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  • Open Access


    Deep Learning Based Power Transformer Monitoring Using Partial Discharge Patterns

    D. Karthik Prabhu1,*, R. V. Maheswari2, B. Vigneshwaran2

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1441-1454, 2022, DOI:10.32604/iasc.2022.024128

    Abstract Measurement and recognition of Partial Discharge (PD) in power apparatus is considered a protuberant tool for condition monitoring and assessing the state of a dielectric system. During operating conditions, PD may occur either in the form of single and multiple patterns in nature. Currently, for PD pattern recognition, deep learning approaches are used. To evaluate spatial order less features from the large-scale patterns, a pre-trained network is used. The major drawback of traditional approaches is that they generate high dimensional data or requires additional steps like dictionary learning and dimensionality reduction. However, in real-time applications, interference incorporated in the measured… More >

  • Open Access


    Leak Detection of Gas Pipelines Based on Characteristics of Acoustic Leakage and Interfering Signals

    Lingya Meng1, *, Cuiwei Liu2, Liping Fang2, Yuxing Li2, Juntao Fu3

    Sound & Vibration, Vol.53, No.4, pp. 111-128, 2019, DOI:10.32604/sv.2019.03835

    Abstract When acoustic method is used in leak detection for natural gas pipelines, the external interferences including operation of compressor and valve, pipeline knocking, etc., should be distinguished with acoustic leakage signals to improve the accuracy and reduce false alarms. In this paper, the technologies of extracting characteristics of acoustic signals were summarized. The acoustic leakage signals and interfering signals were measured by experiments and the characteristics of time-domain, frequency-domain and time-frequency domain were extracted. The main characteristics of time-domain are mean value, root mean square value, kurtosis, skewness and correlation function, etc. The features in frequency domain were obtained by… More >

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