基于振动的反向传播神经网络感应电机故障识别

Kuspijani Kuspijani, Richa Watiasih, Prihastono Prihastono
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引用次数: 3

摘要

振动分析在当今工业的预测性维护中非常重要。本文设计了一种能够在线识别异步电动机状态的振动检测系统。感应电机的振动数据从两个振动传感器accelerometer1和accelerometer2读取,使用ATMEGA16微控制器显示,并可以通过基于人工智能的反向传播神经网络(B-NN)分析直接识别感应电机的状态。经过测试,该系统对异步电动机进行故障识别,成功率达95%。
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Faults Identification of Induction Motor Based On Vibration Using Backpropagation Neural Network
Vibration analysis is very important in predictive maintenance in the industry today. In this paper, has designed a vibration detection system that can identify the condition of the induction motor by online. Induction motor vibration data is read from two vibration sensors, accelerometer1 and accelerometer2, displayed using ATMEGA16 microcontroller and can directly identify the condition of the induction motor by using artificial intelligence-based analysis that is Backpropagation-Neural Network (B-NN). After testing, the system managed to perform fault identification induction motor with the rank of success of 95 percent.
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