重庆大学学报2024，Vol.47Issue(9)：30-38,9.DOI:10.11835/j.issn.1000-582X.2023.224
结合GAF与CNN的操动机构弹簧储能状态智能辨识
Intelligent identification method of spring energy storage state of circuit breaker operating mechanism based on GAF and CNN
摘要
Abstract
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.关键词
断路器/卷积神经网络/弹簧储能状态/格拉姆角场Key words
circuit breaker/convolutional neural network (CNN)/spring energy storage state/Gramian angular field (GAF)分类
信息技术与安全科学引用本文复制引用
施贻铸,满天雪,周余庆,任燕,沈志煌,孙维方..结合GAF与CNN的操动机构弹簧储能状态智能辨识[J].重庆大学学报,2024,47(9):30-38,9.基金项目
浙江省自然科学基金资助项目(LQ21E050003).Supported by the Natural Science Foundation of Zhejiang Province(LQ21E050003). （LQ21E050003）
