Create README.md

README.md

@@ -0,0 +1,158 @@
+---
+pretty_name: Multi-mode Fault Diagnosis Datasets of Three-phase Asynchronous Motor Under Variable Working Conditions
+language:
+- en
+license: mit
+task_categories:
+- time-series
+- classification
+- anomaly-detection
+tags:
+- fault-diagnosis
+- condition-monitoring
+- rotating-machinery
+- induction-motor
+- vibration
+- motor-current
+- variable-operating-conditions
+- compound-faults
+- time-series
+- domain-adaptation
+- test-time-adaptation
+size_categories:
+- 100M<n<1B
+---
+
+# Multi-mode Fault Diagnosis Datasets of Three-phase Asynchronous Motor Under Variable Working Conditions
+
+<img width="666" alt="截屏2026-01-06 12 36 22" src="https://github.com/user-attachments/assets/8901f731-e80a-411f-9734-550b543e5b60" />
+
+## Dataset Summary
+This dataset provides synchronized multi-modal time-series data collected from a **2.2 kW three-phase asynchronous (induction) motor** operating under **variable speed/load conditions** with **deliberately induced faults**. It is intended for developing and benchmarking robust fault diagnosis methods under realistic operating scenarios, especially for **time-varying (transitional) working conditions**.
+
+Key characteristics:
+- Covers **electrical faults**, **mechanical faults**, and **electromechanical compound faults**
+- Includes **two severity levels** for representative fault categories
+- Provides **8-channel synchronous measurements** combining vibration and current signals for multi-modal diagnosis
+
+## Data Availability
+- **IEEE DataPort:** https://ieee-dataport.org/documents/multi-mode-fault-diagnosis-datasets-three-phase-asynchronous-motor-under-variable-working
+- **Hugging Face:** https://huggingface.co/datasets/Samlzy/MCC5-THU-Motor
+
+## Supported Tasks and Leaderboard-Style Use Cases
+This dataset can support (but is not limited to) the following research tasks:
+- **Multi-modal Fault Diagnosis (Vibration + Current Fusion)**
+- **Compound Fault Diagnosis (Electromechanical Coupling)**
+- **Fault Diagnosis with Different Fault Severity Degrees**
+- **Fault Diagnosis with Multiple Steady Working Conditions**
+- **Fault Diagnosis with Unknown / Unseen Working Conditions**
+- **Fault Diagnosis under Variable Working Conditions**
+- **Fault Diagnosis under Transitional Working Conditions (Time-varying Speed/Load Profiles)**
+
+Typical problem formulations:
+- **Multiclass classification** over fault types (and optionally severity)
+- **Open-set / unknown condition generalization** (unseen speed/load profiles)
+- **Online / test-time adaptation (OTTA)** under transitional profiles
+- **Multi-modal fusion learning** using vibration + current
+
+## Languages
+- English (metadata, documentation)
+
+## Dataset Structure
+
+### Data Format
+- **File format:** CSV
+- **Total recordings:** 282 runs
+- **Duration per run:** 90 seconds
+- **Sampling frequency:** 12.8 kHz
+- **Timestamp column:** not included (samples are uniformly sampled)
+
+Each CSV file contains **8 columns** (synchronized signals). The key-phase signal is dimensionless and can be used to derive rotational speed.
+
+### Data Fields (8 Columns)
+Each CSV file contains the following 8 columns:
+
+1. **speed**: motor key-phase signal (dimensionless; rotational speed can be derived)
+2. **torque**: torque on the gearbox input shaft (Nm)
+3. **motor_vibration_X**: vibration acceleration at motor drive end (horizontal radial direction, 0.1g)
+4. **motor_vibration_Y**: vibration acceleration at motor drive end (axial direction, 0.1g)
+5. **motor_vibration_Z**: vibration acceleration at motor drive end (vertical radial direction, 0.1g)
+6. **motor_current_A**: phase-A current (0.1A)
+7. **motor_current_B**: phase-B current (0.1A)
+8. **motor_current_C**: phase-C current (0.1A)
+
+### Variable Working Conditions
+Two operating scenarios are included:
+
+1) **Constant-speed, variable-torque:** motor speed is held constant (e.g., 1000 / 2000 / 3000 rpm), while torque changes over time.  
+2) **Constant-torque, variable-speed:** load torque is held constant (e.g., 20 Nm / 40 Nm), while speed changes over time.
+
+**Note:** Due to magnetic hysteresis effects in the motor and torque generator, the measured speed–torque trajectories may show slight deviations from the preset profiles.
+
+### Fault Types (24 types; electrical, mechanical, and compound)
+The dataset covers a wide range of motor faults, including (representative list):
+
+#### Electrical faults
+- **Stator winding inter-turn short circuit** (two severity levels, e.g., ~5% vs. ~10% of rated phase current equivalence)
+- **Voltage unbalance** (two severity levels, e.g., ~4% vs. ~8%)
+- **Broken rotor bars** (e.g., removal of consecutive rotor bars with rebalancing)
+
+#### Mechanical faults
+- **Rotor unbalance** (added imbalance mass)
+- **Bent shaft** (permanent shaft bend)
+- **Eccentricity** (including static eccentricity with two radial-offset severities, e.g., 0.125 mm and 0.250 mm)
+- **Bearing faults (SKF 6205 deep-groove ball bearing)**
+  - inner raceway defect (light / high)
+  - outer raceway defect (light / high)
+  - rolling element damage (ball defect)
+
+#### Electromechanical compound faults (examples)
+To study coupled signatures and cross-modulation, compound-fault scenarios are included, such as:
+- bearing defect + static eccentricity
+- bearing defect + rotor unbalance
+- bearing defect + broken rotor bars
+- bearing defect + winding short circuit
+
+### File Naming Convention (Example)
+Filenames encode fault type, severity, operating mode, and key condition settings. For example:
+- `Bearing_inner_L_speed_circulation_20Nm_1000rpm`  
+  indicates an inner-race bearing defect (light severity) under a variable-speed profile, with 20 Nm torque and the corresponding speed-time profile tag.
+- `Bearing_inner_H_torque_circulation_20Nm_1000rpm`  
+  indicates an inner-race bearing defect (high severity) under a variable-torque profile, with 1000 rpm and the corresponding torque-time profile tag.
+
+(Exact naming patterns may vary slightly across fault categories; see the dataset directory structure for full coverage.)
+
+## Experimental Setup
+The test rig includes:
+- 2.2 kW three-phase asynchronous motor
+- Torque sensor (e.g., S2001; ±0.5% F.S. accuracy)
+- Two-stage parallel gearbox (used in the rig configuration)
+- Magnetic powder brake (as the load generator)
+- Multi-channel data acquisition system (synchronous sampling at 12.8 kHz)
+
+Sensors and measurements:
+- Triaxial vibration acceleration sensor mounted on the motor drive end
+- Three-phase current clamps for phase currents
+- Key-phase sensor for key-phase signal (dimensionless)
+- Laboratory temperature controlled within a small range (e.g., ±2°C) to reduce experimental variance.
+
+Faults were physically introduced (e.g., precision machining / laser etching with tight tolerance) to ensure controllable and repeatable fault conditions.
+
+## Citation
+If you use this dataset, please cite:
+```
+@article{Chen2026MotorDataset,
+  title   = {Multi-mode Fault Diagnosis Datasets of Three-phase Asynchronous Motor Under Variable Working Conditions},
+  author  = {Shijin Chen and Zeyi Liu and Chenyang Li and Dongliang Zou and Xiao He and Donghua Zhou},
+  journal = {arXiv preprint arXiv:2601.02278},
+  year    = {2026}
+}
+```
+
+## License
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+## Acknowledgements
+We extend our sincere gratitude to the THUFDD Group, led by Prof. Xiao He and Prof. Donghua Zhou, for their invaluable support and contributions to the development of this scheme.
+
+We express our gratitude to the MCC5 Group Shanghai Co. LTD and Zhengzhou University for their invaluable support.