Paderborn University Bearing DataCenter 核心 · 已核验
atlas:paderborn-bearing
机电驱动系统中滚动轴承损伤数据集:同时含人工损伤与加速寿命试验产生的真实损伤, 同步采集振动与两相电机电流,实验条件文档化程度高。
- 落地页
- https://mb.uni-paderborn.de/kat/forschung/bearing-datacenter
- 许可证
- CC-BY-NC-4.0 (判读置信:verified_official)
- 国内可访问性
-
国内直连:可达 (2026-07-11 检测)
代理通道:可达 (2026-07-11 检测)
检测口径:lychee 双通道单轮探测;「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
- 2016
- 发布方
- Paderborn University, KAt Research Group
- 别名
- PU / Paderborn KAt
- 设备类型
rolling_bearing- PHM 任务
fault_diagnosisdomain_adaptation
分发点
| university_site | https://mb.uni-paderborn.de/kat/forschung/bearing-datacenter | |
| university_site | https://mb.uni-paderborn.de/kat/forschung/bearing-datacenter/data-sets-and-download | 官方数据下载页;并卡自候选副本卡(atlas:quarry-url-https-mb-uni-paderborn-...,2026-07-10 用户裁决) |
| university_site | https://groups.uni-paderborn.de/kat/BearingDataCenter/ | 官方数据直链目录;同上并卡 |
故障工况
| fault_type: healthy_baseline |
| description: 人工损伤(EDM/钻孔/电刻)fault_type: bearing_inner_race_faultinduction: artificially_seeded |
| fault_type: bearing_outer_race_faultinduction: artificially_seeded |
| description: 加速寿命试验产生的真实损伤(疲劳点蚀为主)fault_type: bearing_inner_race_faultinduction: accelerated_life_test |
| fault_type: bearing_outer_race_faultinduction: accelerated_life_test |
传感器
| sensor_type: accelerometerobserved_property: vibration_accelerationsampling_rate_hz: 64000.0 |
| sensor_type: current_sensorobserved_property: electric_currentsampling_rate_hz: 64000.0channel_count: 2mounting_note: 两相电机电流 |
| sensor_type: thermocoupleobserved_property: temperaturemounting_note: 低采样率环境/轴承温度 |
运行工况
| description: 四种标准工况组合之一维度condition_type: rotating_speedmin_value: 900.0max_value: 1500.0unit: rpmis_varying: False |
| description: 负载转矩两档condition_type: loadmin_value: 0.1max_value: 0.7unit: N·m |
| description: 径向力两档condition_type: loadmin_value: 400.0max_value: 1000.0unit: N |
关联论文(626 篇,候选区未经人工核验;candidate_citation = 共引启发式候选关联,非使用断言)
- Condition Monitoring of Bearing Damage in Electromechanical Drive Systems by Using Motor Current Signals of Electric Motors: A Benchmark Data Set for Data-Driven Classification 2016 · introduces
- TFD-Trans: Time-frequency hierarchical decomposition transformer for mechanical fault diagnosis 2026 · 候选关联(启发式)
- Semi-supervised cross-domain fault diagnosis via contrastive pre-training and annotation-efficient alignment strategy 2026 · 候选关联(启发式)
- A bearing fault diagnosis method based on Hopfield network stochastic resonance and a multi-feature fusion health index 2026 · 候选关联(启发式)
- Prototype‐attention domain adaptation for explainable bearing fault diagnosis 2026 · 候选关联(启发式)
- Multimodal bearing fault diagnosis based on semantic-aware and enhanced cross-attention 2026 · 候选关联(启发式)
- A Novel Multidynamic Domain Adaptation Transfer Learning Method for Fault Diagnosis of Bearings With Insufficient Labeled Data 2026 · 候选关联(启发式)
- Enhanced dual-channel feature fusion approach for rolling bearing fault diagnosis 2025 · uses
- Multiscale attention large kernel convolutional neural network for rolling bearing fault diagnosis in noise environment 2025 · uses
- Bearing Fault Diagnosis Method Based on Multisensor Hybrid Feature Fusion 2025 · uses
- Optimal feature complexity for small-sample bearing fault detection in manufacturing 2025 · 候选关联(启发式)
- Large scale foundation models for intelligent manufacturing applications: a survey 2025 · 候选关联(启发式)
- An unsupervised domain adaptation method for intelligent fault diagnosis based on target feature enhancement and feature-boundary alignment 2025 · 候选关联(启发式)
- Deep meta-learning-based multi-signal data fusion approach for fault diagnosis 2025 · 候选关联(启发式)
- Statistically-aligned feature augmentation for robust unsupervised domain adaptation in industrial fault diagnosis 2025 · 候选关联(启发式)
- WOA-based Parameter Adaptive VMD Combined with Wavelet Thresholding for Bearing Fault Feature Extraction 2025 · 候选关联(启发式)
- DiagLLM: multimodal reasoning with large language model for explainable bearing fault diagnosis 2025 · 候选关联(启发式)
- A robust deep learning system for motor bearing fault detection: leveraging multiple learning strategies and a novel double loss function 2025 · 候选关联(启发式)
- Extraction of mechanical multi-fault using parameter-adaptive jump plus mode decomposition 2025 · 候选关联(启发式)
- Intelligent Fault Diagnosis of Rolling Bearings in Strong Noise Environment: An Attention-Driven Hybrid Model Based on IENEMD and Parallel Multiscale CNN 2025 · 候选关联(启发式)
仅列前 20 篇(首发/综述优先,按年份倒序);全量见 API。
溯源(PROV,7 条)
| source_url: https://mb.uni-paderborn.de/kat/forschung/bearing-datacenterretrieved_on: 2026-07-07asserted_by: human_curator |
| about_field: sensorssource_citation: Lessmeier et al. (2016), PHM Society European Conferenceretrieved_on: 2026-07-07asserted_by: human_curator |
| about_field: license_id,publication_yearsource_citation: facet-batch-06.yamlretrieved_on: 2026-07-08asserted_by: automated_extractionnote: 代理归纳刻面(依据:官方页许可声明(非商业学术+引用要求,商用联系作者;2026-07-08 核读);基准论文 PHME 2016);候选区,晋升需人工核验 |
| about_field: source_citation: 人工核验:zfbin(委托批准 2026-07-09)retrieved_on: 2026-07-09asserted_by: human_curatorconfidence_level: human_verifiednote: 晋升核心区。首晋升批次 01:KLS-005 手工填卡,实质核验于 KLS-005 完成(评审见 evidence/KLS-005/);委托代理执行 |
| about_field: distributionssource_citation: 人工核验:zfbin(四问拍板 2026-07-10)retrieved_on: 2026-07-10asserted_by: human_curatorconfidence_level: human_verifiednote: 人工改写。并卡增量:副本/镜像卡的 alt 分发点并入(2026-07-10 用户裁决,详见各条目 access_note) |
| about_field: china_accessibilitysource_citation: KLS-009 链接健康扫描(lychee 双通道)retrieved_on: 2026-07-11asserted_by: automated_harvestnote: 定期刷新标注,仅覆盖本字段;历史结果以最新扫描为准 |
| about_field: license_idsource_citation: 人工核验:zfbin(Gate 0 四问拍板 2026-07-14)retrieved_on: 2026-07-14asserted_by: human_curatorconfidence_level: human_verifiednote: 人工改写。KLS-023 SPDX 记法归一:cc-by-nc-4.0 → CC-BY-NC-4.0(官方大小写,语义不变;批次 09 同款先例) |