摘 要：為進(jìn)一步提高異步電動(dòng)機(jī)故障檢測(cè)的準(zhǔn)確性，將人工神經(jīng)網(wǎng)絡(luò)應(yīng)用于異步電動(dòng)機(jī)故障檢測(cè)。通過(guò)提出一種基于BP神經(jīng)網(wǎng)絡(luò)的電機(jī)故障檢測(cè)方法，設(shè)計(jì)了適合該檢測(cè)系統(tǒng)的網(wǎng)絡(luò)結(jié)構(gòu)。仿真結(jié)果表明：相對(duì)于其他算法，該網(wǎng)絡(luò)結(jié)構(gòu)具有更快的學(xué)習(xí)速度和更高的學(xué)習(xí)精度，完全適用于電動(dòng)機(jī)故障檢測(cè)。
關(guān)鍵詞：人工神經(jīng)網(wǎng)絡(luò)；異步電動(dòng)機(jī)；故障檢測(cè)；模式識(shí)別；模式分類(lèi)
中圖分類(lèi)號(hào)：O242.1；TP751 文獻(xiàn)標(biāo)識(shí)碼：A
文章編號(hào)：1672-4984（2008）03-0135-03
Fault diagnosis system for asynchronous electromotor based on artificial neural network
LIU Zhao-you， QIU Shi-hui， WANG Qi
（Department of Electrical Engineering，Chengdu Electromechanical College，Chengdu 610031，China）
Abstract： Artificial neural network was applied to detect the fault of asynchronous electromotor in order to improve the accuracy of asynchronous electromotor fault diagnosis. One fault diagnosis method for electromotor based on BP neural network was proposed，and the network structure was designed for this diagnosis system. The simulation result indicates that the net structure has mush faster learning speed and more superior learning precision compared with other algorithm. It is entirely practical for the fault diagnosis system of the electromotor.
Key words： Artificial neural network； Electromotor； Fault diagnosis system； Pattern recognition； Pattern classification
Editor：liyan
