious economic losses. Therefore, the timely detection of possible defects and hidden dangers in the operation of a switchgear can ensure the reliability and safety of power system operation [5]. For the detection of a single defect type, PD can be detected via acoustic [3, 6] and electrical methods [7, 8], while mechanical defects are mainly detected via vibration [1, 4]and acoustic methods [9; 10]. However, when an abnormality occurs in power equipment, selecting which defect detection method to use first is a problem. Although it is feasible to identify defect types on a case-by-case basis, the complexity of the process and the installation of specialized inspection equipment is not conducive to the initial identification of defects. To enhance detection efficiency, the literature [11; 12] uses composite sensors for the simultaneous detection of PD and mechanical defects, but the preparations cost for this type of sensor are high, and there have been few applications in practical situations. All these methods have advantages and disadvantages for detecting switchgear defects, considering their complexity and cost of use, and acoustic detection has the advantages of inexpensive sensors, anti-electromagnetic interference, easy detection, and monitoring the operation of equipment online [13]. If PD and mechanical defects can be detected and identified using an acoustic method synchronously, this would be more economical, convenient, and applicable compared to other methods. Currently, the use of acoustics to detect and identify mechanical defects or PDs in electrical equipment is being researched, and good progress has been made. In [9], fully integrated empirical mode decomposition was applied to the audio signals of induction motors, and mechanical defects were successfully detected after analyzing the edge frequency characteristics. In [10], acoustic pressure sensors were used to obtain audio intensity cloud maps of mechanical defects, and five eigenvalues extracted using a gray-level covariance matrix (GLCM) were used to realize the diagnosis of mechanical defects in a gas-insulated metal-enclosed switchgear (GIS). In [14], the accurate recognition of acoustic emission (AE) signals of electric tree branches was achieved using an artificial neural network (ANN) and SVM. In [15], MFCC features were extracted for AE signals, and an SVM classifier optimized using sequential minimum optimization (SMO) was used to accurately identify four types of PD defect in GIS. In the above study, the acoustic detection and identification of mechanical defects used audio signals at low frequency, while PD defects used AE signals at high frequency; however, the two defects were detected and identified separately. In [3], a comprehensive identification method was proposed to distinguish the AE signals of PD and background noise, but background noise was not counted as a defect type. To enhance inspection efficiency and reduce costs, the method of using the recording function of smartphones to identify PD and mechanical defects of switchgear simultaneously seems feasible. However, few papers have used smartphones to record the audio of switchgear and identify defects. A related comparison is the use of smartphones for diagnosing mechanical defects in induction motors [16].