FEMTO-ST / PRONOSTIA / IEEE PHM 2012 Bearing Data Challenge 核心 · 已核验
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FEMTO-ST 研究所 PRONOSTIA 平台的轴承加速退化试验数据集,曾作 IEEE PHM 2012 数据挑战赛;挑战赛语境提供了明确的 RUL benchmark 任务设置。
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://hal.science/hal-00719503v1
- 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
- 2012
- 发布方
- FEMTO-ST Institute
- 别名
- PRONOSTIA / PHM2012 / FEMTO
- 设备类型
rolling_bearing- PHM 任务
rul_predictiondegradation_trend_prediction- 跑至失效
- 是
分发点
| institutional_site | https://hal.science/hal-00719503v1 | 平台论文;数据历史上经 challenge/GitHub 多渠道分发 |
故障工况
| description: 全寿命自然退化;失效模式(内圈/外圈/滚动体/保持架)未逐一标注,以整体退化为对象fault_type: otherinduction: accelerated_life_test |
传感器
| sensor_type: accelerometerobserved_property: vibration_accelerationsampling_rate_hz: 25600.0channel_count: 2mounting_note: 水平/垂直两方向,间歇采样(每 10 s 采 0.1 s) |
| sensor_type: rtdobserved_property: temperaturesampling_rate_hz: 10.0 |
运行工况
| description: 三种工况(1800/1650/1500 rpm)condition_type: rotating_speedmin_value: 1500.0max_value: 1800.0unit: rpmis_varying: False |
| description: 与转速成组(4000/4200/5000 N)condition_type: loadmin_value: 4000.0max_value: 5000.0unit: N |
关联论文(691 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。
- PRONOSTIA: An experimental platform for bearings accelerated degradation tests 2012 · 首发该数据集
- Remaining Useful Life Estimation of Bearings 2021 · 综述收录
- Remaining Useful Life Prediction of Rolling Bearings Based on an Improved U-Net and a Multi-Dimensional Hybrid Gated Attention Mechanism 2025 · 使用该数据集
- A novel few-shot deep regression domain adaptation method with wavelet scattering attention mechanism for fault prognostics 2025 · 用于迁移/跨工况
- Uncertainty Quantification in Fault and Degradation Analysis of Rolling Element Bearings 2025 · 使用该数据集
- Enhanced reliability prediction of rolling bearings through a hybrid gamma process and BOA-TCN-BiLSTM-attention mechanism 2025 · 用于方法验证
- Remaining useful life prediction of motor bearings based on slow feature analysis-assisted attention mechanism and dual-LSTM networks 2025 · 用于方法验证
- LSGAN-Transformer life prediction method for rolling bearings under few samples 2025 · 用于方法验证
- An Adaptive BiGRU-ASSA-iTransformer Method for Remaining Useful Life Prediction of Bearing in Aerospace Manufacturing 2025 · 用于方法验证
- A Remaining Useful Life Prediction Method for Rolling Bearings Based on Hierarchical Clustering and Transformer–GRU 2025 · 用于方法验证
- Remaining Useful Life Prediction of Rolling Bearings Based on Deep Time–Frequency Synergistic Memory Neural Network 2025 · 用于方法验证
- Stochastic Identification and Analysis of Long-Term Degradation Through Health Index Data 2025 · 用于方法验证
- Remaining Useful Life Prediction for Rolling Bearings Based on TCN–Transformer Networks Using Vibration Signals 2025 · 用于方法验证
- Remaining Useful Life Prediction of Bearings via Semi-Supervised Transfer Learning Based on an Anti-Self-Healing Health Indicator 2025 · 用于迁移/跨工况
- Remaining Useful Life Prediction Across Conditions Based on a Health Indicator-Weighted Subdomain Alignment Network 2025 · 用于迁移/跨工况
- WaveAtten: A Symmetry-Aware Sparse-Attention Framework for Non-Stationary Vibration Signal Processing 2025 · 用于方法验证
- Remaining Useful Life Prediction for Exciter Rolling Bearing Based on Self‐Attentive <scp>CNN–GRU</scp> 2025 · 候选引用(未核验)
- A Divisive Unsupervised Feature Selection Approach for Explainable Remaining Useful Life Prediction 2025 · 候选引用(未核验)
- Remaining Life Prediction of Motor Bearing Based on Fusion Degradation Indicator 2025 · 候选引用(未核验)
- Bearing Remaining Useful Life Prediction Based on Multi-scale Feature Extraction and Convolutional Attention Mechanism 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(9 条)
| 来源链接: https://hal.science/hal-00719503v1 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 |