HomePage >> Journals >> Transactions on Computer Science and Technology

Transactions on Computer Science and Technology

Transactions on Computer Science and Technology is an international comprehensive professional academic journal of Ivy Publisher, concerning the development of computer science theory and technology application on the combination of computer science and modern industrial technology. The main focus of the journal is the academic papers and comments of latest power electronics theoretical and technical research improvement in the fields of nature s... [More] Transactions on Computer Science and Technology is an international comprehensive professional academic journal of Ivy Publisher, concerning the development of computer science theory and technology application on the combination of computer science and modern industrial technology. The main focus of the journal is the academic papers and comments of latest power electronics theoretical and technical research improvement in the fields of nature science, engineering technology, economy and science, report of latest research result, aiming at providing a good communication platform to transfer, share and discuss the theoretical and technical development of computer science theory and technology development for professionals, scholars and researchers in this field, reflecting the academic front level, promote academic change and foster the rapid expansion of computer science theory and application technology.

The journal receives manuscripts written in Chinese or English. As for Chinese papers, the following items in English are indispensible parts of the paper: paper title, author(s), author(s)'affiliation(s), abstract and keywords. If this is the first time you contribute an article to the journal, please format your manuscript as per the sample paper and then submit it into the online submission system. Accepted papers will immediately appear online followed by printed hard copies by Ivy Publisher globally. Therefore, the contributions should not be related to secret. The author takes sole responsibility for his views.

ISSN Print:2327-090X

ISSN Online:2327-0918

Email:cst@ivypub.org

Website: http://www.ivypub.org/cst/

  0
  0

Paper Infomation

Deep Learning-Based Fault Prediction for Electrical Equipment

Full Text(PDF, 58KB)

Author: Fuyang Miao

Abstract: With the rapid advancement of deep learning and the increasing availability of large-scale data, fault prediction for electrical equipment has become a vital area of research. This paper explores the application of deep learning techniques in predicting faults within electrical systems, focusing on the challenges and methodologies that can enhance prediction accuracy and system reliability. Traditional fault prediction methods, such as threshold-based models and statistical approaches, often fall short in handling complex, nonlinear data and large-scale systems. In contrast, deep learning models, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), have shown significant promise in learning from large and diverse datasets to detect subtle patterns that indicate potential failures. This paper also discusses the importance of data collection and preprocessing, model training, evaluation metrics, and cross-validation techniques, all of which contribute to improving the robustness and accuracy of fault prediction models. Despite the advancements, challenges remain, such as data quality, model interpretability, and computational efficiency. The paper concludes by outlining future research directions and the potential impact of emerging technologies like the Internet of Things (IoT) and edge computing in the field of fault prediction.

Keywords: Deep Learning, Fault Prediction, Electrical Equipment, Convolutional Neural Networks, Recurrent Neural Networks, Data Preprocessing, Model Evaluation

References:

[1] Huibin F, Ying L. A deep learning-based approach for electrical equipment remaining useful life prediction[J]. Autonomous Intelligent Systems,2022,2(1):

[2] Chellamuthu S, Sekaran C E. Retraction Note: Fault detection in electrical equipment’s images by using optimal features with deep learning classifier[J].Multimedia Tools and Applications,2024,83(35):83575-83575.

[3] Wang P, Tan H, Ji C. Prediction of mechanical equipment fault diagnosis based on IPSO-GRU deep learning algorithm[J].Applied Mathematics and Nonlinear Sciences,2024,9(1)::

[4] Yongtao D, Hua W, Kaixiang Z. Design of Fault Prediction System for Electromechanical Sensor Equipment Based on Deep Learning[J].Computational Intelligence and Neuroscience,2022,20223057167-3057167.

[5] Chellamuthu S, Sekaran C E. Fault detection in electrical equipment’s images by using optimal features with deep learning classifier[J].Multimedia Tools and Applications,2019,78(19):27333-27350.

[6] Chellamuthu S, Sekaran C E. Retraction Note: Fault detection in electrical equipment’s images by using optimal features with deep learning classifier[J].Multimedia Tools and Applications,2024,83(35):83575-83575.

[7] Wouter S, Dillam R D, Toon G, et al. Detection and recognition of batteries on X-Ray images of waste electrical and electronic equipment using deep learning[J].Resources, Conservation and Recycling,2020,105246.

[8] Xiaojin G, Qi Y, Menglin W, et al. A Deep Learning Approach for Oriented Electrical Equipment Detection in Thermal Images[J].IEEE Access,2018,641590-41597.

Privacy Policy | Copyright © 2011-2025 Ivy Publisher. All Rights Reserved.

Contact: customer@ivypub.org