在健康和故障条件下,来自固定速度和变速同步发电机的电信号数据集
Rafael Noboro Tominaga1, Luan Andrade Sousa1, Rodolfo Varraschim Rocha2
1University of São Paulo (USP), São Paulo, Brazil.
Data in brief
|December 16, 2024
概括
这项研究引入了来自发电机的电流数据的新数据集,使旋转电机的先进监测和诊断成为可能. 原始,现实世界的数据有助于开发数据驱动的算法,用于故障预测和提高运营效率.
科学领域:
- 电气工程 电气工程
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 有效监控旋转机器对于运营效率,安全和经济可行性至关重要.
- 数据驱动的算法被广泛用于机器的故障检测和行为预测.
- 获取可靠的数据来测试这些算法是一个重大挑战.
研究的目的:
- 从实验室发电机提供电流时间序列数据的公开,可靠的数据集.
- 为了使在健康和内部有缺陷的条件下研究机器行为.
- 促进用于旋转电机监控的数据驱动算法的开发和比较.
主要方法:
- 在各种条件下从发电机收集原始电流和电压时间序列数据.
- 引入控制的内部故障,包括不同类型的短路.
- 使用实验室设置与真正的发电机,捕获数据与自然的工业噪音.
主要成果:
- 一个全面的数据集,包括发电机在正常运行期间的电信号和特定的内部故障.
- 该数据集包括原始,预处理的时间序列数据,适合各种分析方法.
- 为研究难以通过分析或计算建模的现象提供了宝贵的资源.
结论:
- 发布的数据集加速了用于旋转电机的高效监测,诊断和预测技术的开发.
- 它作为测试和验证新的数据驱动方法的基准.
- 这些数据支持电气机器和数据驱动方法的工程教育.
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