MOTOR FAULT DETECTION DATA

Induction motors play a crucial role in industrial applications, but their operation is often compromised by various mechanical and electrical faults. This paper presents a new dataset for comprehensive fault diagnosis of three-phase induction motors, using synchronized multi-sensor data collection. The dataset includes real-time measurements of vibration, voltage, and current collected from a 0.2 kW squirrel cage induction motor. Fault scenarios such as phase removal and mechanical misalignments were simulated to capture a wide range of motor behaviors. Data were collected using high-resolution sensors, with the vibration ,voltage and current sampled at 50kHz. The dataset is organized into tem distinct CSV files, covering different operational scenarios, providing a comprehensive resource for researchers aiming to develop or test fault detection algorithms. The dataset was used to train a RandomForest classifier for fault detection, achieving an accuracy of 99.82%. This demonstrates the effectiveness of the dataset for developing machine learning models aimed at real-time fault diagnosis and predictive maintenance. Unlike existing datasets, this collection provides synchronized data across multiple sensor types, enabling cross-analysis of electrical and mechanical faults. The dataset is publicly available, offering a valuable tool for advancing research in motor fault diagnosis and predictive maintenance.

感应电动机在工业应用中发挥着至关重要的作用，但其运行常受各类机械与电气故障的影响而受阻。本文提出了一种基于同步多传感器数据采集的新型三相感应电动机综合故障诊断数据集。该数据集包含从一台0.2kW鼠笼式感应电动机上采集的振动、电压、电流实时测量数据。研究模拟了缺相、机械不对中等故障场景，以覆盖电动机的多种运行状态。数据通过高分辨率传感器采集，振动、电压与电流的采样率均为50kHz。该数据集包含10个独立的CSV文件，覆盖不同的运行场景，可为致力于开发或测试故障检测算法的研究人员提供全面的研究资源。本数据集被用于训练故障检测用随机森林（RandomForest）分类器，准确率达99.82%。这验证了该数据集在开发面向实时故障诊断与预测性维护的机器学习模型方面的有效性。与现有数据集不同，本数据集提供了多传感器类型的同步数据，可实现电气与机械故障的交叉分析。该数据集已公开，可为电动机故障诊断与预测性维护领域的研究推进提供宝贵工具。

搜集汇总

背景与挑战

背景概述

该数据集是一个用于三相感应电机故障诊断的公开数据集，包含振动、电压和电流的同步多传感器测量数据，采样频率为50kHz，模拟了相位移除和机械不对中等故障场景。数据集组织为十个CSV文件，覆盖不同操作条件，支持电气和机械故障的交叉分析，已成功用于训练准确率达99.82%的机器学习模型，为电机故障检测和预测性维护研究提供了全面资源。