基于MF-DFA特征和LS-SVM算法的刀具磨損狀態(tài)識(shí)別

關(guān) 山，龐弘陽，宋偉杰，康振興

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基于MF-DFA特征和LS-SVM算法的刀具磨損狀態(tài)識(shí)別

關(guān) 山，龐弘陽，宋偉杰，康振興

（東北電力大學(xué)機(jī)械工程學(xué)院，吉林 132012）

切削刀具；刀具磨損；聲發(fā)射；狀態(tài)識(shí)別；多重分形；去趨勢(shì)波動(dòng)分析；支持向量機(jī)

0 引 言

切削是機(jī)械加工中的重要工序，為提高機(jī)械加工的自動(dòng)化和智能化水平，提高生產(chǎn)效率和質(zhì)量，迫切要求對(duì)刀具磨損狀態(tài)進(jìn)行可靠監(jiān)測(cè)，磨損狀態(tài)特征提取是實(shí)現(xiàn)刀具磨損狀態(tài)監(jiān)測(cè)的關(guān)鍵[1]。近年來學(xué)者運(yùn)用時(shí)頻譜、功率譜、小波變換等手段對(duì)切削力信號(hào)、振動(dòng)信號(hào)和聲發(fā)射信號(hào)等對(duì)刀具磨損狀態(tài)進(jìn)行監(jiān)測(cè)[2-4]。刀具磨損過程中發(fā)出的聲發(fā)射（acoustic emission，AE）信號(hào)受到刀具磨損，材料晶格滑移，刀具與工件摩擦以及刀具破損影響，呈現(xiàn)出隨機(jī)性、非線性和耗散性的特點(diǎn)，傳統(tǒng)線性信號(hào)處理方法難以精確提取磨損階段特征[5]。

筆者近幾年的研究中運(yùn)用非線性手段分析了刀具磨損AE信號(hào)的混沌特性和云特性，提高了識(shí)別準(zhǔn)確率[6-7]，然而這些特性不能表征刀具磨損的內(nèi)在動(dòng)力學(xué)特性。分形理論描述了自然界大量存在的偶然性和不規(guī)則，近年來隨著研究的深入，在故障診斷領(lǐng)域得到了一定的應(yīng) 用[8-13]。文獻(xiàn)[9-13]以振動(dòng)信號(hào)、AE信號(hào)以及刀口形貌圖像等為研究對(duì)象，利用廣義分形維數(shù)作為特征量，實(shí)現(xiàn)了對(duì)機(jī)械設(shè)備故障特征的提取，在整體反映非線性信號(hào)的分形特性上取得了一定效果，然而僅采用單分形方法很難準(zhǔn)確反映刀具磨損過程中復(fù)雜的內(nèi)在動(dòng)力學(xué)特性。Kantelhardt等[14]在單分形的基礎(chǔ)上，提出了多重分形去趨勢(shì)波動(dòng)分析（multifractal detrended fluctuations analysis，MF-DFA）方法，既可以反映非線性信號(hào)的整體分形特性，也具有較強(qiáng)的局部分析能力，能夠準(zhǔn)確描述信號(hào)的局部動(dòng)特性。目前MF-DFA方法在信號(hào)處理領(lǐng)域取得了一定的進(jìn)展[15-17]。文獻(xiàn)[15]應(yīng)用MF-DFA方法于液化泵退化特征提取中，分析了分形譜參數(shù)對(duì)液化泵不同退化狀態(tài)的反映能力。文獻(xiàn)[16]利用MF-DFA方法估計(jì)分形譜參數(shù)作為齒輪箱故障特征量，為齒輪箱故障特征提取提供了一種新方法。文獻(xiàn)[17]采用MF-DFA方法分析了風(fēng)電場風(fēng)速時(shí)間序列波動(dòng)，實(shí)現(xiàn)了風(fēng)速變化趨勢(shì)的預(yù)測(cè)。

針對(duì)刀具磨損AE信號(hào)通常具有隨機(jī)性強(qiáng)和易埋沒于噪聲的特點(diǎn)，筆者提出一種基于MF-DFA和最小二乘支持向量機(jī)(least square support vector machine， LS-SVM)算法的刀具磨損狀態(tài)特征識(shí)別方法。首先，用MF-DFA方法處理去噪后的刀具磨損AE信號(hào)，討論其長程相關(guān)性和分形特性；然后，分析對(duì)比了不同磨損階段下多重分形譜參數(shù)的變化，篩選出能靈敏表征刀具磨損狀態(tài)的多重分形譜參數(shù)作為特征量；最后，利用LS-SVM算法實(shí)現(xiàn)不同刀具與材料組合切削的刀具磨損狀態(tài)識(shí)別，驗(yàn)證本文所提方法的有效性，以期提高磨損監(jiān)測(cè)準(zhǔn)確性。

1 基于MF-DFA的多重分形譜分析

1.1 MF-DFA理論簡介

1）計(jì)算序列x偏離均值的累計(jì)離差()

2）將()劃分為互不重疊的長度（尺度）為的N(N= int(/))個(gè)等長子序列。為保證序列信息不丟失，則再從()尾端向前劃分一次共得到2N個(gè)子序列。

3）利用最小二乘法擬合等長子序列的局部趨勢(shì)函數(shù)y()

式中a為擬合多項(xiàng)式的系數(shù)，=0, 1,…,，為多項(xiàng)式擬合最高階數(shù)。

4）計(jì)算均方誤差函數(shù)2(,)

5）確定對(duì)于2N個(gè)子序列全序列的階波動(dòng)函數(shù)F()

式中階數(shù)的取值范圍為非零實(shí)數(shù)，當(dāng)=2時(shí)則為經(jīng)典的DFA法，即表示尺度下波動(dòng)的均方誤差2()。此外，當(dāng)<0時(shí)，F()依賴于2(,)的小波動(dòng)，當(dāng)>0時(shí)，F()依賴于2(,)的大波動(dòng)。

當(dāng)=0時(shí)，波動(dòng)函數(shù)由式（5）確定。

6）對(duì)F()的標(biāo)度行為和長程相關(guān)特性的描述體現(xiàn)于()上，若值變化，()不是唯一值，則原始序列是多重分形過程，否則原始序列是一個(gè)單分形過程；若原始序列｛x｝具有相關(guān)性，則2()與成冪律關(guān)系，即：

()被稱為廣義Hurst指數(shù)，表征原始序列相關(guān)性，可用最小二乘法線性擬合log(2())與log()得到的雙對(duì)數(shù)曲線斜率表示。當(dāng)=(2)時(shí)描述的是序列的長程相關(guān)性，被稱為長程相關(guān)指數(shù)，對(duì)于平穩(wěn)時(shí)間序列(2)就是Hurst指數(shù)。當(dāng)0.5<≤1，說明序列是持久的長程相關(guān)性，即將來會(huì)延續(xù)過去的遞增、遞減趨勢(shì)的性質(zhì)；<0.5，表明序列是負(fù)的、反持久的長程相關(guān)性，即將來與過去遞增、遞減趨勢(shì)相反；當(dāng)=0.5，意味著該序列是一獨(dú)立隨機(jī)過程，不相關(guān)。

1.2 MF-DFA和經(jīng)典多重分形理論的關(guān)系

通過MF-DFA方法得到的()和經(jīng)典多重分形理論中由標(biāo)準(zhǔn)配分函數(shù)得到的()存在式（7）關(guān)系[19]：

結(jié)合Legendre變換[20]對(duì)式（7）等號(hào)兩邊對(duì)求導(dǎo)得到多重分形譜()，奇異指數(shù)和()三者之間的關(guān)系為

1.3 估計(jì)多重分形譜特征參數(shù)

由多重分形譜可得到多重分形的3個(gè)重要參數(shù)：Δ，0和D。多重分形譜寬度Δ=max-min，反映信號(hào)多重分形特性的強(qiáng)弱，多重分形特征越強(qiáng)，D越大。極值點(diǎn)對(duì)應(yīng)的奇異指數(shù)0（max=(0)），反映信號(hào)的隨機(jī)性，隨機(jī)性越大，0越大。多重分形譜維度D=(max)-(min)，反映信號(hào)最大、最小峰值出現(xiàn)頻率的變化，D小于0，表明概率最大子集數(shù)目大于概率最小子集數(shù)目；反之亦然。

式中(0)為()中值。

2 刀具磨損狀態(tài)識(shí)別策略

支持向量機(jī)（support vector machine，SVM）結(jié)構(gòu)簡單，泛化能力較好，近幾年得到了廣泛的研究[21]。當(dāng)訓(xùn)練集規(guī)模很大時(shí)，求解標(biāo)準(zhǔn)支持向量機(jī)容易出現(xiàn)算法復(fù)雜、效率低等問題。因此，文獻(xiàn)[22]提出了一種最小二乘支持向量機(jī)LS-SVM改變了標(biāo)準(zhǔn)SVM的風(fēng)險(xiǎn)函數(shù)和約束問題，用求解線性方程組替代二次規(guī)劃問題，大大降低了計(jì)算的復(fù)雜度[23-25]。

支持向量機(jī)中的正則化參數(shù)和核函數(shù)參數(shù)對(duì)模型的分類性能有很大影響，優(yōu)化過程中參數(shù)之間相互影響，不能使結(jié)果最優(yōu)[26]。本文運(yùn)用Simplex迭代算法[27]進(jìn)行參數(shù)優(yōu)化，并結(jié)合舍一交叉驗(yàn)證構(gòu)建最優(yōu)模型對(duì)每組參數(shù)組合的性能進(jìn)行綜合判斷，來確定正則化參數(shù)和核函數(shù)參數(shù)。

（11）

約束條件為：

利用Lagrange法解式（11）得到：

本文中LS-SVM的輸入為特征向量數(shù)據(jù)，輸出為離散數(shù)值對(duì)應(yīng)刀具3個(gè)磨損階段。

3 試驗(yàn)與結(jié)果分析

3.1 試驗(yàn)數(shù)據(jù)來源及分析

為檢驗(yàn)方法的實(shí)用性和有效性，將提出的方法用于刀具磨損狀態(tài)識(shí)別。實(shí)測(cè)刀具磨損AE信號(hào)來源于刀具磨損試驗(yàn)系統(tǒng)，如圖1a所示。刀具磨損切削試驗(yàn)在CA6140車床進(jìn)行，聲發(fā)射傳感器依靠磁力緊緊吸附在刀柄近刀頭而不干擾切削的位置。本試驗(yàn)將刀具作為研究對(duì)象，一方面通過R15-ALPHA諧振式聲發(fā)射傳感器、PXPAⅡ?qū)拵爸寐暟l(fā)射放大器、PXI-6366數(shù)據(jù)采集卡（采樣頻率為2 MHz）、計(jì)算機(jī)等構(gòu)建了數(shù)據(jù)采集系統(tǒng)采集AE信號(hào)；另一方面使用顯微鏡測(cè)量刀具磨損量的大?。ň葹?.01 mm），建立刀具磨損狀態(tài)信號(hào)和磨損量的對(duì)應(yīng)關(guān)系。

圖1 刀具YT15切削高溫合金GH4169信號(hào)的采集

試驗(yàn)中采用2兩種刀片：YT15硬質(zhì)合金涂層刀片、KC9125硬質(zhì)合金涂層刀片；2種試驗(yàn)車削材料為：退火態(tài)高碳鋼T10、高溫合金GH4169。將刀片與試驗(yàn)材料交叉共產(chǎn)生4種組合，切削材料確定后，由于刀具壽命主要由切削三要素決定，因此每種組合考慮切削速度、進(jìn)給量、切削深度三要素，以刀具磨損量為指標(biāo)，設(shè)計(jì)三因素三水平正交試驗(yàn)以確定刀具不同磨損損狀態(tài)對(duì)應(yīng)的信號(hào)采集時(shí)間。由于高溫合金GH4169對(duì)刀具的磨損速率較快，刀具達(dá)到進(jìn)一步磨損的時(shí)間較短，因此更換新刀后，在上一次切削時(shí)間上延遲切削20 s進(jìn)行一次數(shù)據(jù)采集；而退火態(tài)高碳鋼T10對(duì)刀具的磨損速率較低，在上一次切削時(shí)間上延遲切削180 s進(jìn)行一次數(shù)據(jù)采集，在數(shù)據(jù)采樣完成后同時(shí)進(jìn)行刀具磨損量的測(cè)量，以YT15刀具切削退火態(tài)高碳鋼T10為例制定了圖1b所示的信號(hào)具體采集過程和表1所示的刀具3種磨損狀態(tài)界定范圍和磨損極限。為了使信號(hào)更好地反映刀具當(dāng)前的磨損狀態(tài)，所以僅記錄每次切削過程最后5 s的數(shù)據(jù)，減少數(shù)據(jù)采集量；更換新刀片的目的在于更精準(zhǔn)地模擬刀具連續(xù)切削的過程。

表1 切削退火態(tài)高碳鋼T10時(shí)刀具磨損階段定義

本文以切削速度為 520 r/min，切削深度為 0.5 mm，進(jìn)給量為0.176 mm/r時(shí)采集的AE信號(hào)為例進(jìn)行說明。研究選取初期、正常、急劇磨損狀態(tài)下各60組樣本，每個(gè)樣本取8 192個(gè)采樣點(diǎn)。圖2為YT15硬質(zhì)合金刀具與T10組合切削在不同磨損階段10 000個(gè)采樣點(diǎn)的AE信號(hào)時(shí)域波形。由圖2可見，不同磨損階段的AE信號(hào)在時(shí)域結(jié)構(gòu)上波動(dòng)復(fù)雜且具有較明顯的差異，磨損狀態(tài)信號(hào)隱藏于背景噪聲中，如果直接采用此信號(hào)來分析，則難以提取正確的磨損階段特征。將采集的AE信號(hào)先用小波包分析進(jìn)行去噪處理，基于最小Shannon準(zhǔn)則來確定小波包分解最佳樹并重構(gòu)[28-30]，來達(dá)到信號(hào)初步去噪的目的。

3.2 磨損AE信號(hào)的長程相關(guān)性和分形特性

利用MF-DFA方法分析刀具磨損AE信號(hào)的多重分形特性，要求波動(dòng)函數(shù)F()與子序列長度有良好的對(duì)數(shù)線性關(guān)系[31-32]。當(dāng)取40，多項(xiàng)式擬合階數(shù)取1～6，=[-10,-9, …, 9, 10]時(shí)，討論=2時(shí)，y()中與的關(guān)系。采用最小二乘法線性擬合Hurst指數(shù)，采用式（15）計(jì)算擬合決定系數(shù)2，2的數(shù)值都大于0.9，表明擬合直線滿足統(tǒng)計(jì)檢驗(yàn)。

式中為序列的均值；為q階擬合值；為擬合序列的均值，N為序列長度。

圖3給出了YT15硬質(zhì)合金刀具與T10組合切削在正常磨損階段值變化下Hurst曲線擬合結(jié)果。圖3a為磨損AE信號(hào)均方誤差函數(shù)F()與尺度隨階數(shù)的變化的雙對(duì)數(shù)關(guān)系。每條均方誤差函數(shù)F()與尺度的雙對(duì)數(shù)曲線線性擬合的直線斜率就是Hurst指數(shù)，結(jié)合表2中的數(shù)值分析：Hurst指數(shù)為根據(jù)實(shí)際的logF()與log值用最小二乘線性擬合方法擬合的曲線斜率，決定系數(shù)2表示Hurst指數(shù)擬合值與實(shí)際點(diǎn)的決定系數(shù)，計(jì)算方法如式（15）；當(dāng)取不同值時(shí)，logF()與log都具有較良好的線性關(guān)系；隨的增大呈現(xiàn)波動(dòng)性，但都大于0.5小于1（0.5<<1），說明刀具磨損時(shí)間序列的是具有長程相關(guān)性的有序過程，內(nèi)部波動(dòng)不隨機(jī)，具有維持趨勢(shì)發(fā)展的能力。

圖3 擬合階數(shù)k值變化下Hurst指數(shù)擬合結(jié)果

當(dāng)取不同值時(shí)，磨損AE信號(hào)的廣義Hurst指數(shù)()與波動(dòng)階數(shù)關(guān)系曲線變化如圖3b所示。()隨的增大而減小，呈非線性遞減關(guān)系，表明磨損AE信號(hào)存在不規(guī)則多重分形特征，具有不同的內(nèi)在動(dòng)力學(xué)特性。當(dāng)=2時(shí)，()等于經(jīng)典Hurst指數(shù)[33-34]，對(duì)于任意，的值均大于0.5，說明磨損AE信號(hào)具有長程相關(guān)特性。

表2 Hurst指數(shù)的擬合結(jié)果

為描述刀具不同磨損階段AE信號(hào)不同層次的波動(dòng)，討論=1時(shí)F()與的冪律關(guān)系曲線如圖4所示，可以看出logF()與log呈良好的線性關(guān)系，即F()與存在冪律關(guān)系，即不同磨損階段下的AE信號(hào)在一定尺度上存在標(biāo)度不變性，具有多重分形特征。

圖4 不同磨損階段下均方誤差函數(shù)Fq(s)與尺度s的冪律關(guān)系

3.3 多重分形譜特征參數(shù)

表3 不同磨損階段的多重分形譜參數(shù)平均值

圖5 不同磨損階段AE信號(hào)特征分布

綜上分析，利用MF-DFA方法計(jì)算刀具磨損AE信號(hào)的多重分形譜參數(shù)，構(gòu)造表征刀具磨損階段的三維特征向量。

計(jì)算不同磨損階段的特征向量，繪制三維分布散度圖如圖6所示，可以看出：采用文中提出的方法提取的刀具磨損狀態(tài)特征能很好地表征刀具的磨損狀態(tài)。

注：v表示切削速度，表示進(jìn)給量，a表示切削深度。

Note：vrepresents cutting speed,represents feeding rate, andarepresents cutting depth.

圖6 不同刀具與切削材料組合下不同磨損階段特征參數(shù)的三維散度圖

Fig.6 Three dimensional divergence diagram of characteristic parameters in different wear stages under different cutting tools and cutting materials

3.4 刀具磨損狀態(tài)識(shí)別

將YT15硬質(zhì)合金刀具與T10組合下信號(hào)提取的不同磨損階段去噪后信號(hào)的多重分形特征參數(shù)作為輸入，其中90組訓(xùn)練樣本，90組識(shí)別樣本，每種磨損階段均各30組。采用SVM和LS-SVM進(jìn)行分類，核函數(shù)類型均為RBF。運(yùn)用交叉驗(yàn)證方法[35]優(yōu)化SVM分類器參數(shù)結(jié)果正則化參數(shù)2為4.128 7，核函數(shù)參數(shù)為12.351 5，優(yōu)化LS-SVM得到的2為0.194 1，為3.712 3。

為驗(yàn)證基于LS-SVM方法識(shí)別分類器的有效性，另外采用相同的訓(xùn)練樣本及測(cè)試樣本特征量輸入L-M（Levenberg-Marquardt）優(yōu)化算法BP[36-37]神經(jīng)網(wǎng)絡(luò)中進(jìn)行識(shí)別比較。神經(jīng)網(wǎng)絡(luò)中隱層節(jié)點(diǎn)數(shù)的選擇至關(guān)重要[38]，本文根據(jù)隱層節(jié)點(diǎn)數(shù)選取規(guī)則計(jì)算不同節(jié)點(diǎn)數(shù)下BP識(shí)別結(jié)果如表4所示，綜合表中收斂次數(shù)、訓(xùn)練時(shí)間和識(shí)別率選擇3-6-3 BP神經(jīng)網(wǎng)絡(luò)進(jìn)行識(shí)別。

分別計(jì)算測(cè)試樣本中成功識(shí)別的樣本占總測(cè)試樣本的百分比為識(shí)別準(zhǔn)確率，表5為3種分類器的識(shí)別率比較，可以看出，采用LS-SVM方法的識(shí)別率高于傳統(tǒng)SVM的識(shí)別率并明顯高于BP神經(jīng)網(wǎng)絡(luò)的識(shí)別率。

表4 不同隱層節(jié)點(diǎn)數(shù)的BP模型對(duì)磨損狀態(tài)識(shí)別結(jié)果

表5 三種分類器識(shí)別率對(duì)比

4 結(jié) 論

2）通過刀具磨損實(shí)測(cè)AE信號(hào)的研究，結(jié)果表明基于MF-DFA（multifractal detrended fluctuations analysis）和LS-SVM（least square support vector machine）的方法提取的多重分形譜特征能夠很好地識(shí)別出刀具不同磨損階段，驗(yàn)證了該識(shí)別方法的有效性，對(duì)比支持向量機(jī)和神經(jīng)網(wǎng)絡(luò)識(shí)別結(jié)果，LS-SVM算法識(shí)別率最高，平均準(zhǔn)確率可達(dá)97.78%，為實(shí)現(xiàn)磨損量預(yù)測(cè)打下基礎(chǔ)。

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Cutting tool wear recognition based on MF-DFA feature and LS-SVM algorithm

Guan Shan, Pang Hongyang, Song Weijie, Kang Zhenxing

(132012,)

Cutting is an important process in machining. In order to improve the automatic and intelligent level of machining and improve the production efficiency and quality, it is urgent to monitor the tool wear state. The feature extraction of wear state is the key to the tool wear monitoring. In view of the unique advantages of multifractal theory in accurately depicting the nonlinear phenomena and processes of the system, a tool wear state recognition method based on multifractal detrended fluctuation analysis (MF-DFA) and least squares support vector machine (LS-SVM) is proposed. The acoustic emission (AE) signal is denoised with wavelet packet analysis, and the best tree of wavelet packet decomposition is determined and reconstruction is performed based on the minimum Shannon criterion so as to achieve the purpose of signal initial denoising. Firstly, the MF-DFA method is used to deal with the noise emission signals of the tool wear after denoising, and the long range correlation and fractal characteristics are discussed. It shows that the tool wear time sequence is an orderly process with long range correlation, and the internal fluctuation is not random, and it has the ability to maintain the trend. Then, the multifractal spectrum parameters of different wear stages were analyzed and compared. The parameters of singular exponent corresponding to the point of extreme value and multifractal spectrum widthare increasing with the progression of the wear stage, which indicates that the greater the wear amount, the greater the fluctuation of the AE signal, the more uneven the probability measurement of the whole fractal structure, the more random the fluctuation. The values of the AE signal multifractal dimensionunder different wear states are less than zero, and the multifractal spectrum is left hook like, indicating the number of the maximum subset in the probability measure is relatively large. The absolute value of the normal wear stage is the smallest, which indicates that the volatility is the smallest in this stage; the value of the parameter increases with the increase of the wear amount, indicating that the greater the fluctuation degree of generalized Hurst exponent, the stronger the multifractal characteristics. The singular exponent corresponding to the point of extreme value, the multifractal spectrum widthand the mean of the generalized Hurst exponent, which can sensitively characterize the tool wear state, were selected as the characteristic quantities, and the three-dimensional feature vectors were constructed to characterize the tool wear stage. The clustering effect of the extracted tool wear state characteristics was obvious. The LS-SVM algorithm, SVM algorithm and BP (back propagation) neural network are applied to recognize the tool wear state. Simplex iterative algorithm is used to optimize the parameters, the optimal model is constructed to determine the performance of each group of parameters, and the parameters of regularization and kernel function are determined. The average recognition accuracy is 97.78%. The results show that the tool wear AE signal has long range correlation and obvious multifractal characteristics, the multifractal parameters, i.e. singular exponent corresponding to the point of extreme value,multifractal spectrum widthand mean of the generalized Hurst exponent can be used as sensitive characterization for the feature of tool wear stage, and the tool wear stages can be clearly distinguished. The multifractal spectrum features extracted with the method based on MF-DFA and LS-SVM can identify the different wear stages of the tool well, verify the effectiveness of the recognition method, improve the accuracy of recognition, and lay a foundation for the realization of the wear prediction.

cutting tools; wear of cutting tools; acoustic emission; state recognition; multifractal; detrending wave method; support vector machine

2018-02-01

2018-05-16

吉林省科技廳科技公關(guān)計(jì)劃（20170520099JH）；吉林省省教育廳“十二五”科學(xué)技術(shù)研究項(xiàng)目（20150249）

關(guān) 山，男，吉林省吉林市人，博士，教授，主要從事機(jī)械設(shè)備故障診斷研究。Email：guanshan1970@163.com

10.11975/j.issn.1002-6819.2018.14.008

TH165+.3；TP206

A

1002-6819(2018)-14-0061-08

關(guān) 山，龐弘陽，宋偉杰，康振興. 基于MF-DFA特征和LS-SVM算法的刀具磨損狀態(tài)識(shí)別[J]. 農(nóng)業(yè)工程學(xué)報(bào)，2018，34(14)：61－68. doi：10.11975/j.issn.1002-6819.2018.14.008 http://www.tcsae.org

Guan Shan, Pang Hongyang, Song Weijie, Kang Zhenxing. Cutting tool wear recognition based on MF-DFA feature and LS-SVM algorithm[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2018, 34(14): 61－68. (in Chinese with English abstract) doi：10.11975/j.issn.1002-6819.2018.14.008 http://www.tcsae.org