Multi-mode fault diagnosis datasets of three-phase asynchronous motor under variable working conditions
Shijin Chen1,2, Zeyi Liu3,4, Chenyang Li5
1MCC5 Group Shanghai Co. LTD 201900, ShangHai, China.
Data in Brief
|March 2, 2026
Summary
This study introduces a new dataset for diagnosing faults in three-phase asynchronous motors under variable operating conditions. The data aids in developing robust fault detection methods for industrial applications.
Area of Science:
- Electrical Engineering
- Mechanical Engineering
- Data Science
Background:
- Three-phase asynchronous motors are critical in industry, but failures cause significant downtime.
- Vibration and current signals are key indicators for motor health monitoring.
- Variable operating conditions like fluctuating speeds and loads complicate fault diagnosis.
Purpose of the Study:
- To present a comprehensive dataset for three-phase asynchronous motor fault diagnosis.
- To include diverse fault types (single and compound) and severities.
- To cover realistic, variable speed and load conditions, including transitional states.
Main Methods:
- Collected triaxial vibration, three-phase currents, torque, and key-phase signals.
- Acquired data from a three-phase asynchronous motor under various fault conditions.
- Simulated single and compound faults (rotor unbalance, short circuits, bearing faults).
Main Results:
- A rich dataset encompassing diverse operational states and fault scenarios was created.
- The dataset captures data under both steady and transitional conditions.
- Includes single and mechanical-electrical compound faults for comprehensive analysis.
Conclusions:
- The dataset is valuable for developing and validating advanced fault diagnosis techniques.
- Enables the creation of more robust diagnostic tools for electric motors.
- Supports research into condition monitoring under realistic industrial environments.
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