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结合GAF与CNN的操动机构弹簧储能状态智能辨识OA北大核心CSTPCD

Intelligent identification method of spring energy storage state of circuit breaker operating mechanism based on GAF and CNN

中文摘要英文摘要

操动机构弹簧储能状态的鲁棒辨识对断路器服役性能有重要影响,如何建立起采样信号与弹簧储能状态之间的映射关系是制约其广泛应用的关键.针对这一问题,结合格拉姆角场(Gramian angular field,GAF)与卷积神经网络(convolutional neural network,CNN),提出了一种弹簧储能状态智能辨识方法,并成功应用于断路器操动机构.采用格拉姆角场将采集到的时域信号进行二维化处理,并利用其进行操动机构动态特性演化过程的追踪.断路器操动机构状态辨识实验验证了所提出的智能诊断方法有效性(识别成功率接近100.00%),为断路器在役状态的鲁棒识别提供一种可能.

Robust identification of the spring energy state in circuit breaker operating mechanism is of great significance for maintaining service performance. However,establishing a mapping relationship between the sampled signal and the spring energy storage state remains a key challenge limiting its widespread application. To solve this problem,this study proposes an intelligent identification method that combines Gramian angular field (GAF) and convolutional neural network(CNN) and successfully applies it to the operating mechanism of a circuit breaker. In the proposed method,GAF is used to transform the collected time-domain signal into a two-dimensional representation,which helps track the evolution process of the dynamic characteristics of the operating mechanism. The state identification experiment of the circuit breaker operating mechanism verifies the effectiveness of the proposed intelligent diagnosis method,achieving a recognition success rate close to 100.00%. This method offers a promising approach for the robust identification of the in-service state of circuit breakers.

施贻铸;满天雪;周余庆;任燕;沈志煌;孙维方

温州大学机电工程学院,浙江温州 325035温州大学机电工程学院,浙江温州 325035||嘉兴南湖学院机电工程学院,浙江嘉兴 314001集美大学海洋装备与机械工程学院,福建厦门 361021

动力与电气工程

断路器卷积神经网络弹簧储能状态格拉姆角场

circuit breakerconvolutional neural network (CNN)spring energy storage stateGramian angular field (GAF)

《重庆大学学报》 2024 (009)

30-38 / 9

浙江省自然科学基金资助项目(LQ21E050003).Supported by the Natural Science Foundation of Zhejiang Province(LQ21E050003).

10.11835/j.issn.1000-582X.2023.224

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