IMS / University of Cincinnati Bearing Run-to-Failure Dataset 核心 · 已核验
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IMS 中心(辛辛那提大学)轴承 run-to-failure 试验数据集:4 个轴承同轴运行至自然失效, 经典 prognostics 数据集;官方源多次迁移,镜像众多,溯源需谨慎。
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://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。 - 发布年份
- 2007
- 发布方
- IMS Center, University of Cincinnati / NASA PCoE
- 别名
- IMS / NASA PCoE Bearing
- 设备类型
rolling_bearing- PHM 任务
rul_predictionanomaly_detectiondegradation_trend_prediction- 跑至失效
- 是
分发点
| nasa_pcoe | https://www.nasa.gov/intelligent-systems-division/discovery-and-systems-health/pcoe/pcoe-data-set-repository/ | 原 ti.arc.nasa.gov 链接已死,官方页历经迁移 |
| phm_society | https://data.phmsociety.org/nasa/ | PHM Society 镜像,当前最可靠公开源 |
故障工况
| description: 试验 1 轴承 3 内圈失效fault_type: bearing_inner_race_faultinduction: accelerated_life_test |
| description: 试验 1 轴承 4 滚动体失效fault_type: bearing_rolling_element_faultinduction: accelerated_life_test |
| description: 试验 2/3 外圈失效fault_type: bearing_outer_race_faultinduction: accelerated_life_test |
传感器
| sensor_type: accelerometerobserved_property: vibration_accelerationsampling_rate_hz: 20480.0mounting_note: 每轴承 1~2 通道(试验批次不同) |
运行工况
| condition_type: rotating_speedmin_value: 2000.0max_value: 2000.0unit: rpmis_varying: False |
| description: 弹簧机构施加的径向载荷condition_type: loadmin_value: 6000.0max_value: 6000.0unit: lbf |
关联论文(1439 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。
- Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics 2006 · 首发该数据集
- Recent deep learning models for diagnosis and health monitoring: A review of research works and future challenges 2023 · 综述收录
- Deep Learning Aided Data-Driven Fault Diagnosis of Rotatory Machine: A Comprehensive Review 2021 · 综述收录
- A comprehensive review on convolutional neural network in machine fault diagnosis 2020 · 综述收录
- UNFIT monitoring of roller bearing degradation: A new event-based concept for early defect detection 2026 · 用于方法验证
- Comparison of independent component analysis and principal component analysis for remaining useful life prediction of rolling element bearings 2026 · 用于方法验证
- Condition-Based Bearing Inventory Management Using NASA/IMS Run-to-Failure Data 2026 · 用于方法验证
- Multi-Domain Representation Learning for Bearing Fault Diagnosis with Phase and Transient Preservation 2026 · 用于方法验证
- An Unsupervised Data-Driven Framework for Bearing Failure Prognosis via Health Stage Clustering and Artificial Neural Network-Based Remaining Useful Life Estimation 2026 · 用于方法验证
- Color Recurrence Plots from Uniform Delay Embeddings for Bearing Degradation Tracking and Prognostics 2026 · 用于方法验证
- Hybrid HHO–WHO Optimized Transformer-GRU Model for Advanced Failure Prediction in Industrial Machinery and Engines 2026 · 用于方法验证
- A Resource-Aware Method for Evaluating AI Models in Data Streaming Systems for Predictive Maintenance 2026 · 用于方法验证
- Unsupervised anomaly detection method for rotating machinery based on discrete latent space modeling 2026 · 候选引用(未核验)
- A Review of Various Fault Diagnosis and RUL Estimation Techniques for Predictive Maintenance in Industrial Rotating Machinery 2026 · 候选引用(未核验)
- BearGen: LLM-guided signal generation framework for bearing fault diagnosis 2026 · 候选引用(未核验)
- A dual-stage LSTM framework for accurate prediction of bearing remaining useful life under shaft misalignment conditions 2026 · 候选引用(未核验)
- Early fault detection of rolling bearings based on deep memory-guided attention residual shrinkage network 2026 · 候选引用(未核验)
- Sharpness aware flattening for domain generalization with pre-trained vision transformer in machinery fault diagnosis 2026 · 候选引用(未核验)
- Clustering low-rank tensor train dynamic mode decomposition: An enhanced multivariate signal processing algorithm and its applications 2026 · 候选引用(未核验)
- A novel remaining useful life prediction method of rolling bearings based on multivariate prediction method and long short-term memory with residuals model 2026 · 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)
| 来源链接: https://data.phmsociety.org/nasa/ 日期: 2026-07-07 |
| 日期: 2026-07-07 |
| 日期: 2026-07-08 |
| 日期: 2026-07-09 |
| 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 |