熔炼炉健康检测数据集

本数据集是一套用于设备状态监测与预测性维护的工业时序数据集，涵盖2023年10月至2024年3月期间5台熔炼炉的运行数据。数据集包含55条多维度检测记录，每条记录包含炉内温度、炉壁温度、冷却水温度、电压、电流、炉内压力、振动幅度、能耗及自动判定的健康状态（正常/警告/警报）共10个关键参数。
数据通过现场传感器系统采集，并经过严格的异常值处理、缺失值填充与逻辑一致性验证。该数据集具有明确的时间序列特征与设备差异性，可有效支持设备健康度评估、故障预警模型训练、能效优化分析及工艺数字孪生构建等多种工业智能应用，为铸造行业设备管理的数字化转型提供了高质量、可追溯的数据基础。

This is an industrial time-series dataset for equipment condition monitoring and predictive maintenance, encompassing operational data from 5 smelting furnaces spanning October 2023 to March 2024. The dataset contains 55 multi-dimensional detection records, with each record including 10 key parameters: in-furnace temperature, furnace wall temperature, cooling water temperature, voltage, current, in-furnace pressure, vibration amplitude, energy consumption, and automatically determined health status (normal/warning/alarm). The data was collected via on-site sensor systems, and underwent rigorous outlier handling, missing value imputation, and logical consistency validation. This dataset features distinct time-series characteristics and equipment heterogeneity, which can effectively support various industrial intelligent applications such as equipment health assessment, fault warning model training, energy efficiency optimization analysis, and process digital twin construction. It provides a high-quality, traceable data foundation for the digital transformation of equipment management in the foundry industry.

搜集汇总

背景与挑战

背景概述

该数据集是一个工业时序数据集，涵盖2023年10月至2024年3月期间5台熔炼炉的运行数据，包含55条记录，每条记录有炉内温度、炉壁温度、冷却水温度、电压、电流、炉内压力、振动幅度、能耗及健康状态共10个关键参数。数据经过异常值处理、缺失值填充和逻辑一致性验证，具有明确的时间序列特征和设备差异性，适用于设备健康度评估、故障预警模型训练、能效优化分析和工艺数字孪生构建等工业智能应用，为铸造行业设备管理的数字化转型提供高质量数据基础。