ArticleHeliyon2025
Integrative fault diagnostic analytics in transformer windings: Leveraging logistic regression, discrete wavelet transform, and neural networks.
Article in Heliyon, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Harnessing the potential of lead-free BiFeORSC advances · 2026Article
- Biological Profiling of Postbiotics of Lactobacillus plantarum: Antibacterial, Antioxidant, and Cytotoxic Properties Under In Vitro and Food Circumstances.Probiotics and antimicrobial proteins · 2026Article
- Fault diagnosis method of rolling-element bearings via FBEWT and EIRVM.Scientific reports · 2026Article
- A new intelligent method based on cognitive artificial intelligence for predicting transformer remaining useful life.MethodsX · 2025Article
- Electrical Diagnosis Techniques for Power Transformers: A Comprehensive Review of Methods, Instrumentation, and Research Challenges.Sensors (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protection of transformers is crucial in the power industry due to their susceptibility to various electrical and mechanical faults over time. Traditional methods like Frequency Response Analysis (FRA) have limitations in accurately diagnosing these faults. This paper highlights the potential of combining advanced signal processing techniques with machine learning algorithms by presenting an innovative hybrid model for accurately detecting transformer winding faults, utilizing Logistic Regression, Artificial Neural Networks (ANN) and Discrete Wavelet Transform (DWT). The primary novelty of this approach lies in the use of Logistic Regression to evaluate the impact of each wavelet decomposition, which aids in selecting the most effective wavelet bases, reducing data volume, and decreasing computational complexity. By integrating these methods, the proposed model significantly enhances fault detection accuracy and system performance. The effectiveness of the algorithm is validated through a practical case study, demonstrating a 97 % success rate in detecting transformer faults and reducing misclassification to 2.9 %.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.