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基于FDR閾值自動(dòng)選取的拖拉機(jī)PTO轉(zhuǎn)矩載荷譜外推
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國(guó)家重點(diǎn)研發(fā)計(jì)劃項(xiàng)目(2016YFD0700102)


Extrapolation of Tractor PTO Torque Load Spectrum Based on Automated Threshold Selection with FDR
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    摘要:

    針對(duì)時(shí)域外推過程中常用的閾值選取計(jì)算復(fù)雜、精度低等問題,基于錯(cuò)誤發(fā)現(xiàn)率(False discovery rate, FDR)提出了一種閾值自動(dòng)選取方法,并基于該方法進(jìn)行了拖拉機(jī)動(dòng)力輸出軸(Power takeoff, PTO)轉(zhuǎn)矩載荷譜外推研究。首先,利用開發(fā)的拖拉機(jī)PTO轉(zhuǎn)矩?zé)o線監(jiān)測(cè)系統(tǒng)采集拖拉機(jī)作業(yè)工況下的載荷數(shù)據(jù),并進(jìn)行數(shù)據(jù)預(yù)處理;將順序擬合檢驗(yàn)與多重檢驗(yàn)相結(jié)合,采用FDR自動(dòng)閾值選取法選取了最優(yōu)閾值,確定了時(shí)域外推數(shù)據(jù)的上限閾值為342N·m,下限閾值為100N·m;基于極大似然估計(jì)對(duì)超閾值數(shù)據(jù)進(jìn)行了廣義帕累托分布(Generalized Pareto distribution, GPD)尺度參數(shù)和形狀參數(shù)的擬合,建立了PTO轉(zhuǎn)矩載荷的超閾值模型,并與傳統(tǒng)圖像法的擬合結(jié)果進(jìn)行了比較,結(jié)果表明,兩種方法的擬合結(jié)果與載荷樣本的決定系數(shù)均大于0.995;從擬合優(yōu)度來看,對(duì)于上限閾值,自動(dòng)閾值選取法的擬合優(yōu)度比圖像法的擬合優(yōu)度降低8.7%,而對(duì)于下限閾值,自動(dòng)閾值選取法比圖像法降低31.21%。利用時(shí)域外推方法對(duì)PTO轉(zhuǎn)矩載荷數(shù)據(jù)進(jìn)行外推,對(duì)外推1倍后的載荷時(shí)間歷程與原載荷時(shí)間歷程進(jìn)行對(duì)比分析;當(dāng)時(shí)域外推因子為131、PTO轉(zhuǎn)矩累積頻次達(dá)到106次時(shí),得到了PTO轉(zhuǎn)矩載荷譜;基于統(tǒng)計(jì)學(xué)特征與雨流計(jì)數(shù)分析對(duì)外推載荷譜進(jìn)行了驗(yàn)證,結(jié)果表明,外推后的載荷譜與樣本載荷譜分布規(guī)律一致,能夠在保留載荷特征的前提下實(shí)現(xiàn)均值、幅值的雙向外推。

    Abstract:

    The compilation of the load spectrum of tractor power output components is the main method to study the relationship between tractor working load and fatigue life. Based on the peak over threshold (POT) model, the time-domain extrapolation method of the tractor power take-off (PTO) operation load spectrum was studied. Aiming at the problems of complex calculation and low accuracy of the commonly used threshold selection methods for timedomain extrapolation, an automatic threshold selection method based on false discovery rate (FDR) was proposed. Based on this method, extrapolation of the tractor PTO torque load spectrum was realized. Firstly, a tractor PTO torque wireless acquisition system was developed to collect tractor PTO operating load data and perform data preprocessing;then, the FDR automatic threshold selection method was used to select the optimal threshold, and the upper threshold of the time-domain extrapolation data was determined to be 342N·m and the lower threshold was 100N·m. The maximum likelihood estimation was used to fit the scale parameters and shape parameters of the generalized Pareto distribution of excess threshold data, and the peak over threshold (POT) model of PTO torque load was built and compared with the fitting results of traditional image methods: the determination coefficients between the fitting results of the two methods and the load samples were greater than 0.995;from the perspective of goodness of fit, for the upper threshold, the goodness of fit of the automatic threshold selection method was 8.7% smaller than that of the image method, and for the lower threshold, the goodness of fit of the automatic threshold selection method was 31.21% smaller than that of the image method. Finally, the PTO load data was extrapolated based on the POT model, and 1 time extrapolated load data was compared with the original load time history;when the time domain extrapolation factor was 131 and the cumulative frequency of PTO torque reached 106 times, the PTO torque load spectrum was obtained. The extrapolated load spectrum was verified based on statistical characteristics and rainflow counting method. The results showed that the extrapolated load spectrum had the same distribution law as the original load spectrum, which can realize the twoway extrapolation of the average amplitude on the basis of retaining the load characteristics, providing a theoretical basis for the compilation of the tractor PTO torque load spectrum.

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王禹,王玲,呂東曉,溫昌凱,王書茂.基于FDR閾值自動(dòng)選取的拖拉機(jī)PTO轉(zhuǎn)矩載荷譜外推[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2021,52(3):364-372. WANG Yu, WANG Ling, Lü Dongxiao, WEN Changkai, WANG Shumao. Extrapolation of Tractor PTO Torque Load Spectrum Based on Automated Threshold Selection with FDR[J]. Transactions of the Chinese Society for Agricultural Machinery,2021,52(3):364-372.

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  • 收稿日期:2020-11-09
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  • 在線發(fā)布日期: 2021-03-10
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