Article(id=1207658079085241116, tenantId=1146029695717560320, journalId=1205116883411038211, issueId=1207658076900008717, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1765857723255, onlineDateStr=2025-12-16, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1765857723255, onlineIssueDateStr=2025-12-16, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1765857723255, creator=13701087609, updateTime=1765857723255, updator=13701087609, issue=Issue{id=1207658076900008717, tenantId=1146029695717560320, journalId=1205116883411038211, year='2025', volume='23', issue='2', pageStart='189', pageEnd='376', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1765857722735, creator=13701087609, updateTime=1765862348176, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1207677477451833566, tenantId=1146029695717560320, journalId=1205116883411038211, issueId=1207658076900008717, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1207677477451833567, tenantId=1146029695717560320, journalId=1205116883411038211, issueId=1207658076900008717, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=351, endPage=355, ext={EN=ArticleExt(id=1207658079752135467, articleId=1207658079085241116, tenantId=1146029695717560320, journalId=1205116883411038211, language=EN, title=Identification and verification of half cosine basis function impact load of cantilever beam structure, columnId=1207658078934246168, journalTitle=Chinese Journal of Construction Machinery, columnName=Performance Mensuration, Experimentation and Fault Diagnosis, runingTitle=null, highlight=null, articleAbstract=

It is also necessary to consider the impact damage, the loosening of components and the vibration of the structure caused by the impact load factors in the study of the vibration state of the equipment. In this paper, an impact load identification algorithm based on half cosine function is designed. The proper interval is determined by genetic algorithm, and the dimensions of beams, thin plates and trusses are determined by data method. The numerical simulation results show that the error of SCFF fitting method is lower than that of Tikhonov and Chebyshev orthogonal polynomial fitting (COPF), and the SCFF recognition advantage is more obvious with the increase of noise. The peak error of less than 10% is obtained, and the minimum value is reached under the parameter optimization, which indicates that the parameter optimization has good applicability. The test results show that the low-frequency vibration state of the cantilever beam caused by the impact load is inferred by analyzing the spectrum data of the response signal, and the correction of the model by the first four modes meets the feasibility requirements. When the SCFF method is used for identification, it can form a good agreement with the actual load and obtain a smaller peak error.

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研究设备的振动状态还需考虑受工作冲击载荷因素导致的冲击损伤、部件松动以及自身结构振动的影响。为此设计了一种建立在半余弦函数基础上的冲击载荷识别算法,通过遗传算法确定合适区间,利用数据方法确定梁、薄板与桁架的尺寸,并进行实验验证。数值模拟结果表明:拟合冲击载荷(SCFF)方法误差均比Tikhonov、切比雪夫正交多项式拟合(COPF)更低,随着噪声增大SCFF识别优势更加明显。获得10%以内的峰值误差,在参数优化下达到最小值,表明参数优化具备良好的适用性。试验验证结果表明:通过分析响应信号频谱数据,推断该冲击载荷之后造成的悬臂梁发生低频振动状态,前四阶模态对模型实施修正满足可行性要求。采用SCFF方法识别时,可以跟实际载荷形成良好的吻合状态,可以识别获得更小峰值误差。

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王莹(1981—),女,讲师,硕士。E-mail:

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王莹(1981—),女,讲师,硕士。E-mail:

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Model parameters of cantilever beam

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项目阶段
1234
固有频率/Hz理论值3.1519.5454.6699.38
实验值3.6021.7962.23118.30
修正值3.3820.1460.02113.64
阻尼比/%4.403.652.952.45
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悬臂梁模型参数

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项目阶段
1234
固有频率/Hz理论值3.1519.5454.6699.38
实验值3.6021.7962.23118.30
修正值3.3820.1460.02113.64
阻尼比/%4.403.652.952.45
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Results of shock excitation identification by different methods

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项目TikhonovCOPFSCFF
相对误差/%36.9325.6824.54
峰值误差/%2.595.451.98
时间/s5856.483.15
), ArticleFig(id=1207748675548324594, tenantId=1146029695717560320, journalId=1205116883411038211, articleId=1207658079085241116, language=CN, label=表2, caption=

不同方法冲击激励识别结果

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项目TikhonovCOPFSCFF
相对误差/%36.9325.6824.54
峰值误差/%2.595.451.98
时间/s5856.483.15
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悬臂梁结构半余弦基函数冲击载荷识别及验证
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王莹 1 , 马晓力 2 , 王强 3
中国工程机械学报 | 性能检测、试验及故障诊断 2025,23(2): 351-355
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中国工程机械学报 | 性能检测、试验及故障诊断 2025, 23(2): 351-355
悬臂梁结构半余弦基函数冲击载荷识别及验证
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王莹1 , 马晓力2, 王强3
作者信息
  • 1.河南建筑职业技术学院 工程管理系,河南 郑州 450000
  • 2.郑州大学 水利与环境学院,河南 郑州 450000
  • 3.河南飞工重型机械制造有限公司,河南 郑州 450064
  • 王莹(1981—),女,讲师,硕士。E-mail:

Identification and verification of half cosine basis function impact load of cantilever beam structure
Ying WANG1 , Xiaoli MA2, Qiang WANG3
Affiliations
  • 1. Department of Engineering Management, Henan Vocational and Technical College of Architecture, Zhengzhou 450000, Henan, China
  • 2. College of Water Conservancy and Environment, Zhengzhou University, Zhengzhou 450000, Henan, China
  • 3. Henan Feigong Heavy Machinery Manufacturing Co., Ltd., Zhengzhou 450064, Henan, China
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研究设备的振动状态还需考虑受工作冲击载荷因素导致的冲击损伤、部件松动以及自身结构振动的影响。为此设计了一种建立在半余弦函数基础上的冲击载荷识别算法,通过遗传算法确定合适区间,利用数据方法确定梁、薄板与桁架的尺寸,并进行实验验证。数值模拟结果表明:拟合冲击载荷(SCFF)方法误差均比Tikhonov、切比雪夫正交多项式拟合(COPF)更低,随着噪声增大SCFF识别优势更加明显。获得10%以内的峰值误差,在参数优化下达到最小值,表明参数优化具备良好的适用性。试验验证结果表明:通过分析响应信号频谱数据,推断该冲击载荷之后造成的悬臂梁发生低频振动状态,前四阶模态对模型实施修正满足可行性要求。采用SCFF方法识别时,可以跟实际载荷形成良好的吻合状态,可以识别获得更小峰值误差。

冲击载荷  /  悬臂梁  /  载荷识别  /  函数拟合  /  遗传算法

It is also necessary to consider the impact damage, the loosening of components and the vibration of the structure caused by the impact load factors in the study of the vibration state of the equipment. In this paper, an impact load identification algorithm based on half cosine function is designed. The proper interval is determined by genetic algorithm, and the dimensions of beams, thin plates and trusses are determined by data method. The numerical simulation results show that the error of SCFF fitting method is lower than that of Tikhonov and Chebyshev orthogonal polynomial fitting (COPF), and the SCFF recognition advantage is more obvious with the increase of noise. The peak error of less than 10% is obtained, and the minimum value is reached under the parameter optimization, which indicates that the parameter optimization has good applicability. The test results show that the low-frequency vibration state of the cantilever beam caused by the impact load is inferred by analyzing the spectrum data of the response signal, and the correction of the model by the first four modes meets the feasibility requirements. When the SCFF method is used for identification, it can form a good agreement with the actual load and obtain a smaller peak error.

impact load  /  cantilever beam  /  load identification  /  function fitting  /  genetic algorithm
王莹, 马晓力, 王强. 悬臂梁结构半余弦基函数冲击载荷识别及验证. 中国工程机械学报, 2025 , 23 (2) : 351 -355 .
Ying WANG, Xiaoli MA, Qiang WANG. Identification and verification of half cosine basis function impact load of cantilever beam structure[J]. Chinese Journal of Construction Machinery, 2025 , 23 (2) : 351 -355 .
对设备振动状态进行工程研究时,还需考虑受工作冲击载荷作用时所产生的冲击破坏、连接结构松动与振动[1]。在分析结构强度、系统参数调节、健康状态监测时,对结构进行冲击载荷分析属于一项重要工作。在实际工程应用领域中,由于系统包含了复杂的结构以及受到实际技术条件的限制,并不能利用直接测试的方法确定冲击载荷,同时冲击载荷也呈现明显的稀疏性特征,存在持续时间短并会引起强烈冲击的特性,即使采集得到冲击载荷数据也不能通过力传感器达到精确测试的目的[2]
随着识别技术的快速进步,结构动力载荷的识别方法也在现代工程领域获得了快速推广,目前主要包括频域、时域这2种处理方式。其中,频域法是在建立线性假设的前提下建立的[3]。可通过线性函数来表示频域结构振动响应与载荷激励状态,得到结构振动响应结果后,再构建频响函数矩阵并以求逆算法确定载荷参数[4-5]。进行时域载荷识别时,需要构建载荷和振动系统传递函数之间的卷积函数,再以解卷积的方式确定时域离散结果[6],可以满足瞬态信号的处理要求,对冲击载荷进行识别时,通常以时域数据作为分析对象[7]。可以将冲击载荷的识别理解为一个逆问题求解的过程,产生较大的不适定性或病态性[8]。针对上述问题,学者们开发出冲击载荷识别的正则化算法。
还有一些学者为冲击载荷识别过程开发了函数拟合算法,例如,张方等[9]利用广义正交多项式构建基函数再对载荷进行拟合计算,该方法能够满足短样本冲击载荷的准确识别要求,但在处理复杂结构方面需要对大量数据进行计算。Gunawan等[10]通过二次样条函数来拟合冲击载荷,实现了理想的识别效果,在模态空间的基础上建立样条函数拟合算法,再通过Tikhonov正则化算法调整获得更优的不适定度。Hu等[11]建立了一种切比雪夫算式进行拟合处理,经过计算确定了冲击载荷,再以B样条函数来辨别冲击载荷,通过测试发现所得结果均比截断奇异值分解方法更优。随着数据处理技术的不断进步,许多新的算法也被应用于建立更高效的载荷识别算法。
由于现阶段建立在函数拟合基础上的载荷识别算法在识别稀疏性冲击载荷识别方面无法达到高精度性能,同时存在缺少良好的鲁棒性以及对实际参数的选择难度较大的情况。本文针对以上研究情况,设计了一种建立在半余弦函数基础上的冲击载荷识别算法,首先通过遗传算法确定合适的区间条件,之后利用数据仿真方法确定梁、薄板与桁架的尺寸数据,并进行了实验验证。
从时域层面分析,对于单输入单输出(SISO)的线性振动系统可以构建零时刻的位移响应和激励函数关系:
式中:yt)为时间历程t函数;τ为时间滞后量;ht)为单位脉冲响应,与振动模态存在直接关联;ft)为外部激励,属于载荷识别过程的一个未知参数。
通过上述表达式描述振动系统激励下的系统振动响应性能。在线性系统中包含多个载荷激励的情况下,响应点可以通过振源响应线性叠加的方式完成,在多输入多输出(MIMO)系统中也可将载荷识别过程表示成式(2)的形式。由于实际测试过程必然会产生误差,因此可以将包含噪声的控制算法表示为
式中:H为传递矩阵;f为外部施加激励;w为环境噪声。
对于包含大量条件的矩阵H,如果只采用求逆算法或通过最小二乘算法进行处理时,并不能获得理想的处理效果,如何完成不适定性情况的准确分析已经成为载荷识别过程的一项重要研究内容。针对不适定性可以通过Tikhonov正则化过程处理完成,使正则化识别结果满足非零条件。由于冲击载荷在本实验测试范围内保持非零的特点,形成了具有显著稀疏性的冲击载荷信号[12]。根据以上稀疏特征,本文设计了一种利用半余弦函数组成的基函数对冲击载荷进行拟合处理,构建得到的半余弦基函数为
式中:σ为一个半波宽参数,由余弦函数周期进行确定,区间在[A1A2];b为余弦函数作用时间,区间在[0,B]。
确定σibj后,构建半余弦基函数,之后采用半余弦基函数线性组合方法建立冲击载荷函数为
式中:aij为未知权系数。
再对式(4)实施离散化,经过简化计算得到构造矩阵G的矢量函数:
根据λ计算矢量A唯一解为
式中:λ为取值恒正状态的Tikhonov正则化参数,利用L曲线算法的形式计算获得正则化参数λ
首先建立冲击载荷矢量函数Q,然后以SCFF方法求解计算得到矩阵G,由此获得半余弦基函数。
梁材料的各项力学性能参数为:弹性模量为3.2 GPa,密度为1 310 kg/m3,长度为80 cm,测试截面为4 cm边长的方形区域。图1给出了该试样的示意结构。在悬臂梁的两端设置固定约束点,再将其均匀分成8个结点,各激励点与测量点的分布情况如图1所示。
结点8属于测量点,本次测试在ABAQUS软件上进行,通过仿真获得结点8在载荷作用下的无噪声位移参数。在Matlab函数awgn向位移信号中加入高斯白噪声作为环境噪声和测试误差的模拟信号。在50 dB与5 dB条件下,将载荷作用于测量区域,得到的位移数据如图2所示。
控制区间参数A1=0.1,B=0.2。为保证计算结果满足重复性要求,该实验共测试了50组样本的识别数据,并取均值作为最终结果,从而避免受个别信号影响造成识别结果的明显波动现象。利用Tikhonov、SCFF与COPF这3种方法进行载荷识别时得到的误差结果与噪声关系如图3所示。
SCFF方法通过稀疏基函数处理,使冲击载荷识别得到非零值,从而对非加载区间达到更高的识别精度。
图3给出了不同信噪比条件下采用3种方法形成的识别误差。在[5,50]dB条件下开展测试时,SCFF方法获得的相对误差和峰值误差均更小;提高噪声强度后,通过SCFF方法处理时表现出更强的识别优势。
设置区间参数A1=0.05,A2=0.1,B=0.15,可以控制识别相对误差不超过20%,并实现10%范围内的峰值误差,以上误差指标经过参数优化后均可以获得最小值。识别误差也存在明显波动,因此可以认为SCFF方法已成为一项必要的区间参数优化技术。
以悬臂梁结构作为测试对象,验证了SCFF方法处理性能。本次测试的悬臂梁材料类型为PS塑料,该材料的密度为1 221 kg/m3,弹性模量为3 GPa,泊松比等于0.3;试样长度为90 cm,宽为8 cm,厚为1 cm。
为系统测试结构安装了PSV-500激光测试仪。先在1~2 500 Hz频率区间内开展频带扫描得到系统固有频率。按照表1参数建立了优化模型,之后采集了以上数据。
受待识别冲击载荷影响时,测点位移时域变化如图5所示。
根据响应信号频谱参数测试结果可知,响应信号能量主要来自[0,50]Hz的频率区间,由此可以推断受到该冲击载荷作用时,悬臂梁将会出现低频振动的现象,并且形成了比第四阶固有模态频率更小的区间。因此,利用前四阶模态来调节模型参数可以达到可行性要求。不同算法冲击载荷识别所得结果如图6所示。
以参数修正模型构建的传递矩阵包含500的条件数,该参数取决于悬臂梁结构、激励点与测量点的位置。当条件数等于500时,表明冲击载荷识别过程并未产生明显病态特征,该结果也可以根据图6的识别情况得到验证。图5显示,虽然测量点位移响应信号中存在较高比例的噪声成分,采用Tikhonov方法对传递矩阵进行矩阵求逆计算时也未出现发散的情况[13]。而采用Tikhonov方法进行识别时则受到噪声因素的明显干扰,从而引起毛刺形态的识别结果,导致误差明显升高。当选择COPF、SCFF进行识别时,则可以跟实际载荷形成良好的吻合状态,各项识别参数见表2
根据表2测试结果可知,COPF方法需构建专门的基函数矩阵,从而需要占用比SCFF更长的处理时间。Tikhonov识别性能容易受噪声的严重影响,经识别测试得到的误差也明显高于其他处理方式。Tikhonov能够获得更小的相对误差,而相对误差则更大,这主要是因为以Tikhonov方法识别时会导致冲击噪声明显升高。COPF则可以获得更小的相对误差,SCFF方法则可以同时实现更小相对误差和峰值误差。
本文开展悬臂梁结构半余弦基函数冲击载荷识别及验证分析,得到如下结论:
(1)数值模拟结果表明,获得10%以内的峰值误差,在参数优化值条件下达到最小值,表明参数优化具备良好的适用性。
(2)试验验证结果表明,通过分析响应信号频谱数据,推断该冲击载荷之后造成悬臂梁发生低频振动状态,前四阶模态对模型实施修正满足可行性要求。采用SCFF方法识别时,可以跟实际载荷形成良好的吻合状态,可以识别获得更小峰值误差。
  • 国家自然科学基金资助项目(51478437)
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国家自然科学基金资助项目(51478437)
作者信息
    1.河南建筑职业技术学院 工程管理系,河南 郑州 450000
    2.郑州大学 水利与环境学院,河南 郑州 450000
    3.河南飞工重型机械制造有限公司,河南 郑州 450064
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
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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