汽车零配件制造设备故障预警数据集

数据集涵盖振动三轴、温度、噪声、电流等关键物理参数，并包含异常检测、趋势预警、故障模式识别及预警等级等多维标签。数据共42条监测记录，其中异常记录29条（占比69%），预警等级涵盖1级（轻微）、2级（中度）、3级（严重）。识别出的故障模式包括电机不平衡、皮带松动、电气故障、轴承磨损、冷却系统异常等8类典型工业故障。该数据集具备时序连续、多参数同步、标签完整的特点，适用于设备健康状态评估、预测性维护模型构建、故障根因分析等工业场景，为智能制造系统的可靠性提升与运维决策优化提供高质量数据基础。

This dataset contains key physical parameters including triaxial vibration, temperature, noise and current, as well as multi-dimensional labels covering anomaly detection, trend early warning, fault pattern recognition and warning level. The dataset consists of 42 monitoring records in total, with 29 anomaly records accounting for 69% of the total. The warning levels include Level 1 (minor), Level 2 (moderate) and Level 3 (severe). Eight typical industrial fault patterns have been identified, such as motor unbalance, loose belt, electrical fault, bearing wear, abnormal cooling system and other related faults. This dataset is characterized by sequential continuity, synchronized multi-parameter collection and complete labeling. It is suitable for industrial scenarios including equipment health status assessment, predictive maintenance model development, fault root cause analysis and more, thus providing a high-quality data basis for improving the reliability of intelligent manufacturing systems and optimizing operation and maintenance decisions.

搜集汇总

背景与挑战

背景概述

该数据集专注于汽车零配件制造设备的故障预警，包含42条监测记录，涵盖振动、温度、噪声和电流等多维物理参数，并标注了异常检测、预警等级及8类典型故障模式（如电机不平衡、轴承磨损）。其特点是时序连续、标签完整，适用于设备健康评估、预测性维护模型构建和故障根因分析，为智能制造系统的可靠性提升提供高质量数据支持。