Multi-Modal Fault Diagnosis Data for Induction Motor Under Variable Conditions

Offering synchronized multi-modal time-series data from a 2.2 kW three-phase asynchronous motor operating under variable speed and load conditions with deliberately induced faults. It is intended for developing and benchmarking robust fault diagnosis methods under realistic, time-varying operating scenarios.

Use Cases

• Train a model on synchronized multi-modal time-series data to classify deliberately induced motor faults under variable speed conditions.
• Benchmark fault diagnosis methods using data from a 2.2 kW three-phase asynchronous motor operating under variable load conditions.
• Develop robust predictive maintenance algorithms for induction motors using multi-modal data collected from realistic operating scenarios.

Strengths

• Data is collected from a specific 2.2 kW three-phase asynchronous motor.
• Includes multi-modal time-series data synchronized under variable operating conditions.
• Features deliberately induced faults for developing diagnostic methods.

Limitations

• The dataset size, row count, column count, and specific file formats are unknown.
• Specific data modalities and sensor types are not detailed in the provided input.
• The geographic origin and precise temporal coverage of the data collection are unspecified.

Provenance

Source

Author 'Samlzy' on Hugging Face.

Collection Method

Data collected from a physical 2.2 kW three-phase asynchronous motor under variable speed/load conditions with induced faults.

Freshness

The dataset was last updated on 2026-01-06.