Multichannel consecutive data cross-extraction with 1DCNN-attention for diagnosis of power transformer

• URL: http://arxiv.org/abs/2310.07323v1
• Date: Wed, 11 Oct 2023 09:14:17 GMT
• Title: Multichannel consecutive data cross-extraction with 1DCNN-attention for diagnosis of power transformer
• Abstract summary: This article presents the structure of multichannel consecutive data cross-extraction (MCDC) for transformer diagnosis. For the better accommodation in scenario of transformer diagnosis, one dimensional convolution neural network attention (1DCNN-attention) mechanism is introduced and offers a more efficient solution. The effectiveness of MCDC and the superior generalization ability, compared with other algorithms, are validated in experiments conducted on a dataset collected from real operation cases of power transformer.
• Score: 3.8288189143989517
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Power transformer plays a critical role in grid infrastructure, and its diagnosis is paramount for maintaining stable operation. However, the current methods for transformer diagnosis focus on discrete dissolved gas analysis, neglecting deep feature extraction of multichannel consecutive data. The unutilized sequential data contains the significant temporal information reflecting the transformer condition. In light of this, the structure of multichannel consecutive data cross-extraction (MCDC) is proposed in this article in order to comprehensively exploit the intrinsic characteristic and evaluate the states of transformer. Moreover, for the better accommodation in scenario of transformer diagnosis, one dimensional convolution neural network attention (1DCNN-attention) mechanism is introduced and offers a more efficient solution given the simplified spatial complexity. Finally, the effectiveness of MCDC and the superior generalization ability, compared with other algorithms, are validated in experiments conducted on a dataset collected from real operation cases of power transformer. Additionally, the better stability of 1DCNN-attention has also been certified.

Related papers

• Feature-Weighted MMD-CORAL for Domain Adaptation in Power Transformer Fault Diagnosis [2.7898966850590625]
  This work proposes a feature-weighted domain adaptation technique that combines Maximum Mean Discrepancy (MMD) and Correlation Alignment (CORAL) with feature-specific weighting (MCW)<n> Experimental evaluations on datasets for power transformers demonstrate the effectiveness of the proposed method, which achieves a 7.9% improvement over Fine-Tuning and a 2.2% improvement over MMD-CORAL (MC)
  arXiv  Detail & Related papers  (2025-05-20T20:44:35Z)
• DINT Transformer [5.990713912057883]
  DIFF Transformer addresses the issue of irrelevant context interference by introducing a differential attention mechanism. We propose DINT Transformer, which extends DIFF Transformer by incorporating a differential-integral mechanism.
  arXiv  Detail & Related papers  (2025-01-29T08:53:29Z)
• Dual-TSST: A Dual-Branch Temporal-Spectral-Spatial Transformer Model for EEG Decoding [2.0721229324537833]
  We propose a novel decoding architecture network with a dual-branch temporal-spectral-spatial transformer (Dual-TSST) Our proposed Dual-TSST performs superiorly in various tasks, which achieves the promising EEG classification performance of average accuracy of 80.67%. This study provides a new approach to high-performance EEG decoding, and has great potential for future CNN-Transformer based applications.
  arXiv  Detail & Related papers  (2024-09-05T05:08:43Z)
• Efficient Diffusion Transformer with Step-wise Dynamic Attention Mediators [83.48423407316713]
  We present a novel diffusion transformer framework incorporating an additional set of mediator tokens to engage with queries and keys separately. Our model initiates the denoising process with a precise, non-ambiguous stage and gradually transitions to a phase enriched with detail. Our method achieves a state-of-the-art FID score of 2.01 when integrated with the recent work SiT.
  arXiv  Detail & Related papers  (2024-08-11T07:01:39Z)
• Multi-Source and Test-Time Domain Adaptation on Multivariate Signals using Spatio-Temporal Monge Alignment [59.75420353684495]
  Machine learning applications on signals such as computer vision or biomedical data often face challenges due to the variability that exists across hardware devices or session recordings. In this work, we propose Spatio-Temporal Monge Alignment (STMA) to mitigate these variabilities. We show that STMA leads to significant and consistent performance gains between datasets acquired with very different settings.
  arXiv  Detail & Related papers  (2024-07-19T13:33:38Z)
• MVMTnet: A Multi-variate Multi-modal Transformer for Multi-class Classification of Cardiac Irregularities Using ECG Waveforms and Clinical Notes [4.648677931378919]
  Deep learning can be used to optimize diagnosis and patient monitoring for clinical-based applications. For cardiovascular disease, one such condition where the rising number of patients increasingly outweighs the availability of medical resources in different parts of the world, a core challenge is the automated classification of various cardiac abnormalities. The proposed novel multi-modal Transformer architecture would be able to accurately perform this task while demonstrating the cross-domain effectiveness of Transformers.
  arXiv  Detail & Related papers  (2023-02-21T21:38:41Z)
• DTAAD: Dual Tcn-Attention Networks for Anomaly Detection in Multivariate Time Series Data [0.0]
  We propose an anomaly detection and diagnosis model, DTAAD, based on Transformer and Dual Temporal Convolutional Network (TCN) scaling methods and feedback mechanisms are introduced to improve prediction accuracy and expand correlation differences. Our experiments on seven public datasets validate that DTAAD exceeds the majority of currently advanced baseline methods in both detection and diagnostic performance.
  arXiv  Detail & Related papers  (2023-02-17T06:59:45Z)
• Towards Data-Efficient Detection Transformers [77.43470797296906]
  We show most detection transformers suffer from significant performance drops on small-size datasets. We empirically analyze the factors that affect data efficiency, through a step-by-step transition from a data-efficient RCNN variant to the representative DETR. We introduce a simple yet effective label augmentation method to provide richer supervision and improve data efficiency.
  arXiv  Detail & Related papers  (2022-03-17T17:56:34Z)
• Can Transformers be Strong Treatment Effect Estimators? [86.32484218657166]
  We develop a general framework based on the Transformer architecture to address a variety of treatment effect estimation problems. Our methods are applied to discrete, continuous, structured, or dosage-associated treatments. Our experiments with Transformers as Treatment Effect Estimators (TransTEE) demonstrate that these inductive biases are also effective on the sorts of estimation problems and datasets that arise in research aimed at estimating causal effects.
  arXiv  Detail & Related papers  (2022-02-02T23:56:42Z)
• Transfer Learning for Fault Diagnosis of Transmission Lines [55.971052290285485]
  A novel transfer learning framework based on a pre-trained LeNet-5 convolutional neural network is proposed. It is able to diagnose faults for different transmission line lengths and impedances by transferring the knowledge from a source neural network to predict a dissimilar target dataset.
  arXiv  Detail & Related papers  (2022-01-20T06:36:35Z)
• TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data [13.864161788250856]
  TranAD is a deep transformer network based anomaly detection and diagnosis model. It uses attention-based sequence encoders to swiftly perform inference with the knowledge of the broader temporal trends in the data. TranAD can outperform state-of-the-art baseline methods in detection and diagnosis performance with data and time-efficient training.
  arXiv  Detail & Related papers  (2022-01-18T19:41:29Z)
• Dispensed Transformer Network for Unsupervised Domain Adaptation [21.256375606219073]
  A novel unsupervised domain adaptation (UDA) method named dispensed Transformer network (DTNet) is introduced in this paper. Our proposed network achieves the best performance in comparison with several state-of-the-art techniques.
  arXiv  Detail & Related papers  (2021-10-28T08:27:44Z)
• nnFormer: Interleaved Transformer for Volumetric Segmentation [50.10441845967601]
  We introduce nnFormer, a powerful segmentation model with an interleaved architecture based on empirical combination of self-attention and convolution. nnFormer achieves tremendous improvements over previous transformer-based methods on two commonly used datasets Synapse and ACDC.
  arXiv  Detail & Related papers  (2021-09-07T17:08:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.