## 5 Conclusions  
This paper proposed a method for identifying defects in a switchgear using audio from smartphones. First, audio files including background noise, mechanical vibration, and PD were selected and preprocessed. Then, the similarities and differences of each type of audio signal, in terms of time domain waveforms, frequency band energies, and harmonic components, were studied. Then, the time domain, frequency band energy features, MFCC features, and the present method were extracted and input into a SVM for recognition and to compare the accuracy. Finally, the method was experimentally validated, and the following conclusions were obtained:  
Figure 11: Experimental platform for identifying switchgear defects using smartphones. (**a**) PD defect and experimental circuits; (**b**) Experimental circuits for mechanical defects, which were loose screws.  
Figure 12: Recognition results for audio 1 and 2. The recognition result for background noise was 0, the recognition value for mechanical vibration was 1, and the recognition result for PD was 2.  
* Currently, widely available smartphones record audio in a frequency range of about 0-16 kHz, which makes the distinction between the three types of audio signals in terms of frequency band energy potentially small. Mechanical vibrations and PDs of switchgear may both have harmonic components, but there are differences in the fundamental frequency and harmonic distributions. In addition, mechanical vibration has a high autocorrelation in the time domain waveform, while background noise and PD have a low autocorrelation. Accurate identification of the three types of audio requires a combination of these features;
* In the recognition test for the time domain, frequency band energy, MFCC features, and the present features combined with SVM and comparison of the recognition rate, it was found that the present features had the highest accuracy in recognizing background noise, mechanical vibration, and PD, which provides a new idea for the screening of audio features for mechanical defects and PD;
* The popularity of smartphones makes audio files easily accessible. In the experiment, by applying this method to recognize the audio recorded with a smartphone, the result proved that this method could well recognize three types of audio signal. This method can help technicians to rapidly diagnose the defects of a switchgear, and it has good versatility and applicability.  
The number of selected audio files in this paper was limited, and the audio signals of special working conditions, such as switchgear breaking and closing operations, were not considered. Therefore, more types of background noise, mechanical vibration, and PD need to be collected, and the feature and recognition algorithm should be optimized to improve the recognition accuracy for various audio signals of a switchgear.  
Conceptualization, D.D.; methodology, D.D. and Q.L.; software, Q.L. and H.Y.; validation, Q.L. and Z.S.; formal analysis, Y.Y.; data curation, R.Q. and Y.Y.; writing--original draft preparation, Q.L.; writing--review and editing, R.Q.; funding acquisition, D.D. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control, Xiamen, 361024, China.  
Not applicable.  
Data available on request due to restrictions eg privacy or ethical. The data presented in this study are available on request from the corresponding author. These data are not disclosed to the public due to the commercial secrecy of the enterprise's production activities.  
The authors declare no conflict of interest.