在炉设备中的一个长尾分布时间序列数据集
Weijun Hu1, Anze Jiang1, Kaihong Chen1
1School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, 510006, China.
Scientific data
|May 5, 2025
概括
这项研究预处理了有缺陷的工业炉数据,创建了一个高维时间序列数据集. 这使得能够更好地分析运行因素,以优化炉性能和寿命.
科学领域:
- 工业工程 工业工程 工业工程
- 数据科学数据科学数据科学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 工业炉对于发电,化学品和纸张等各个行业的能源生产至关重要.
- 炉运行数据的时间序列分析有助于优化性能和延长设备寿命.
- 工业环境中的传感器数据通常是不完整的或错误的,这是由于运营挑战.
研究的目的:
- 来自燃煤炉的有缺陷的操作数据进行预处理.
- 为工业设备分析生成一个高维时间序列数据集.
- 在工业设备数据中为解决长尾分布奠定基础.
主要方法:
- 从江的一个燃煤炉收集运行数据.
- 预处理的不完整和错误的传感器数据.
- 生成了一个高维时间序列数据集,包括压力,温度,流速和氧气水平.
主要成果:
- 一个预处理的,高维的时间序列数据集成功生成.
- 确定了关键的操作参数,并纳入数据集.
- 数据集显示出长尾分布,特别是在炉出口蒸汽温度.
结论:
- 生成的数据集为分析具有长尾数据分布的工业设备提供了基础.
- 解决数据质量问题对于有效的工业炉性能分析至关重要.
- 这项工作支持了工业炉预测性维护和运行优化方面的进展.
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