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Circuit Breaker Target Sound Signal Detection Method based on VAD and SVDD Algorithms
编号：59 访问权限：仅限参会人 更新：2024-04-11 21:48:04 浏览：608次 口头报告

报告开始：2023年12月09日 14:00（Asia/Shanghai）

报告时间：15min

所在会场：[S7] Power system protection and control [S7] Power system protection and control

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摘要
Currently, the closed-set recognition algorithm is the primary research method for switchgear circuit breaker fault diagnosis. For real-time acquisition of the operation of a long voice signal from a switchgear circuit breaker, a significant number of silent signal segments and non-circuit breaker action sound signals are present; these types of signals will cause false alarms with the closed-set recognition algorithm. As a result, this article proposes a method for extracting the target sound signal by combining the Deep SVDD algorithm with the voice activity detection (VAD) algorithm. Initially, the double threshold endpoint detection algorithm is employed to intercept lengthy sound samples in order to eliminate voiceless segment sound signals. Subsequently, the Deep SVDD algorithm is utilized to train the model to acquire the capability of single classification, thereby excluding abnormal sound signals that do not correspond to circuit breaker actions and diminishing the rate of false alarms.
关键词
Circuit breaker, Voice detection, Deep SVDD, Self-Encoder Network, VAD.
报告人
Yang Zhencheng
Student Southeast University

稿件作者
Yang Zhencheng Southeast University
Wu Zaijun Southeast University
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重要日期

  • 会议日期

    12月08日

    2023

    至

    12月10日

    2023

  • 11月01日 2023

    初稿截稿日期

  • 12月10日 2023

    注册截止日期

主办单位
IEEE IAS
承办单位
Southwest Jiaotong University (SWJTU)
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