NASA Milling Dataset (BEST Lab / PCoE) 核心 · 已核验
mxdsv039x1q0s464
NASA PCoE(BEST Lab)铣削刀具磨损数据:16 组走刀实验,后刀面磨损 VB 定期测量;声发射/振动/主轴电流三类信号
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- 落地页
- https://www.nasa.gov/intelligent-systems-division/discovery-and-systems-health/pcoe/pcoe-data-set-repository/
- 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。 - 设备类型
machine_tool- PHM 任务
degradation_trend_predictionrul_prediction- 跑至失效
- 是
故障工况
| description: 铣刀后刀面磨损(VB 值标注)fault_type: wear |
传感器
| sensor_type: current_sensorobserved_property: electric_currentmounting_note: 主轴 AC/DC 电流 |
| sensor_type: acoustic_emission_sensorobserved_property: acoustic_emission |
| sensor_type: accelerometerobserved_property: vibration_acceleration |
运行工况
| description: 16 组切削条件:铸铁/钢两种工件,变切深(0.75/1.5 mm)与进给(0.25/0.5 mm/rev)condition_type: other |
关联论文(37 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。
- Data-Driven Remaining Useful Life Estimation for Milling Process: Sensors, Algorithms, Datasets, and Future Directions 2021 · 综述收录
- A Reproducible Benchmark for Tool Wear Prediction Using NASA Milling Dataset 2026 · 用于方法验证
- Unlocking Tool Wear Information from Spindle Current Signals: An explorative Energy-Based Analysis using the NASA Milling Dataset 2026 · 使用该数据集
- Uncertainty-Aware Predictive Maintenance Scheduling: A Decision-Support Framework for Industrial Production Systems 2026 · 用于方法验证
- Smart Predictive Maintenance: A TCN-Based System for Early Fault Detection in Industrial Machinery 2026 · 用于方法验证
- ISA-PHM – a Standardized Format for Storing and Utilizing Meta-data of Diagnostic and Prognostic Tests 2026 · 使用该数据集
- TRANSFORMER-BASED TOOL WEAR CLASSIFICATION FOR PREDICTIVE MAINTENANCE OF METAL CUTTING MACHINES 2026 · 用于方法验证
- Condition-adaptive 1DCNN-transformer for multi-condition tool wear prediction 2026 · 候选引用(未核验)
- LightTEN: An efficient and lightweight temporal encoding network for remaining useful life prediction 2026 · 候选引用(未核验)
- Cross-condition tool wear prediction via feature decoupling and domain-adaptive representation learning 2026 · 候选引用(未核验)
- Cross-condition tool wear prediction via feature decoupling and domain-adaptive representation learning 2026 · 候选引用(未核验)
- Multi-source scale-preserving domain adaptation for cross-machine tool wear estimation from unlabeled target signals 2026 · 候选引用(未核验)
- Towards Intelligent Manufacturing: Machine Learning, Deep Learning, and Computer Vision for Tool Wear Estimation in Milling and Micromilling Processes 2026 · 候选引用(未核验)
- Performance Analysis of Advanced Feature Extraction Methods for Manufacturing Defect Detection via Vibration Sensors in CNC Milling Machines 2026 · 候选引用(未核验)
- Predictive machine health monitoring using deep convolution neural network for noisy vibration signal of rotating machine using empirical mode decomposition 2025 · 用于方法验证
- Real-time monitoring of coated tool wear based on the DSAMM-Transformer architecture: an experimental study 2025 · 候选引用(未核验)
- Tool Wear Identification and Monitoring of Hard Alloy End Mills Using an Improved WOA and ConvLSTM 2025 · 候选引用(未核验)
- Condition-Adaptive 1DCNN-Transformer for Multi-Condition Tool Wear Prediction 2025 · 候选引用(未核验)
- Sustainability Awareness in Manufacturing: A Review of IoT Audio Sensor Applications in the Industry 5.0 Era 2025 · 候选引用(未核验)
- Novel Taxonomy and Approaches for the Identification of Frequently Occurring Regularities in Degradation Processes of Engineering Systems 2025 · 候选引用(未核验)
仅列前 20 篇(首发/综述优先,按年份倒序);全量见 API。
溯源(10 条)
| 日期: 2026-07-08 |
| 日期: 2026-07-08 |
| 日期: 2026-07-08 |
| 日期: 2026-07-10 |
| 日期: 2026-07-11 |
| 日期: 2026-07-14 |
| 日期: 2026-07-25 |
| 日期: 2026-07-26 |
| 日期: 2026-07-26 |
| 日期: 2026-07-29 |