Experimental Vibration Dataset for Rolling Bearing Outer Race Fault Diagnosis under Variable Load Conditions 核心 · 已核验
mxdswvf2yedp3b09
This dataset provides vibration signals from rolling element bearings, specifically the UC204 insert ball bearing (GBR), collected under healthy and faulty conditions for research in fault diagnosis and condition monitoring. Experimental Setup: Hardware: Measurements were captured using an ADXL210 accelerometer mounted near the bearing housing. Data Acquisition: Signals were processed via a National Instruments USB-6212 (16-bit resolution) interfaced with LabVIEW. Operational Parameters: The shaft speed was constant at ~1500 rpm (25 Hz). Three load levels were tested: 0.25 hp, 0.4 hp, and 0.6 hp. Fault Conditions: Artificial outer race defects were introduced as linear grooves with four severity levels (lengths): 0.18 mm, 0.36 mm, 0.54 mm, and 0.72 mm. Data Structure: Sampling Rate: 3200 Hz. Signal Length: 32,000 samples (~10 seconds) per file. Quantity: 10 signals per operating condition. Format: Plain text (.txt) time-series files, organized into folders by condition and load. This dataset is suitable for vibration analysis, signal processing, and machine learning applications in predictive maintenance.
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://dx.doi.org/10.17632/szfkx3mzcm.1
- 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。 - 设备类型
rolling_bearing- PHM 任务
fault_diagnosiscondition_monitoring
故障工况
| fault_type: healthy_baseline |
| fault_type: bearing_outer_race_fault |
传感器
| sensor_type: accelerometer |
运行工况
| condition_type: load |
关联论文(108 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。
- Overview and Analysis of Publicly Available Degradation Data Sets for Tasks within Prognostics and Health Management 2025 · 综述收录
- Transfer learning-based rolling bearing fault diagnosis method with feature fusion 2026 · 用于迁移/跨工况
- Online Distribution-Based Clustering Algorithm for Condition Monitoring in Rotating Machines 2026 · 使用该数据集
- DFPSE: Diagnostic-Feature-Preserving Signal Enhancement for Robust Bearing Fault Diagnosis under Compound Noise 2026 · 用于方法验证
- Physics-Aware Lightweight Artificial Intelligence (AI) for Bearing Fault Diagnosis in Industry 4.0 Predictive Maintenance 2026 · 候选引用(未核验)
- A novel industrial equipment fault diagnosis method combining unseen class recognition and few-shot augmentation 2026 · 候选引用(未核验)
- Multimodal fusion of large-kernel convolution and BiGRU for bearing fault diagnosis 2026 · 候选引用(未核验)
- CEAE-CGIVT: cross-granularity interactive vision transformer with columnar expansion denoising module for aero-engine bearing fault diagnosis 2026 · 候选引用(未核验)
- A Novel Approach for Motor Bearing Fault Detection Using EMD-Based Denoising and Detrended Fluctuation Analysis & LSTM Multimodal Hybrid Features with K-Means Clustering 2026 · 候选引用(未核验)
- An efficient lightweight multi-scale CNN framework with CBAM and SPP for bearing fault diagnosis 2026 · 候选引用(未核验)
- Neuronal attention circuit (NAC) for representation learning 2026 · 候选引用(未核验)
- For mechanical fault diagnosis: A bi-level optimization feature selection model based on Pareto front and cross-entropy loss 2026 · 候选引用(未核验)
- The bearing shell surface indentation and early-state wear detection combining active ultrasound and one-dimensional convolutional neural network 2026 · 候选引用(未核验)
- Towards a more realistic evaluation of machine learning models for bearing fault diagnosis 2026 · 候选引用(未核验)
- Industrial robot transmission components cross-machines fault diagnosis via fault intrinsic representation and channel self-healing under sensor failure 2026 · 候选引用(未核验)
- Machine learning approaches for improving load-dependent bearing fault diagnosis of nuclear facility components 2026 · 候选引用(未核验)
- A bearing fault diagnosis method based on contrastive disentanglement for single-source domain generalization 2026 · 候选引用(未核验)
- Fault diagnosis of rolling bearings based on mechanism-guided deep subdomain adaptation method 2026 · 候选引用(未核验)
- Fault diagnosis of automotive transmission bearings based on a TCN model optimized by an improved CCO algorithm 2026 · 候选引用(未核验)
- Research on Wavelet Domain Signal Denoising Algorithm Based on Adaptive Continuously Differentiable Threshold Functions 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(17 条)
| 来源链接: https://dx.doi.org/10.17632/szfkx3mzcm.1 日期: 2026-07-09 |
| 日期: 2026-07-10 |
| 日期: 2026-07-10 |
| 日期: 2026-07-10 |
| 日期: 2026-07-10 |
| 日期: 2026-07-10 |
| 日期: 2026-07-10 |
| 日期: 2026-07-10 |
| 日期: 2026-07-11 |
| 日期: 2026-07-14 |
| 日期: 2026-07-25 |
| 引文: 「1. Experimental Setup: Install the UC204 bearing into the housing unit of the laboratory test rig. Mount the ADXL210 accelerometer near the bearing housing to capture radial vibrations. Connect the sensor to the NI USB-6212 DAQ board and initialize the LabVIEW interface. 2. System Startup and Calibration: Power on the system via the electrical panel. Set the shaft rotational speed to 1500 rpm (25 Hz) using the frequency inverter. Verify speed stability using the encoder readings. 3. Data Acquisition Protocol: Set the resistive load bank to the first level (0.25 hp). Perform signal acquisition: record 10 signals at a sampling frequency of 3200 Hz (32,000 samples per signal). Repeat the acquisition for the remaining load levels (0.4 hp and 0.6 hp). 4. Fault Induction and Comparison: Replace the healthy bearing with those containing artificial outer race defects (grooves of 0.18 mm, 0.36 mm, 0.54 mm, and 0.72 mm). For each damaged bearing, repeat the full acquisition cycle (Steps 2 and 3) under all load conditions to ensure data consistency. 5. Data Storage: Export the raw data from LabVIEW as plain text (.txt) files for post-processing and analysis.」 来源链接: https://data.mendeley.com/public-api/datasets/szfkx3mzcm 日期: 2026-09-02 |
| 来源链接: https://data.mendeley.com/public-api/datasets/szfkx3mzcm 日期: 2026-09-02 |
| 日期: 2026-09-02 |
| 日期: 2026-09-02 |
| 来源链接: https://data.mendeley.com/public-api/datasets/szfkx3mzcm 日期: 2026-09-02 |
| 日期: 2026-09-02 |