Mechanical Fault Diagnosis of Power Transformer by GFCC Time-frequency Map of Acoustic Signal and Convolutional Neural Network

Geng, Qi; Wang, F. H.; Zhou, Dake

doi:10.1109/ispec48194.2019.8975318

Cited by 9 publications

(11 citation statements)

References 7 publications

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“…On the other hand, since the over-voltage supplied to the transformer causes the overheating and deformation of the windings to result in failure, it is important to determine the abnormal supply voltage levels that cause the failure of the transformer. Recently, methods for the monitoring and fault diagnosis of transformers have been studied as advanced and complex methods [ 15 , 16 , 17 , 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

“…The online monitoring method for the transformer should not interfere with the normal operation of the transformer, nor should it require a special operating mode for monitoring. To satisfy these requirements, some studies have recently reported on the monitoring of the mechanical condition of transformers and diagnosis of faults by measuring acoustic signals to ensure safe and reliable transformer operation [ 15 , 16 , 17 , 18 , 19 , 20 ]. Note that since the acoustic signal is generated by the vibration of the windings and cores of the transformer, the acoustic signal contains sufficient information to determine the transformer state.…”

Section: Introductionmentioning

confidence: 99%

“…In the time domain, the magnitude of a signal for a certain time is analyzed. In the frequency domain, the distribution of each frequency is analyzed [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ]. Additionally, in the time–frequency domain, the vibration and amplitude of the frequency according to a certain time are analyzed.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, in the time–frequency domain, the vibration and amplitude of the frequency according to a certain time are analyzed. Geng [ 18 ] used the time–frequency spectrogram of the acoustic signal and AlexNet [ 27 ] as a method for diagnosing the mechanical failure of the dry-type transformer. Previous methods have often used the acoustic vibration signal to monitor and analyze the mechanical faults, such as winding deformation or loosening defects [ 15 , 16 , 17 , 18 , 19 , 20 ], which has strong anti-interference and high sensitivity advantages.…”

Section: Introductionmentioning

confidence: 99%

“…Geng [ 18 ] used the time–frequency spectrogram of the acoustic signal and AlexNet [ 27 ] as a method for diagnosing the mechanical failure of the dry-type transformer. Previous methods have often used the acoustic vibration signal to monitor and analyze the mechanical faults, such as winding deformation or loosening defects [ 15 , 16 , 17 , 18 , 19 , 20 ], which has strong anti-interference and high sensitivity advantages. However, the vibration signal is usually obtained through the vibration sensor placed on the box wall of the transformer.…”

Section: Introductionmentioning

confidence: 99%

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Power Transformer Voltages Classification with Acoustic Signal in Various Noisy Environments

Kim

Lee

2022

Sensors

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Checking the stable supply voltage of a power distribution transformer in operation is an important issue to prevent mechanical failure. The acoustic signal of the transformer contains sufficient information to analyze the transformer conditions. However, since transformers are often exposed to a variety of noise environments, acoustic signal-based methods should be designed to be robust against these various noises to provide high accuracy. In this study, we propose a method to classify the over-, normal-, and under-voltage levels supplied to the transformer using the acoustic signal of the transformer operating in various noise environments. The acoustic signal of the transformer was converted into a Mel Spectrogram (MS), and used to classify the voltage levels. The classification model was designed based on the U-Net encoder layers to extract and express the important features from the acoustic signal. The proposed approach was used for its robustness against both the known and unknown noise by using the noise rejection method with U-Net and the ensemble model with three datasets. In the experimental environments, the testbeds were constructed using an oil-immersed power distribution transformer with a capacity of 150 kVA. Based on the experimental results, we confirm that the proposed method can improve the classification accuracy of the voltage levels from 72 to 88 and to 94% (baseline to noise rejection and to noise rejection + ensemble), respectively, in various noisy environments.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Power Transformer Voltages Classification with Acoustic Signal in Various Noisy Environments

Kim

Lee

2022

Sensors

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Voice activity detection for audio signal of voyage data recorder using residue network and attention mechanism

Zhang

Liu

et al. 2022

Ships and Offshore Structures

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

Ji,

Liu,

Wang

et al. 2023

Meas. Sci. Technol.

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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. 110kV three-phase GIS fault simulation experiment shows that for GIS mechanical faults, the diagnosis method based on SFABR can obtain 96.1% fault identification accuracy. In different noise environments, compared with the traditional acoustic-based fault diagnosis methods, this method has stronger anti-noise performance, and can more effectively realize the identification of GIS mechanical failure types. In future research, the method can be further extended to fault diagnosis of more types of power equipment.

show abstract

Mechanical Fault Diagnosis of Power Transformer by GFCC Time-frequency Map of Acoustic Signal and Convolutional Neural Network

Cited by 9 publications

References 7 publications

Power Transformer Voltages Classification with Acoustic Signal in Various Noisy Environments

Power Transformer Voltages Classification with Acoustic Signal in Various Noisy Environments

Voice activity detection for audio signal of voyage data recorder using residue network and attention mechanism

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

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