Multi-mode Fault Diagnosis Datasets of Three-phase Asynchronous Motor Under Variable Working Conditions

TL;DR

This paper introduces a comprehensive dataset of three-phase asynchronous motor signals under various fault types and operational conditions, aiding the development of robust fault diagnosis methods in realistic industrial scenarios.

Contribution

It provides a detailed, multi-condition dataset capturing diverse faults and operating states, which is novel for advancing fault diagnosis research in variable conditions.

Findings

Dataset includes multiple fault types and severities.

Signals collected under steady and transitional states.

Supports development of robust fault diagnosis methods.

Abstract

Three-phase asynchronous motor are fundamental components in industrial systems, and their failure can lead to significant operational downtime and economic losses. Vibration and current signals are effective indicators for monitoring motor health and diagnosing faults. However, motors in real applications often operate under variable conditions such as fluctuating speeds and loads, which complicate the fault diagnosis process. This paper presents a comprehensive dataset collected from a three-phase asynchronous motor under various fault types and severities, operating under diverse speed and load conditions. The dataset includes both single faults and mechanical-electrical compound faults, such as rotor unbalance, stator winding short circuits, bearing faults, and their combinations. Data were acquired under both steady and transitional conditions, with signals including triaxial vibration, three-phase currents, torque, and key-phase signals. This dataset supports the development and validation of robust fault diagnosis methods for electric motors under realistic operating conditions.