Adaptive Diagnosis for Transformer with Unknown Faults Based on Antenna-Augmented RFID Sensor and Deep Learning

In engineering practice, transformers always have one or more new mechanical fault types that have not been found yet, which would decrease the diagnosis accuracy. This work introduces an adaptive fault diagnosis approach for transformer mechanical failure in incubation period with unknown fault typ...

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Vydané v:IEEE sensors journal Ročník 23; číslo 17; s. 1
Hlavní autori: Wang, Tao, Xiao, Yanxia, Li, Bing, He, Yigang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.09.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
adaptive fault diagnosis
antenna-augmented RFID sensor
Antennas
cluster
Clusters
Deep learning
Fault diagnosis
Faults
Feature extraction
Machine learning
Particle swarm optimization
Power transformers
Radio frequency identification
Radiofrequency identification
Sensors
sparse stacked denoising autoencoder
Support vector machines
transformer
Transformers
Vibrations
ISSN:1530-437X, 1558-1748
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Shrnutí:In engineering practice, transformers always have one or more new mechanical fault types that have not been found yet, which would decrease the diagnosis accuracy. This work introduces an adaptive fault diagnosis approach for transformer mechanical failure in incubation period with unknown fault types. An antenna-augmented radio frequency identification (RFID) sensor is used to acquire vibration signal and position information. The faults' signals of transformer are generally featured as nonlinear, moreover, it always contains unknown faults, making it hard to extract discriminative features from the obtained signals. Therefore, an adaptive fault diagnosis method consisting of sparse stacked denoising autoencoder (SSDA) and unknown fault cluster is proposed. The SSDA is adopted to extract robust feature and quantum particle swarm optimization (QPSO) is employed to find the optimal the layer nodes structure and learning rate of the SSDA model. The SSDA model along with a full-connected layer can divide the data into known faults and unknown faults. The knowns faults are classified by support vector machine (SVM), and the unknown faults are divided into different categories by using the fault cluster. The experiments validate that the augmented RFID sensor has reliable communication performance within the distance of 17.5m. Moreover, the proposed method achieves prominent results for transformer fault diagnosis with unknown faults.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2023.3299081