航空發(fā)動機(jī)在高溫、高壓、高轉(zhuǎn)速及較大振動等惡劣的條件下工作時,其控制系統(tǒng)中的傳感器很容易受到干擾,所以發(fā)動機(jī)測量參數(shù)中常常包含較大的噪聲。另一方面,發(fā)動機(jī)的測量參數(shù)多于其獨(dú)立變量的數(shù)量,即在這些測量參數(shù)中存在冗余信息。AANN(自聯(lián)想神經(jīng)網(wǎng)絡(luò)) 通過對信息的壓縮及解壓縮過程,能夠利用冗余信息抑制其測量噪聲。在發(fā)動機(jī)故障診斷過程中,應(yīng)用自聯(lián)想神經(jīng)網(wǎng)絡(luò)對測量參數(shù)進(jìn)行預(yù)處理,可以大大提高故障診斷的準(zhǔn)
確率。
關(guān)鍵詞:航空發(fā)動機(jī);故障診斷;神經(jīng)網(wǎng)絡(luò);數(shù)字濾波
Abstract : Sensors in engine control system are easily disturbed , as aeroengine operates in an environment of high temperature , high pressure ,high speed and rough vibration. Therefore engine measurements contain nois2 es. On the other hand , the number of measurements is greater than that of independent variables in the sys2 tem , which implies that there is spare information in the parameters. Auto associative neural network (AANN) is introduced to reduce the noise level contained through mapping and decoding process. It is found that the
noise can be greatly filtered to result in a higher success rate of fault diagnosis of aeroengine.
Key words :aeroengine ; fault diagnosis ; neural network ; digital filter