Fault Diagnosis Method of Asynchronous Motor Based on Deep Learning
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摘要: 异步电机作为整个传动系统的灵魂,如果没有异步电机的正常工作就无法完成其他各项工作。基于此,本文采用深度学习的方法来对异步电机故障进行诊断,通过深入研究精准找到异步电机发生故障的部位以及快速找寻到异步电机故障的诊断方法,从而进一步提高异步电机的使用寿命。Abstract: As the soul of the whole transmission system, asynchronous motor can not complete other work without the normal work of asynchronous motor. Based on this, this paper uses the method of deep learning to diagnose the fault of asynchronous motor, through in-depth study, accurately find the fault location of asynchronous motor and quickly find the fault diagnosis method of asynchronous motor, so as to further improve the service life of asynchronous motor.
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Key words:
- deep learning /
- DBN /
- asynchronous motor /
- fault diagnosis
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[1] 李艳兰.异步电动机电气故障的识别与诊断[D].太原:太原理工大学,2015. [2] 朱丽娟.基于小波神经网络的异步电动机振动故障诊断研究[D].太原:太原理工大学,2018. [3] 韩敏,崔丕锁.一种用于模式识别的动态RBF神经网络算法[J].大连理工大学学报,2006(5):746-751. [4] 陈耀武,汪乐宇.转子机械故障诊断仪器系统[J].中国电机工程学报,2000(12):48-52.
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