A Novel IEPE AE-Vibration-Temperature-Combined Intelligent Sensor for Defect Detection of Power Equipment

Zhang, Zhaoyu; Wang, Haotian; Chen, Huan; Shi, Tianyi; Song, Yanfeng; Han, Xutao; Li, Junhao

doi:10.1109/tim.2023.3272046

Cited by 9 publications

(2 citation statements)

References 36 publications

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“…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].…”

Section: Introductionmentioning

confidence: 99%

Audio General Recognition of Partial Discharge and Mechanical Defects in Switchgear Using a Smartphone

Dai,

Liao,

Sang

et al. 2023

Applied Sciences

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Mechanical defects and partial discharge (PD) defects can appear in the indoor switchgear of substations or distribution stations, making the switchgear a safety hazard. However, traditional acoustic methods detect and identify these two types of defects separately, ignoring the general recognition of audio signals. In addition, the process of using testing equipment is complex and costly, which is not conducive to timely testing and widespread application. To assist technicians in making a quick preliminary diagnosis of defect types for switchgear, improve the efficiency of the subsequent overhaul, and reduce the cost of detection, this paper proposes a general audio recognition method for identifying defects in switchgear using a smartphone. Using this method, we can analyze and identify audio and video files recorded with smartphones and synchronously distinguish background noise, mechanical vibration, and PD audio signals, which have good applicability within a certain range. When testing the feasibility of using smartphones to identify three types of audio signal, through characterizing 12 sets of live audio and video files provided by technicians, it was found that there were similarities and differences in these characteristics, such as the autocorrelation, density, and steepness of the waveforms in the time domain, and the band energy and harmonic components of the frequency spectrum, and new combinations of features were proposed as applicable. To compare the recognition performance for features in the time domain, frequency band energy, Mel-frequency cepstral coefficient (MFCC), and this method, feature vectors were input into a support vector machine (SVM) for a recognition test, and the recognition results showed that the the present method had the highest recognition accuracy. Finally, a set of mechanical defects and PD defects were set up for a switchgear, for practical verification, which proved that this method was general and effective.

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Section: Introductionmentioning

confidence: 99%

Audio General Recognition of Partial Discharge and Mechanical Defects in Switchgear Using a Smartphone

Dai,

Liao,

Sang

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…10 Besides, temperature monitoring is also essential for scientific or industrial equipment to avoid any kind of disaster or failure. 11,12 However, conventional commercial electronic temperature sensors require heavy energy supply systems and are vulnerable to electromagnetic interference. 13 Compared to the electronic counterpart, optic-based temperature sensors exhibit the advantages of being non-contact and having higher sensitivity and low detection limits.…”

Section: Introductionmentioning

confidence: 99%