AI4I 2020 Predictive Maintenance Dataset (UCI) 核心 · 已核验
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AI4I 2020 预测性维护合成基准(UCI #601):10000 样本×14 特征,建模铣削类设备五种失效模式(TWF/HDF/PWF/OSF/RNF),带机器失效二元标签与模式标签;预测性维护教学与基准高频引用
本卡描述如含来自官方页的原文片段,其版权归原作者,不在本站 CC-BY 4.0 许可范围(见关于与许可)。
- 落地页
- https://archive.ics.uci.edu/dataset/601/ai4i+2020+predictive+maintenance+dataset
- 许可证
- CC-BY-4.0 (置信:verified_official)
- 国内可访问性
-
国内直连:可达 (2026-07-11 检测)
非直连:可达 (2026-07-11 检测)
「直连超时」表示检测窗口内未完成,系慢或不稳定证据,不构成封锁证据。 - 发布年份
- 2020
- 发布方
- UCI Machine Learning Repository
- 别名
- AI4I 2020
- 设备类型
machine_tool- PHM 任务
fault_detectionfault_diagnosis
故障工况
| description: 刀具磨损失效 TWFfault_type: wearinduction: simulated_synthetic |
| description: 散热失效 HDFfault_type: overheatinginduction: simulated_synthetic |
| description: 功率失效 PWF/过应变失效 OSF/随机失效 RNFfault_type: otherinduction: simulated_synthetic |
传感器
| sensor_type: otherobserved_property: othermounting_note: 合成测点:环境/过程温度、转速、扭矩、刀具磨损时长 |
运行工况
| description: 三档产品质量变体(L/M/H)对应不同转速-扭矩分布condition_type: loadis_varying: True |
关联论文(299 篇)
仅表示这篇论文与本数据集在文献网络中相邻, 不代表该论文确实使用了本数据集。
- Predictive maintenance dataset 2026 · 首发该数据集
- Predictive maintenance dataset 2026 · 首发该数据集
- A Review of Publicly Available Datasets from Manufacturing Systems 2024 · 综述收录
- Prognostix-XAI: Interpretable Modeling of Mechanical Degradation Dynamics 2026 · 用于方法验证
- Imbalance-Aware Predictive Maintenance Framework with Explainable Machine Learning: A case study using the AI4I 2020 Dataset 2026 · 用于方法验证
- Machine Learning-Based Predictive Maintenance for Manufacturing: Optimizing Failure Detection with Sensor-Based Models 2026 · 使用该数据集
- Distributed Agentic Micro-Agent for Resilience in the Computing Continuum 2026 · 使用该数据集
- AI-Driven Digital Twin Framework for Risk-Aware Predictive Maintenance in Electrical Control Systems 2026 · 用于方法验证
- XAI-PdMNet-Bench: an explainable generative AI framework for leakage-safe predictive maintenance in industry 5.0 manufacturing 2026 · 用于迁移/跨工况
- FusionNet: intelligent sequence fusion for predictive maintenance in edge-enabled industrial IoT systems 2026 · 用于方法验证
- AI-driven predictive maintenance for connected vehicles using environmental context integration evaluated through simulation benchmarking and field validation 2026 · 使用该数据集
- Non-response adjusted mean estimation using tool wear and torque data in predictive maintenance systems 2026 · 用于方法验证
- Physics-informed, cost-aware fault classification with comparative explainability for predictive maintenance: a SHAP–LIME agreement analysis 2026 · 用于方法验证
- Next-Generation Digital Twin Analytics in Smart Manufacturing using Cross-Layered Edge Cloud Deep Learning 2026 · 用于方法验证
- Application of Graph Neural Networks for Structural Dependency Analysis in Industrial Digital Twins Using the UCI AI4I 2020 Predictive Maintenance Dataset 2026 · 使用该数据集
- From Prediction to Performance: Evaluating How Machine Learning Accuracy Affects Industrial System Availability 2026 · 使用该数据集
- Edge Analytics for Real-Time Process Optimization in Smart Manufacturing: A Hybrid Machine Learning Approach 2026 · 用于方法验证
- Predictive Maintenance Through Conventional Intelligent Fault Diagnostics and Prognostic Models 2026 · 用于方法验证
- A dual-stream deep learning architecture for business impact scoring and alert escalation 2026 · 用于方法验证
- A Leakage-Aware and Reproducible Evaluation Framework for Predictive Maintenance Classification 2026 · 使用该数据集
仅列前 20 篇(首发/综述优先,按年份倒序);全量见 API。
溯源(9 条)
| 日期: 2026-07-08 |
| 来源链接: https://api.datacite.org/dois/10.24432/C5HS5C 日期: 2026-07-08 |
| 日期: 2026-07-08 |
| 日期: 2026-07-09 |
| 日期: 2026-07-11 |
| 日期: 2026-07-11 |
| 日期: 2026-07-21 |
| 日期: 2026-07-25 |
| 日期: 2026-07-31 |