Article(id=1311999993150534009, tenantId=1146029695717560320, journalId=1311385739772133495, issueId=1311999647825101743, articleNumber=null, orderNo=null, doi=10.6041/j.issn.1000-1298.2025.12.004, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1756656000000, receivedDateStr=2025-09-01, revisedDate=1758729600000, revisedDateStr=2025-09-25, acceptedDate=null, acceptedDateStr=null, onlineDate=1790734774147, onlineDateStr=2026-09-30, pubDate=1766592000000, pubDateStr=2025-12-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790734774147, onlineIssueDateStr=2026-09-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790734774147, creator=13701087609, updateTime=1790734774147, updator=13701087609, issue=Issue{id=1311999647825101743, tenantId=1146029695717560320, journalId=1311385739772133495, year='2025', volume='56', issue='12', pageStart='1', pageEnd='818', issueExtLink='null', onlineDate='null', pubDate='1766592000000', pubDateStr='2025-12-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1790734691815, creator='13701087609', updateTime=1790738411040, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1312015247465083866, tenantId=1146029695717560320, journalId=1311385739772133495, issueId=1311999647825101743, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1312015247465083867, tenantId=1146029695717560320, journalId=1311385739772133495, issueId=1311999647825101743, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=59, endPage=68, ext={EN=ArticleExt(id=1311999993611907450, articleId=1311999993150534009, tenantId=1146029695717560320, journalId=1311385739772133495, language=EN, title=Coupled Wheel Testing Method under Typical Driving Conditions of High-horsepower Tractors, columnId=1311999648647185329, journalTitle=Transactions of the Chinese Society for Agricultural Machinery, columnName=Special Column of Key Technologies for Intelligent Agricultural Power Equipment, runingTitle=null, highlight=null, articleAbstract=

Tractors are the most widely used power machinery in agricultural production. Conducting durability simulations of tractors under real-world operating conditions through laboratory test benches is of great significance for improving their reliability. A four-column wheel coupling test bench was utilized to explore the whole-machine durability testing methodology for tractors. An operational load acquisition system was established to collect load data under actual working conditions. Accelerated life testing was conducted by using three different identification methods: the time-domain damage retention identification method, the time-frequency analysis-based cumulative power spectral density identification method, and the wavelet transform-based envelope identification method. The effectiveness of these acceleration methods was validated through wheel coupling bench testing. The results indicated that, within the application scenarios and iterative approaches adopted, the time-frequency analysis-based cumulative power spectral density identification method demonstrated superior performance in signal compression, achieving a reduction ratio of 36.37%. The drive load spectra for the wheel coupling test bench were iteratively derived from both the original signal and the three accelerated signals. The loading results demonstrated that when the chassis and cab were selected as test targets, the damage reproduction tendency at designated measurement points was effectively achieved. Under the same test conditions but with different acceleration identification methods, the time-frequency analysis-based cumulative power spectral density identification method showed superior consistency in reproducing chassis damage, while the time-domain damage retention identification method performed better in replicating cab damage.

, authors=Zhiqiang GUO1, 2, Zhenghe SONG1, Lingfang XIANG3, Zihan YANG1, 2, Shengcao HUANG1, 2, Hengchu LIN2, Huawei HE2, authorsList=Zhiqiang GUO, Zhenghe SONG, Lingfang XIANG, Zihan YANG, Shengcao HUANG, Hengchu LIN, Huawei HE, authorCompany=null, correspAuthors=Zhenghe SONG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1311999998934479265, articleId=1311999993150534009, tenantId=1146029695717560320, journalId=1311385739772133495, language=CN, title=大马力拖拉机典型行驶工况轮耦合试验方法研究, columnId=1311999648798180275, journalTitle=农业机械学报, columnName=智能农业动力装备关键技术专栏, runingTitle=null, highlight=null, articleAbstract=

借助实验室台架实现拖拉机实际作业工况下的耐久性试验模拟,对拖拉机可靠性提升具有重要意义。本文借助四立柱轮耦合试验台,开展拖拉机整机耐久性试验方法研究。构建了载荷采集系统,获取了拖拉机实际作业工况载荷数据,采用时域损伤保留识别方法、基于时频分析的累积功率谱密度识别方法和基于小波变换的包络线识别方法进行加速编辑研究,并通过轮耦合台架试验进行加速效果评价。结果表明,在本文应用场景及迭代方法下,基于时频分析的累积功率谱密度识别方法在信号压缩比例方面表现最优,缩减比例达36.37%。基于原始信号及上述3种加速方法,通过加速编辑与迭代处理,获得轮耦合试验台驱动载荷谱,加载效果表明,以底盘、驾驶室为试验目标时均能满足对特定目标测点损伤复现的倾向性,同一试验目标、不同加速识别方法下,基于时频分析的累积功率谱密度识别法在底盘损伤复现方面一致性更佳,时域损伤保留识别法在驾驶室损伤复现方面一致性更佳。

, authors=郭志强1, 2, 宋正河1, 向玲芳3, 杨子涵1, 2, 黄胜操1, 2, 林恒矗2, 何华伟2, authorsList=郭志强, 宋正河, 向玲芳, 杨子涵, 黄胜操, 林恒矗, 何华伟, authorCompany=null, correspAuthors=宋正河, authorNote=

郭志强(1988—),男,博士生,主要从事农机装备载荷谱与耐久性试验方法研究,E-mail:

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宋正河(1973—),男,教授,博士生导师,主要从事农机装备试验验证方法与技术研究,E-mail:
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Layout diagram of load information acquisition system for power shift tractors

, figureFileSmall=null, figureFileBig=null, tableContent=
传感器类型安装数量采集通道编号安装位置描述部分测点传感器安装示意图
单向加速度传感器4ACC_FA_L
ACC_FA_R
ACC_RA_L
ACC_RA_R
前桥左侧
前桥右侧
后桥左侧
后桥右侧
3向加速度传感器3ACC_FA_M_X
ACC_FA_M_Y
ACC_FA_M_Z
ACC_RA_M_X
ACC_RA_M_Y
ACC_RA_M_Z
ACC_ROOF_X
ACC_ROOF_Y
ACCROOF_Z
前桥中部X方向
前桥中部Y方向
前桥中部Z方向
后桥中部X方向
后桥中部Y方向
后桥中部Z方向
驾驶室顶X方向
驾驶室顶Y方向
驾驶室顶Z方向
应变计(1/4桥路)9STR_FA_LA
STR_FA_RA
STR_FL_SL
STR_FR_SL
STR_CAB_LI
STR_CAB_LR
STR_CAB_RI
STR_CAB_RR
前桥左上方
前桥右上方
前桥左侧
前桥右侧
驾驶室左侧内
驾驶室左侧后
驾驶室右侧内
驾驶室右侧后
拉线位移传感器4DIS_FA_L
DIS_FA_R
DIS_RA_L
DIS_RA_R
前桥左侧
前桥右侧
后桥左侧
后桥右侧
), ArticleFig(id=1312000010921800182, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999993150534009, language=CN, label=表1, caption=

动力换挡拖拉机载荷信息采集系统布置示意图

, figureFileSmall=null, figureFileBig=null, tableContent=
传感器类型安装数量采集通道编号安装位置描述部分测点传感器安装示意图
单向加速度传感器4ACC_FA_L
ACC_FA_R
ACC_RA_L
ACC_RA_R
前桥左侧
前桥右侧
后桥左侧
后桥右侧
3向加速度传感器3ACC_FA_M_X
ACC_FA_M_Y
ACC_FA_M_Z
ACC_RA_M_X
ACC_RA_M_Y
ACC_RA_M_Z
ACC_ROOF_X
ACC_ROOF_Y
ACCROOF_Z
前桥中部X方向
前桥中部Y方向
前桥中部Z方向
后桥中部X方向
后桥中部Y方向
后桥中部Z方向
驾驶室顶X方向
驾驶室顶Y方向
驾驶室顶Z方向
应变计(1/4桥路)9STR_FA_LA
STR_FA_RA
STR_FL_SL
STR_FR_SL
STR_CAB_LI
STR_CAB_LR
STR_CAB_RI
STR_CAB_RR
前桥左上方
前桥右上方
前桥左侧
前桥右侧
驾驶室左侧内
驾驶室左侧后
驾驶室右侧内
驾驶室右侧后
拉线位移传感器4DIS_FA_L
DIS_FA_R
DIS_RA_L
DIS_RA_R
前桥左侧
前桥右侧
后桥左侧
后桥右侧
), ArticleFig(id=1312000011005686263, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999993150534009, language=EN, label=Tab. 2, caption=

Main parameter information of load information acquisition system

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设备类型品牌型号参数
电桥板层HBK-eDAQ-EBRG16通道;最大量程±10 V
高电平板层HBK-eDAQ-EHLS16通道;最大量程±74.9 V
车辆总线板层HBK-eDAQ-ECOM3通道高速CAN;1通道GPS
单向加速度计PCB-3711F1110G量程±10 g
3向加速度计PCB-3713F1150G量程±50 g
位移传感器Celesco-MT2A量程127 cm
单向应变计BE350-3AA工作温度-30~80℃
3向应变BE350-3CB工作温度-30~80℃
), ArticleFig(id=1312000011081183736, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999993150534009, language=CN, label=表2, caption=

载荷信息采集系统主要参数信息

, figureFileSmall=null, figureFileBig=null, tableContent=
设备类型品牌型号参数
电桥板层HBK-eDAQ-EBRG16通道;最大量程±10 V
高电平板层HBK-eDAQ-EHLS16通道;最大量程±74.9 V
车辆总线板层HBK-eDAQ-ECOM3通道高速CAN;1通道GPS
单向加速度计PCB-3711F1110G量程±10 g
3向加速度计PCB-3713F1150G量程±50 g
位移传感器Celesco-MT2A量程127 cm
单向应变计BE350-3AA工作温度-30~80℃
3向应变BE350-3CB工作温度-30~80℃
), ArticleFig(id=1312000011148292601, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999993150534009, language=EN, label=Tab. 3, caption=

Main parameters of wheel coupling test bench

, figureFileSmall=null, figureFileBig=null, tableContent=
参数数值
最大试验车总质量/kg30 000
单个位移作动器最大作用力/kN250
作动器行程/mm-150~150
作动器加载频率/Hz≤50
), ArticleFig(id=1312000011232178682, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999993150534009, language=CN, label=表3, caption=

轮耦合试验台主要参数

, figureFileSmall=null, figureFileBig=null, tableContent=
参数数值
最大试验车总质量/kg30 000
单个位移作动器最大作用力/kN250
作动器行程/mm-150~150
作动器加载频率/Hz≤50
), ArticleFig(id=1312000011320259067, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999993150534009, language=EN, label=Tab. 4, caption=

Compression ratio and damage retention ratio of acceleration signal

, figureFileSmall=null, figureFileBig=null, tableContent=
信号类型压缩比例损伤保留比例
加速信号Ⅰ28.2897.94
加速信号Ⅱ36.3798.02
加速信号Ⅲ11.2098.15
), ArticleFig(id=1312000011387367932, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999993150534009, language=CN, label=表4, caption=

加速信号压缩比例与损伤保留比例

, figureFileSmall=null, figureFileBig=null, tableContent=
信号类型压缩比例损伤保留比例
加速信号Ⅰ28.2897.94
加速信号Ⅱ36.3798.02
加速信号Ⅲ11.2098.15
), ArticleFig(id=1312000011458671101, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999993150534009, language=EN, label=Tab. 5, caption=

Statistical parameter error of acceleration signal

, figureFileSmall=null, figureFileBig=null, tableContent=
信号类型方差误差均方根峰度系数
加速信号Ⅰ22.22710.59710.355
加速信号Ⅱ30.26613.50114.910
加速信号Ⅲ6.9833.5133.151
), ArticleFig(id=1312000011521585662, tenantId=1146029695717560320, journalId=1311385739772133495, articleId=1311999993150534009, language=CN, label=表5, caption=

加速信号的统计参数误差

, figureFileSmall=null, figureFileBig=null, tableContent=
信号类型方差误差均方根峰度系数
加速信号Ⅰ22.22710.59710.355
加速信号Ⅱ30.26613.50114.910
加速信号Ⅲ6.9833.5133.151
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大马力拖拉机典型行驶工况轮耦合试验方法研究
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郭志强 1, 2 , 宋正河 1 , 向玲芳 3 , 杨子涵 1, 2 , 黄胜操 1, 2 , 林恒矗 2 , 何华伟 2
农业机械学报 | 智能农业动力装备关键技术专栏 2025,56(12): 59-68
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农业机械学报 |智能农业动力装备关键技术专栏 2025 , 56 (12) : 59 -68
大马力拖拉机典型行驶工况轮耦合试验方法研究
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郭志强(1988—),男,博士生,主要从事农机装备载荷谱与耐久性试验方法研究,E-mail:

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郭志强(1988—),男,博士生,主要从事农机装备载荷谱与耐久性试验方法研究,E-mail:

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郭志强1, 2 , 宋正河1 , 向玲芳3, 杨子涵1, 2, 黄胜操1, 2, 林恒矗2, 何华伟2
作者信息
  • 1.中国农业大学现代农业装备优化设计北京市重点实验室,北京 100083
  • 2.洛阳智能农业装备研究院有限公司,洛阳 471000
  • 3.第一拖拉机股份有限公司,洛阳 471000
通讯作者:
宋正河(1973—),男,教授,博士生导师,主要从事农机装备试验验证方法与技术研究,E-mail:
作者简介:

郭志强(1988—),男,博士生,主要从事农机装备载荷谱与耐久性试验方法研究,E-mail:

Coupled Wheel Testing Method under Typical Driving Conditions of High-horsepower Tractors
Zhiqiang GUO1, 2 , Zhenghe SONG1 , Lingfang XIANG3, Zihan YANG1, 2, Shengcao HUANG1, 2, Hengchu LIN2, Huawei HE2
Affiliations
  • 1.Beijing Key Laboratory of Optimized Design for Modern Agricultural Equipment, China Agricultural University, Beijing 100083, China
  • 2.Luoyang Intelligent Agricultural Equipment Research Institute Co., Ltd., Luoyang 471000, China
  • 3.First Tractor Co., Ltd., Luoyang 471000, China
出版时间: 2025-12-25 doi: 10.6041/j.issn.1000-1298.2025.12.004
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借助实验室台架实现拖拉机实际作业工况下的耐久性试验模拟,对拖拉机可靠性提升具有重要意义。本文借助四立柱轮耦合试验台,开展拖拉机整机耐久性试验方法研究。构建了载荷采集系统,获取了拖拉机实际作业工况载荷数据,采用时域损伤保留识别方法、基于时频分析的累积功率谱密度识别方法和基于小波变换的包络线识别方法进行加速编辑研究,并通过轮耦合台架试验进行加速效果评价。结果表明,在本文应用场景及迭代方法下,基于时频分析的累积功率谱密度识别方法在信号压缩比例方面表现最优,缩减比例达36.37%。基于原始信号及上述3种加速方法,通过加速编辑与迭代处理,获得轮耦合试验台驱动载荷谱,加载效果表明,以底盘、驾驶室为试验目标时均能满足对特定目标测点损伤复现的倾向性,同一试验目标、不同加速识别方法下,基于时频分析的累积功率谱密度识别法在底盘损伤复现方面一致性更佳,时域损伤保留识别法在驾驶室损伤复现方面一致性更佳。

拖拉机  /  轮耦合试验  /  耐久性  /  加速编辑

Tractors are the most widely used power machinery in agricultural production. Conducting durability simulations of tractors under real-world operating conditions through laboratory test benches is of great significance for improving their reliability. A four-column wheel coupling test bench was utilized to explore the whole-machine durability testing methodology for tractors. An operational load acquisition system was established to collect load data under actual working conditions. Accelerated life testing was conducted by using three different identification methods: the time-domain damage retention identification method, the time-frequency analysis-based cumulative power spectral density identification method, and the wavelet transform-based envelope identification method. The effectiveness of these acceleration methods was validated through wheel coupling bench testing. The results indicated that, within the application scenarios and iterative approaches adopted, the time-frequency analysis-based cumulative power spectral density identification method demonstrated superior performance in signal compression, achieving a reduction ratio of 36.37%. The drive load spectra for the wheel coupling test bench were iteratively derived from both the original signal and the three accelerated signals. The loading results demonstrated that when the chassis and cab were selected as test targets, the damage reproduction tendency at designated measurement points was effectively achieved. Under the same test conditions but with different acceleration identification methods, the time-frequency analysis-based cumulative power spectral density identification method showed superior consistency in reproducing chassis damage, while the time-domain damage retention identification method performed better in replicating cab damage.

tractor  /  wheel coupling test  /  durability  /  acceleration editor
郭志强, 宋正河, 向玲芳, 杨子涵, 黄胜操, 林恒矗, 何华伟. 大马力拖拉机典型行驶工况轮耦合试验方法研究. 农业机械学报, 2025 , 56 (12) : 59 -68 . DOI: 10.6041/j.issn.1000-1298.2025.12.004
Zhiqiang GUO, Zhenghe SONG, Lingfang XIANG, Zihan YANG, Shengcao HUANG, Hengchu LIN, Huawei HE. Coupled Wheel Testing Method under Typical Driving Conditions of High-horsepower Tractors[J]. Transactions of the Chinese Society for Agricultural Machinery, 2025 , 56 (12) : 59 -68 . DOI: 10.6041/j.issn.1000-1298.2025.12.004
目前我国已经成为世界农机制造和使用大国,拖拉机年产量和销量均居世界前列。但拖拉机等国产典型农机装备产品能够达到国际领先水平的不足5%,同质化严重、耐久性可靠性不足,使用者对国产拖拉机产品的满意度远低于同类型的国外产品[1-3]。农机装备质量可靠性已成为我国农机行业亟待克服的难点[4],提升农机装备质量可靠性是现阶段我国农机装备高质量发展的重点任务。
目前,国内农机企业主要依靠田间试验、用户田间作业示范应用来验证拖拉机质量可靠性,但容易受到季节、作业面积限制,且试验成本高、周期长、效率低。国外农机企业如John Deere等,20世纪80年代已开始农机作业载荷数据采集工作,构建形成农机装备全工况载荷谱,并使用实测载荷谱加载驱动四立柱车轮耦合道路模拟试验台[5]模拟复现田间实际作业工况以及道路行驶状态,验证和提高其农机产品质量可靠性及驾乘舒适性。
拖拉机实际作业时,存在轮胎-土壤互作力、犁具-土壤互作力以及传动系统零部件之间的相互激励,这些负载具有随机性、时变性、强非线性等特性,对拖拉机耐久可靠性形成巨大考验[6-7]。准确采集拖拉机实际作业载荷谱,利用动态载荷信号表征实际作业工况中的受载特征,为疲劳试验、强化试验及加速耐久性试验提供了基准载荷输入,对于开展拖拉机耐久性试验具有决定性作用。在汽车领域,融合载荷谱加速编辑方法的耐久性加速试验技术已发展较为成熟,面向整车和关键零部件均已形成较完备的应用案例[8]。汽车领域针对整车耐久性能评估可通过3种方式实车道路路试、室内台架仿真试验和有限元仿真计算实现[9]。
主要从时域和频域2个方面开展载荷谱编辑。时域方面,文献[10-13]主要从应变幅值、损伤参数、损伤保留识别、损伤累计等方法处理载荷谱,结果表明,各优化编辑后的载荷谱对应的耐久性测试寿命,均分布在理论预测寿命3倍误差带区间范围内。在频域方面,文献[14-20]通过引入小波、累积功率谱密度、模糊聚类、短时傅里叶变换的方法,对载荷谱进行加速编辑,结果表明,在实现每次循环疲劳损伤与原始损伤基本一致的前提下,大幅压缩加载时间。国内也开展了相关研究,但主要集中在汽车及其零部件领域。
文献[21-22]以乘用车或商用车为研究对象,借助轮耦合试验台验证了原始载荷谱、加速载荷谱在轮耦合试验台上的复现一致性。文献[23-25]以拖拉机为研究对象,提出了更多新的载荷谱加速、迭代方法,并对比验证了田间试验和台架试验的等效一致性。均证明了轮耦合试验台能够在室内准备模拟垂向激励,复现车辆实际作业工况对车辆底盘等关键零部件及整机耐久性考核评价。
为了在轮耦合试验台复现拖拉机典型行驶工况地面激励,本文搭建拖拉机载荷数据采集系统,基于田间实测载荷对不同工况下整机振动特征开展分析,提出拖拉机轮耦合台架试验方法,并对典型行驶工况下轮耦合试验复现精度及有效性开展验证,以期为农机装备开展室内耐久性试验验证提供参考。
以中国一拖东方红LF2204型动力换挡拖拉机为研究对象,整车质量约8.6 t,整车尺寸为5 489 mm×2 997 mm×3 330 mm,发动机标定功率162 kW,动力输出轴功率131.1 kW。
采用20个传感器,布置在前桥、后桥、驾驶室、液压悬挂等不同位置,形成不同工况下动力换挡拖拉机载荷数据采集系统,以采集应变、位移、加速度等载荷信息,试验测试系统布置方案如表1所示。加速度传感器采集的加速度信号能够有效反映拖拉机在受载荷激励时振动、冲击和共振等特性的动力学响应。由于轮耦合试验台仅具备生成垂向激励的结构特点,因此,单向加速度传感器按照整机垂向方向安装,此外,3向加速度传感器按照整机前进方向为空间坐标轴X轴正方向,整机前进方向左侧为Y轴正方向,整机垂向为Z轴正方向规则进行安装布置。
应变计可有效获取被测区域平均应变,并验证轮耦合试验台与实际工况的损伤一致性。因此,在前桥、后桥、驾驶室、液压悬挂系统特定结构处布置应变计。拉线位移传感器主要用于监测悬架垂向运动,评估拖拉机在作业时的动态响应,将拉线位移传感器布置在拖拉机前后桥靠近轮边处,用来记录路面激励造成的前桥、后桥垂向摆动情况,且在轮耦合台架试验中与加速度通道一同作为目标迭代通道。构建的数据采集系统主要参数信息如表2所示。
基于用户实际作业场景的试验条件限制并结合轮耦合台架试验要求,将试验工况设置为空车无悬挂、空车田间顺垄行驶、空车田间跨垄行驶。按照拖拉机工况划分进行载荷数据采集,如图1所示。不同工况下,拖拉机受到的激励形式存在差异,本文重点研究空车无悬挂情况下载荷特性与加速编辑方法。基于构建的载荷信息采集系统,分别采集空车无悬挂、空车田间顺垄行驶、空车田间跨垄行驶的载荷,并截取完整工况信号中连续的360 s信号,进行振动特性及关键部位应变造成的损伤情况分析。
采用加速度均方根(RMS)评价分析不同测点振动水平[26],拖拉机空车无悬挂下13个加速度信号均方根雷达图如图2所示。由图2可知拖拉机两侧轮边加速度均方根较为接近,这是因为拖拉机左右两边结构对称;前桥加速度均方根大于后桥,这是由于试验用拖拉机前桥所处位置容易受到发动机激振影响,且前桥与车身整体结构铰接,存在左右浮动现象,而后桥与驾驶室、车身整体属于刚性连接,不存在车轴单一自由度,因此,前桥相较于后桥振动激励更加敏感。前桥、后桥中部3向加速度中X、Y方向均方根较小,对垂向激励不敏感,Z方向可反映垂向激励。驾驶室顶部3向加速度由于驾驶室结构自由度限制不固定,导致3个方向均会随着垂向激励产生一定大小的加速度。空车无悬挂工况下,各测点均方根由小到大为:土路行驶、田间顺垄行驶、田间跨垄行驶,即振动强度与路面起伏程度相关,跨垄行驶工况下整机振动强度最为剧烈。故而选取此工况作为后续研究基础。
轮耦合试验台现已被广泛应用于车辆工程领域耐久性试验研究,其原理在于通过控制4个独立作动器垂向位移实现对车辆所受路面垂向激励的动态模拟。结合拖拉机空车工况下振动特征分析结果,选择MTS-320型道路模拟试验台架开展轮耦合试验,如图3所示。
试验台主要性能参数如表3所示。轮耦合试验时,基于试验目的、载荷特性及关键零部件的损伤贡献度选择目标迭代通道,在车辆领域,常常选取加速度和位移信号作为目标迭代通道,原因在于加速度信号能够直接反映拖拉机结构动态响应,特别是在复杂工况下的振动情况;位移信号能够有效反映轮胎与地面接触状态及底盘相对于机架的相对变化,以及弥补加速度传感器在低频区域灵敏度误差增加的问题。
将采集到的作业载荷信号进行迭代,利用传递函数逆向求解台架作动器驱动信号,加载到轮耦合试验台,从而在室内台架上模拟复现拖拉机空载作业工况。试验迭代流程如图4所示。
研究了原始信号,并采用基于时域损伤保留识别法[27](加速方法Ⅰ)、基于时频分析的累积功率谱密度损伤识别法[28](加速方法Ⅱ,采用Morlet连续小波变换,尺度参数设置为1~256)、基于小波变换的包络线损伤识别法[29](加速方法Ⅲ,小波分解层数设置为12,阈值步数设置为100)对拖拉机空车无悬挂的田间跨垄行驶工况载荷进行加速编辑处理。将加速信号相对于原始信号损伤保留量阈值设为98%,即加速信号损伤量接近原始信号的98%视为达到加速标准。
原始信号、加速信号Ⅰ、加速信号Ⅱ和加速信号Ⅲ时域对比如图5所示。将原始信号中被删减部分信号时长与原始信号时长比值定义为加速信号压缩比例[30-31],分析可得各加速信号压缩比例与损伤保留量如表4所示。由图5和表4可知,从信号压缩效果来看,加速信号Ⅱ在相同的伪损伤保留比例条件下得到的加速信号时间更短,压缩比例达到36.37%;加速信号Ⅰ次之,压缩比例达28.28%;而加速信号Ⅲ在信号压缩效果上的表现最差,仅有11.20%。
载荷统计参数是表征载荷相似性的参数。方差反映信号波动幅度特性;均方根可以反映载荷信号中的平均能量;峰度系数为统计四阶矩,表征概率密度分布曲线在平均值处峰值的特征数。利用方差、均方根和峰度系数对加速编辑前后的信号统计特征差异进行分析,能评估不同载荷谱加速编辑方法效果和信号分布相似度,保证信号编辑前后平均能量和统计分布一致。原始信号与3种加速信号统计参数误差如表5所示。
由表5可知,加速信号Ⅲ统计参数相对于加速信号Ⅰ和Ⅱ与原始信号更为接近,误差较小,均保持在7%以内;加速信号Ⅱ统计参数误差(包括方差、均方根、峰度系数)在3种加速信号中均为最大,其中方差误差高达30.266%;加速信号Ⅰ统计参数误差处于加速信号Ⅱ与Ⅲ之间,其中均方根误差与峰度系数误差约10%,但方差误差达到22.227%。
载荷统计参数可表征载荷雨流计数,可揭示加速编辑是否有效剔除了低幅值、低损伤的微小循环,同时保留或强化了高幅值、高损伤的主循环。对各加速信号和原始信号进行雨流计数,结果如图6所示。从图6可以看出,各加速信号与原始信号的均幅值统计分布特征具有相似性,3种载荷谱加速方法均对低幅值载荷循环进行了不同程度删减,其中,加速信号Ⅰ和加速信号Ⅱ对于均值为0附近的低幅值载荷循环的删减效果较为明显,加速信号Ⅲ相较于前两者对低幅值载荷循环删减效果较弱。
拖拉机部件功率谱密度从能量分布角度揭示了载荷对部件作用的影响。通过对比不同信号功率谱密度,可以分析载荷谱在加速处理前后能量保留情况。在此基础上,计算了原始信号及3种加速信号功率谱密度结果如图7所示。由图7可知,3种加速信号与原始信号频带分布在0~5 Hz,且峰值能量均在2 Hz左右。在低频0~5 Hz范围内,加速信号Ⅰ与Ⅲ与原始信号具有基本一致的变化趋势,但在幅值上略有增加,说明其具有与原始信号相似能量分布;加速信号Ⅱ变化趋势与原始信号具有较大差距,其峰值能量分布在2.5 Hz左右,这与原始信号产生了较大偏差。从功率谱密度的一致性分析可得,加速信号Ⅰ与Ⅲ功率谱密度一致性效果优于加速信号Ⅱ。
在损伤保留比例98%条件下,基于时频分析的累积功率谱密度损伤识别法对信号缩减比例最佳,但在统计参数、功率谱密度等方面较其他2种方法相比略有劣势;基于小波变换的包络线损伤识别法针对小幅值平稳载荷的识别效果不佳,加速信号时间压缩比例较低,但在统计参数、功率谱密度等方面与原始信号一致性最高;时域损伤保留识别法处于上述2种方法之间,在保持较好时间压缩比例前提下,时域与频域特征值与原始信号保持了一定的一致性。综上所述,3种方法在不同方面各有优劣,需结合台架试验目标和加载效果进一步开展适用性评估。
为探索拖拉机不同作业工况下田间试验在轮耦合试验台架上的复现情况及两者之间的载荷差异,研究了原始信号以及使用加速方法Ⅰ、加速方法Ⅱ、加速方法Ⅲ分别编辑的加速信号,作为轮耦合试验台驱动信号,进行原始信号、加速信号的迭代及对应的结果分析。
借助迭代软件结合频响函数反求作动器初始位移驱动信号,经历反复迭代后,得到车身传感器实际响应信号,计算每次迭代后目标迭代通道均方根,对比与目标响应信号均方根误差。一般在目标迭代通道均方根误差在10%以下即可满足迭代要求[32],故设置均方根误差持续收敛至最小时即停止迭代(即若再进行一次迭代,均方根误差即发散),生成符合本试验要求的作动器驱动谱;将满足试验要求的作动器驱动谱导入台架控制系统,得到作动器输入激励下拖拉机整车传感器实际响应信号。经过对原始信号迭代,得到均方根误差曲线。由图8可知,随着迭代次数增加,迭代通道均方根误差逐渐降低,迭代7次后,均方根误差降低较小,几乎与第6次持平,因此不再进行第8次迭代。从图8可以看到,各通道迭代均方根误差均低至25%以下,最低可达到7%,可以得出原始信号的目标迭代通道实际响应信号复现情况良好,与目标响应信号吻合度较高。
为了复现拖拉机在实际作业过程中结构件损伤情况进行了轮耦合试验,在轮耦合试验迭代完成后,需要对应变信号进行伪损伤计算,再与田间实际作业损伤值进行对比,原始信号迭代前后应变伪损伤误差如图9所示。由图9可知,原始信号迭代实际响应信号与目标响应信号应变伪损伤误差在不同通道具有不同的复现精准度。迭代后以底盘为试验目标(侧重于复现底盘前桥结构件损伤)应变信号的损伤复现程度优于以驾驶室为试验目标(侧重于复现驾驶室结构件损伤)的应变信号,其中前桥应变伪损伤误差最小,分别可达14.99%和15.98%;后桥应变伪损伤误差相对较高,分别为50.29%和51.3%;驾驶室结构件4个应变信号中对垂向激励敏感测点驾驶室钢梁左右侧内表面应变伪损伤误差相对更高,表明迭代后驾驶室损伤复现效果不佳。
使用加速方法Ⅱ和加速方法Ⅰ,对原始信号进行加速编辑,得到加速信号,通过轮耦合迭代试验,得到目标迭代通道均方根误差。采用加速方法Ⅱ对驾驶室、底盘均方根误差如图10所示。在2种试验目标迭代过程中,只有少数目标迭代通道通过迭代次数增加均方根误差降低到20%以下,大部分通道均方根误差降低程度不高,说明基于时频分析的累积功率谱密度识别法在加速过程中删减时信号和保留信号拼接部分过渡不佳,导致信号出现突变,在迭代前虽然通过专用软件对其进行了滤波平滑处理,但实际迭代过程中在突变处作动器反应不及时,故导致目标迭代通道均方根误差较大。
选取不同试验目标下通过时域损伤保留识别法即加速方法Ⅰ进行原始信号加速,将加速信号进行迭代,得到目标迭代通道均方根误差如图11所示。不同试验目标下采取时域损伤保留识别法进行加速处理的加速信号的迭代效果均优于基于时频分析的累积功率谱密度识别法,其中以底盘为试验目标时不同目标迭代通道均方根误差均在25%之下,最低可达8.48%;相比之下,以驾驶室为试验目标的加速信号迭代效果相对较差,均方根整体偏高,最低只达到18.04%。也从侧面验证了评价模型针对性加速效果,当驾驶室作为试验目标时,主要复现驾驶室结构件振动情况,而轮耦合台架试验只能以轮边加速度和位移信号作为目标迭代加速通道,故以驾驶室为试验目标时的加速信号迭代效果劣于以底盘为试验目标时加速信号迭代效果。
由原始信号迭代结果可知,经过轮耦合台架迭代后的加速信号,一般在对垂向激励敏感的应变测点会有较好的损伤复现效果,故对加速信号应变伪损伤分析只挑选前桥左右侧上表面及驾驶室钢梁左右侧内表面进行分析,将上述加速信号迭代结果并计算应变通道的伪损伤误差与原始信号迭代响应对比,如图12所示。从图12可以看到,在以底盘为试验目标时,采用加速方法Ⅰ和Ⅱ进行加速后,前桥左右侧上方应变测点进行迭代后伪损伤误差分别为1.46%、9.83%和11.29%、12.68%,均小于以驾驶室为目标时两测点伪损伤误差16.01%、31.38%和20.00%、39.71%。在以驾驶室为试验目标时,驾驶室钢梁左右侧内表面应变测点进行迭代后伪损伤误差分别为63.74%、60.48%和57.29%、59.32%,均小于以底盘为目标时两测点伪损伤误差75.00%、67.27%和75.60%、69.75%。
由图12可知,当以底盘为试验目标时,无论采用何种加速方法,作为直接反映底盘垂向激励的两个应变测点,均呈伪损伤误差小于原始信号迭代响应的应变伪损伤误差,表明通过评价模型处理后加速信号经过实际轮耦合台架迭代后能够实现更加贴近原始信号效果,证实了提出的评价模型具有适应轮耦合台架试验能力;当以驾驶室为试验目标时,仅有采用加速方法Ⅰ编辑的驾驶室钢梁左侧内表面应变经迭代后伪损伤误差小于原始信号迭代响应的应变伪损伤误差,其余测点均没有此趋势,这可能是由于驾驶室在结构上与底盘连接处由弹簧垫构成,导致垂向激励从底盘传导至驾驶室时产生了非线性传递,且本次轮耦合台架试验目标迭代通道选取均在底盘结构件上,导致对驾驶室测点复现准确性降低。
综上所述,无论何种加速方法,评价模型均能实现以底盘、驾驶室为试验目标时目标测点结构件损伤的复现倾向性,即验证了评价模型对不同试验目标的针对性加速效果。
利用加速方法Ⅲ对底盘加速信号进行轮耦合迭代试验,得到各通道均方根误差如图13所示。
将图13和图10a、11a进行对比,可以看到3种加速识别方法加速编辑后的加速信号中,时域损伤保留识别法与基于小波变换的包络线损伤识别法的迭代效果较优,所有目标迭代通道均低于28%,其中时域损伤保留识别法均低于25%,但基于时频分析的累积功率谱密度识别法迭代效果较差,其中6个目标迭代通道误差仅低于38%,剩余2个通道仅在第5次迭代时达到55%左右。
计算目标应变复现通道伪损伤误差,结果如图14所示。在3种方法对比中,前桥左右侧上方应变测点伪损伤误差由小到大为加速方法Ⅱ、加速方法Ⅰ、加速方法Ⅲ,虽采用加速方法Ⅱ进行迭代的加速信号实际迭代效果较差,但其伪损伤误差却最为接近原始信号,且还低于原始信号迭代响应;驾驶室钢梁左右侧内表面应变测点伪损伤误差由小到大为加速方法Ⅰ、加速方法Ⅲ、加速方法Ⅱ,这与前桥应变测点有区别,可能是受迭代方法影响,迭代通道选取会对不同测点的损伤复现一致性造成较大影响,此外,由于前桥应变可以直接反映轮耦合台架作动器对底盘的垂向激励,而驾驶室因为弹簧垫连接底盘的原因无法线性反映直接作用在轮胎上的垂向激励,导致加速方法Ⅰ更适合驾驶室损伤复现。在应用场景、迭代方法及通道选取下,加速方法Ⅱ在底盘的损伤复现方面一致性较好;加速方法Ⅰ在驾驶室损伤复现方面一致性较好。
(1)以开展拖拉机整机轮耦合试验为目标,构建了拖拉机载荷信号采集系统,使用均方根对采集的原始信号进行载荷振动特性分析,结果表明田间跨垄行驶时振动强度最为显著,具有最高的潜在损伤风险。
(2)在损伤保留比例98%条件下,基于时频分析的累积功率谱密度识别方法信号压缩率最优,达到36.37%;基于小波变换的包络线识别方法在处理小幅值平稳载荷时识别效果较弱,信号压缩率较低仅11.20%;时域损伤保留识别方法则在信号压缩率为28.28%,但在时域及频域特征参数方面,3种识别方法各有优势。
(3)轮耦合台架在重现垂向激励敏感测点处损伤方面表现优异,以底盘为试验目标时,迭代效果优于以驾驶室为试验目标,均方根误差最低为8.48%;驾驶室最低为18.04%。以底盘、驾驶室为试验目标时3种加速方法均能满足对特定目标点结构件损伤复现的倾向性。基于时频分析的累积功率谱密度识别法在底盘损伤复现方面一致性更佳,时域损伤保留识别法在驾驶室的损伤复现方面一致性更佳。
  • 国家自然科学基金项目(52472462)
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2025年第56卷第12期
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doi: 10.6041/j.issn.1000-1298.2025.12.004
  • 接收时间:2025-09-01
  • 首发时间:2026-09-30
  • 出版时间:2025-12-25
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  • 收稿日期:2025-09-01
  • 修回日期:2025-09-25
基金
国家自然科学基金项目(52472462)
作者信息
    1.中国农业大学现代农业装备优化设计北京市重点实验室,北京 100083
    2.洛阳智能农业装备研究院有限公司,洛阳 471000
    3.第一拖拉机股份有限公司,洛阳 471000

通讯作者:

宋正河(1973—),男,教授,博士生导师,主要从事农机装备试验验证方法与技术研究,E-mail:
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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