ShuffleNet-based comprehensive diagnosis for insulation and mechanical faults of power equipment

作     者：Yanxin Wang Jing Yan Qifeng Sun Yiming Zhao Tingliang Liu 

作者机构：State Key Laboratory of Electrical Insulation and Power EquipmentXi'an Jiaotong UniversityXi'anShaanxi ProvinceChina School of Foreign StudiesXi'an Jiaotong UniversityXi'anShaanxi ProvinceChina 

出 版 物：《High Voltage》 (高电压（英文）)

年 卷 期：2021年第6卷第5期

页      面：861-872页

核心收录：

学科分类：0808[工学-电气工程] 080803[工学-高电压与绝缘技术] 08[工学] 0807[工学-动力工程及工程热物理] 

主　　题：fault diagnosis insulation 

摘      要：Despite the complicated fault mechanism of power equipment,with the increasing promotion and development of the Ubiquitous Power Internet of Things(UPIoT),the fault information of power equipment can be instantly saved,which makes possible intelligent diagnosis via fault *** study proposes a new method to implement comprehensive intelligent diagnosis by adopting the ShuffleNet light-weight convolution neural network(SLCNN).Considering the requirements of the UPIoT intelligent terminal,this study constructs six models,which are measured in terms of recognition accuracy,model storage,and calculation cost when applied to insulation and mechanical *** with the existing models,the SLCNN outperforms them significantly in terms of recognition accuracy,with an accuracy of 95.77%and 99.9%in insulation and mechanical fault diagnoses,*** SLCNN also demonstrates obvious advantages in other performance indicators,all of which contribute to its use in the accurate and reliable fault diagnosis of power equipment in the UPIoT ***,through comparing feature maps,it is discovered that the aliasing degree and boundary of insulation defects are not as obvious as those of mechanical faults,which means that insulation fault diagnosis is much more difficult than mechanical fault diagnosis.