Suzhou Electric Appliance Research Institute
期刊號: CN32-1800/TM| ISSN1007-3175

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基于BP神經網絡模型的異步電動機故障辨識

來源:電工電氣發布時間:2021-08-18 12:18 瀏覽次數:62

基于BP神經網絡模型的異步電動機故障辨識

喬維德
(無錫開放大學 科研與質量控制處,江蘇 無錫 214011)
 
    摘 要:針對目前三相異步電動機故障診斷方法存在的局限性及其缺陷,在利用小波包分析提取電動機故障信號特征量基礎上,提出基于蝙蝠-粒子群及改進 BP 算法的異步電動機 BP 神經網絡故障辨識模型,采用蝙蝠-粒子群算法優化 BP 神經網絡結構參數,利用改進 BP 算法訓練 BP 神經網絡。仿真結果分析表明,該 BP 神經網絡模型用于三相異步電動機故障辨識,辨識速度快、準確度高、可靠性好。
    關鍵詞:異步電動機;小波包分析;蝙蝠-粒子群算法;改進 BP 算法;故障辨識
    中圖分類號:TM307 ;TM343+.2     文獻標識碼:A     文章編號:1007-3175(2021)08-0006-05
 
Asynchronous Motor Fault Identification Based on BP Neural Network Model
 
QIAO Wei-de
(Scientific Research and Quality Control Division, Wuxi Open University, Wuxi 214011,China)
 
    Abstract: In view of the limitations and deficiencies of current three-phase asynchronous motor fault diagnosis methods, based on the use of wavelet packet analysis to extract the characteristics of the motor fault signal, a fault identification model of asynchronous motor BP neural network based on bat-particle swarm and improved BP algorithm is proposed. The bat-particle swarm algorithm is used to optimize the structural parameters of the BP neural network, and the improved BP algorithm is used to train the BP neural network. The analysis of simulation results shows that the BP neural network model is used for fault identification of three-phase asynchronous motors, with fast identification speed, high accuracy and good reliability.
    Key words: asynchronous motor; wavelet packet analysis; bat-particle swarm algorithm; improved BP algorithm; fault identification
 
參考文獻
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