用神经网络实时监测电机电压电流，快速识别六类常见故障。
Fault Analysis And Predictive Maintenance Of Induction Motor Using Machine Learning
- 输入三相电压电流，用前向神经网络实现故障分类。
- 在0.33马力电机上实测，故障识别准确率高。
- 无需额外传感器，适合工业电机的智能维护场景。
异步电机是工业中至关重要的电气设备，应用广泛。本文提出一种基于机器学习的故障检测与分类模型，仅使用三相电压和电流作为输入。旨在通过早期检测与诊断，保护关键电气部件并防止异常事件恶化。该研究构建了一个快速前馈人工神经网络模型，用于检测过压、欠压、断相、电压不平衡、过载及接地故障等常见电气故障。提出一种无需外部传感器的监测系统，电机自身作为传感器，仅监控输入信号。通过分类器设定健康与故障状态下的电流电压阈值。利用0.33马力异步电机的实时数据训练与测试神经网络。所建模型可在特定时刻分析电压电流值，并将其分类为无故障或具体故障类型。随后将模型接入真实电机，实现故障的精准检测与分类，以便及时采取后续措施。
原文摘要 · Abstract (English)
Induction motors are one of the most crucial electrical equipment and are extensively used in industries in a wide range of applications. This paper presents a machine learning model for the fault detection and classification of induction motor faults by using three phase voltages and currents as inputs. The aim of this work is to protect vital electrical components and to prevent abnormal event progression through early detection and diagnosis. This work presents a fast forward artificial neural network model to detect some of the commonly occurring electrical faults like overvoltage, under voltage, single phasing, unbalanced voltage, overload, ground fault. A separate model free monitoring system wherein the motor itself acts like a sensor is presented and the only monitored signals are the input given to the motor. Limits for current and voltage values are set for the faulty and healthy conditions, which is done by a classifier. Real time data from a 0.33 HP induction motor is used to train and test the neural network. The model so developed analyses the voltage and current values given at a particular instant and classifies the data into no fault or the specific fault. The model is then interfaced with a real motor to accurately detect and classify the faults so that further necessary action can be taken.
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