通过多标签分类方法提高动力设备缺陷的识别.
Wenjie Zheng1, Yi Yang2, Fengda Zhang1
1State Grid Shandong Electric Power Research Institute, Jinan, 250002, Shandong, China.
Scientific reports
|September 18, 2024
概括
这项研究引入了一个新的数据集和方法,用于精确的电力设备缺陷分类,改进维护决策. 分析标签相关性和使用平衡损失函数显著提高了分类性能.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
背景情况:
- 精确的电力设备缺陷识别对于维护至关重要.
- 由于主观的手册记录和有限的范围,传统方法是无效的.
- 当前的方法往往过于简化了仅基于缺陷等级的状态评估.
研究的目的:
- 开发一套针对电力设备缺陷的多标签分类数据集.
- 评估和比较各种机器学习和深度学习方法用于缺陷分类.
- 为了提高动力设备缺陷识别和分类的精度.
主要方法:
- 将历史缺陷记录编译成一个新的多标签分类数据集.
- 评估了11种已建立的多标签分类方法 (传统的ML和深度学习).
- 采用平衡损失函数来解决样本不平衡,并将任务细分为标签召回和排名阶段.
主要成果:
- 考虑标签相关性的方法显示出显著的性能优势.
- 平衡损失函数有效地缓解了样本失衡问题.
- 将分类任务划分为回忆和排名阶段,提高了整体性能.
结论:
- 开发的数据集和评估的方法为电力设备缺陷分类提供了更精确的方法.
- 考虑标签相关性和解决样本不平衡是提高准确性的关键.
- 创建的数据集是公开可用的,以推进电力设备健康评估的研究.
相关概念视频
01:25Multimachine Stability
143
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
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01:26Classification of Systems-I
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
177