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基于EMD與神經(jīng)網(wǎng)絡(luò)的內(nèi)燃機氣門間隙故障診斷
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    摘要:

    利用LabVIEW構(gòu)建了基于EMD與神經(jīng)網(wǎng)絡(luò)的內(nèi)燃機氣門間隙故障診斷系統(tǒng),。用490BPG型發(fā)動機在轉(zhuǎn)速為1 200 r/min,、無負(fù)荷時進(jìn)行了試驗研究，采用經(jīng)驗?zāi)Ｊ椒纸釫MD方法對氣門振動信號進(jìn)行分解，對分解得到的前4個固有模態(tài)函數(shù)IMF分別求其關(guān)聯(lián)維數(shù),，將IMF1～I(xiàn)MF4的關(guān)聯(lián)維數(shù)作為神經(jīng)網(wǎng)絡(luò)的輸入向量,，用4種工況的80組樣本訓(xùn)練了內(nèi)燃機氣門故障診斷系統(tǒng)的網(wǎng)絡(luò)模型,。試驗結(jié)果表明,，20組測試樣本的測試結(jié)果均與實際狀況相一致，診斷準(zhǔn)確率為100%,，該系統(tǒng)能快速準(zhǔn)確地識別內(nèi)燃機氣門間隙故障,。

    Abstract:

    A fault diagnosis system was constructed and designed based on EMD correlation dimension, artificial neural network and LabVIEW. The experiment was done with 490BPG model engine when it worked at 1 200 r/min without load. The original vibration signals of valve were decomposed based on EMD method, and then the correlation dimensions of the top four intrinsic mode functions(IMF1～I(xiàn)MF4) were calculated, the correlation dimensions of the IMF1～I(xiàn)MF4 as the input parameters of the artificial neural network were taken and the network model was trained with 80 training samples of four work status, it indicated that the test results of 20 test samples of the experiment conformed to the practical status and the correct rate was 100%. The system could be used online to inspect and diagnose the faults of engine valve clearance. 

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王祝平,王為,李小昱,張軍.基于EMD與神經(jīng)網(wǎng)絡(luò)的內(nèi)燃機氣門間隙故障診斷[J].農(nóng)業(yè)機械學(xué)報,2007,38(12):133-136.[J]. Transactions of the Chinese Society for Agricultural Machinery,2007,38(12):133-136.

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