Results 1 to 10 of about 14,240 (207)
Transformer fault diagnosis using continuous sparse autoencoder. [PDF]
Springerplus, 2016 This paper proposes a novel continuous sparse autoencoder (CSAE) which can be used in unsupervised feature learning. The CSAE adds Gaussian stochastic unit into activation function to extract features of nonlinear data. In this paper, CSAE is applied to solve the problem of transformer fault recognition. Firstly, based on dissolved gas analysis method,
Wang L, Zhao X, Pei J, Tang G.
europepmc +3 more sources
Convolutional Neural Network-Based Transformer Fault Diagnosis Using Vibration Signals [PDF]
Sensors, 2023 Fast and accurate fault diagnosis is crucial to transformer safety and cost-effectiveness. Recently, vibration analysis for transformer fault diagnosis is attracting increasing attention due to its ease of implementation and low cost, while the complex ...
Chao Li +5 more
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Transformer fault diagnosis method based on TLR-ADASYN balanced dataset [PDF]
Scientific Reports, 2023 As the cornerstone of transmission and distribution equipment, power transformer plays a very important role in ensuring the safe operation of power system.
Shan Guan, Haiqi Yang, Tongyu Wu
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Power transformer fault diagnosis method based on multi source signal fusion and fast spectral correlation [PDF]
Scientific ReportsAddressing the issues that signal measured by a single sensor can not provide a complete description of power transformer fault states and the problems that selection of signal features relies on manual experience, a method based on multi source signal ...
Shan Guan +3 more
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Transformer fault diagnosis based on adversarial generative networks and deep stacked autoencoder [PDF]
HeliyonEstablishing a deep learning model for transformer fault diagnosis using transformer oil chromatogram data requires a large number of fault samples. The lack and imbalance of oil chromatogram data can lead to overfitting, lack of representativeness of ...
Lei Zhang +5 more
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Low-Power Chemiresistive Gas Sensors for Transformer Fault Diagnosis. [PDF]
MoleculesDissolved gas analysis (DGA) is considered to be the most convenient and effective approach for transformer fault diagnosis. Due to their excellent performance and development potential, chemiresistive gas sensors are anticipated to supersede the traditional gas chromatography analysis in the dissolved gas analysis of transformers.
Mei H, Peng J, Xu D, Wang T.
europepmc +4 more sources
Research on transformer fault diagnosis method based on ACGAN and CGWO-LSSVM [PDF]
Scientific ReportsThis paper proposes a transformer fault diagnosis method based on ACGAN and CGWO-LSSVM to address the problem of misjudgment and low diagnostic accuracy caused by the small number and uneven distribution of some fault samples in transformer fault ...
Shan Guan, Tong-yu Wu, Hai-qi Yang
doaj +2 more sources
Transformer Fault Diagnosis Based on Hybrid Sampling and Support Vector Machines
Zhongguo dianli, 2021 Aiming at the impact of transformer imbalanced data set on transformer fault diagnosis model. A transformer fault diagnosis method based on hybrid sampling and support vector machines (SVM) is proposed.
Liang LI +6 more
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A Siamese Vision Transformer for Bearings Fault Diagnosis
Micromachines, 2022 Fault diagnosis methods based on deep learning have progressed greatly in recent years. However, the limited training data and complex work conditions still restrict the application of these intelligent methods. This paper proposes an intelligent bearing fault diagnosis method, i.e., Siamese Vision Transformer, suiting limited training data and complex
Qiuchen He +5 more
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Petri net based transformer fault diagnosis [PDF]
2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512), 2004 The reduction of the time needed for transformer fault diagnosis is an important task for transformer users. In this paper, Petri nets are exploited, in order to simulate the transformer fault diagnosis process and to define the actions followed to repair the transformer.
P.S. Georgilakis +2 more
openaire +1 more source