WO2018188432A1 - 一种工程结构模态识别的模型定阶方法 - Google Patents
一种工程结构模态识别的模型定阶方法 Download PDFInfo
- Publication number
- WO2018188432A1 WO2018188432A1 PCT/CN2018/078134 CN2018078134W WO2018188432A1 WO 2018188432 A1 WO2018188432 A1 WO 2018188432A1 CN 2018078134 W CN2018078134 W CN 2018078134W WO 2018188432 A1 WO2018188432 A1 WO 2018188432A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- order
- response
- modal
- mrci
- model
- Prior art date
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M5/00—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings
- G01M5/0066—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings by exciting or detecting vibration or acceleration
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/04—Analysing solids
- G01N29/12—Analysing solids by measuring frequency or resonance of acoustic waves
Definitions
- the invention belongs to the technical field of engineering structure monitoring data analysis, and relates to a model ordering method for engineering structure modal identification.
- the change of the modal parameters of the engineering structure is due to the change of its own characteristics, so the performance evaluation of the structure can be performed by the identified modal parameters.
- the input excitation is difficult to measure, so the modal parameter identification method based on structural response (working modal analysis) is more practical.
- the current commonly used method is the time domain based subspace method. .
- KJAstron et al. proposed a method of first determining the order of time series based on the sum of squared residuals and then using the F criterion to check the table according to the confidence level.
- Japanese statistician H. Akaike et al. Weighing the applicability and complexity of the model Akaike's information index fixed-order criteria, the order of the model is determined by minimization; Zhang Wenquan et al.
- the invention aims to provide a new model ordering method for modal identification of engineering structures, and solve the model quasi-determination order problem in the modal recognition process.
- the technical solution of the invention deduces a model grading method in modal identification, which is characterized in that a Hankel matrix is formed according to an impulse response signal with environmental interference to an engineering structure, and then a singular value decomposition is performed on the Hankel matrix, and the decomposition is performed.
- the rank of the obtained singular value matrix is taken as the hypothetical order of the model, and the frequency of the structure is obtained.
- the modal response of the measured response is decomposed according to the mode superposition method by the obtained frequency, and the modal response corresponding to each frequency is obtained.
- the modal response of the obtained modal response in each degree of freedom is the root mean square, and the metric of the single degree of freedom response under the modal response is obtained, and then the root mean square values of the respective degrees of freedom are added to obtain
- the measure of the magnitude of the contribution of the modal response is the Modal Response Contribution Index (MRCI); the order is used as the abscissa, the MRCI is used as the ordinate, and the relationship between the order and the MRCI is plotted.
- MRCI Modal Response Contribution Index
- a model ordering method for engineering structure modal identification the steps are as follows:
- the first step is to collect the impulse response and post-processing of the engineering structure.
- H(k) is as follows:
- ⁇ is a singular value matrix
- U and V are ⁇ arrays
- the second step is to obtain the mode shape matrix.
- the rank of the singular value matrix ⁇ is taken as the order of the structural model, the rank is cH, the eigenvalue ⁇ j is obtained by the feature system implementation method, and the N eigenvalues are selected from the obtained cH eigenvalues ⁇ j for analysis.
- the relationship between the modal response and the structural response is obtained as the modal shape matrix ⁇ j under the measured data:
- the third step further obtains the time-history response of the jth-order mode and the root mean square response of the time-course response of the jth-order mode
- the fourth step is to get the model order.
- r is the number of degrees of freedom
- the normalized MRCI is the ordinate, that is, the MRCI value is divided by the MRCI maximum value, and the relationship diagram between the two is drawn; when the MRCI value ratio of the two adjacent orders is found to be the largest when the relationship graph is found.
- the corresponding order is the model order.
- the invention has the beneficial effects that the accurate order of the model can be obtained by using the measured data and the modal response contribution indicator.
- the acquisition of this order is simple, and no iterative calculation or iterative calculation is needed.
- the exact order of acquisition helps to obtain accurate structural modal parameters.
- Figure 1 is a plot of model order and MRCI values.
- the mass of each layer is 1.1 ⁇ 10 6 kg
- the inter-layer stiffness is 862.07 ⁇ 10 6 N/m
- the damping is Rayleigh damping
- the Rayleigh damping coefficient is 5% from the first two orders.
- the damping ratio is determined
- the excitation form is pulse excitation
- the noise level is 20% of the actual signal variance
- the response signal is the displacement of each layer of the structure.
- vector y is the measured signal with noise interference
- ⁇ is a singular value matrix
- U and V are ⁇ arrays
- ⁇ dimension is 130 ⁇ 130.
- the eigenvalue ⁇ j is obtained by the feature system implementation method.
- ⁇ j is a vector of 8 ⁇ 1.
- the normalized MRCI value is the ordinate, that is, the MRCI value divided by the MRCI maximum value is used to plot the relationship between the two, as shown in FIG. It can be seen from the figure that the MRCI value ratio is the largest between the 16th and 17th orders, so the 16th order is selected as the model order.
- the modal parameters appear in the form of conjugate pairs.
- the example is a structure of 8 degrees of freedom, and the corresponding real model order is 16th order. It can be seen that the order of the model can be accurately identified by the method of the present invention.
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Computational Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Theoretical Computer Science (AREA)
- Pure & Applied Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Algebra (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- Computing Systems (AREA)
- Aviation & Aerospace Engineering (AREA)
- Complex Calculations (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
本发明属于工程结构监测数据分析技术领域,提供了一种工程结构模态识别的模型定阶方法。本发明通过特征实现算法初步求解结构各阶频率,然后将各阶频率对应的模态响应分解出来,再求解模态响应的均方根值,最后将模态响应中各个自由度的均方根值相加得到模态响应贡献量指标MRCI,做出阶次与MRCI的关系图,找出两个相邻阶次的MRCI值比值最大时对应的阶次,即为模型的准确阶次。该模型阶次可作为特征实现算法中的奇异值矩阵的截断阶次,可用于准确识别出结构的模态参数。
Description
本发明属于工程结构监测数据分析技术领域,涉及工程结构模态识别的模型定阶方法。
工程结构模态参数的变化源于其自身特性的变化,因此可通过识别出的模态参数,进行结构的性能评估。对于实际工程而言,因其输入激励难以测定,所以所以仅基于结构响应的模态参数识别方法(工作模态分析)就显得更为实用,目前较为常用的方法为基于时域的子空间方法。
采用子空间识别方法进行模态识别时,需要进行模型定阶,不准确的阶次会使识别出的模态带有很大的误差。针对模态识别中模型定阶方法,已有许多学者开展了研究。K.J.Astron等在提出了首先根据残差平方和定阶,然后采用F准则按置信水平进行查表、计算确定时序模型阶次的方法;日本统计学家H.Akaike等从信息论出发,提出了综合权衡模型适用性与复杂性的Akaike's信息指标定阶准则,通过极小化来确定模型的阶次;张文泉等从F检验出发,以AIC为基础,推导出F检验临界值,并将其应用于自回归模型(Auto-Regressive,简称AR)和自回归滑动平均模型(Auto-Regressive and Moving Average Model,简称ARMA)阶次的确定;丁韬等把状态空间模型转化为能观测性规范性,导出系数输出相关函数所满足的线性回归方程,通过观察数据乘积矩阵行列式随其维数的变化情况,进行可确定出***的阶次;杨文献等分析了信号信噪比与奇异熵间的内在联系,提出了一种根据奇异熵增量渐进特性来对结构阶次进行确定的有效方法。然而,这些方法对于许多工程结构,有时难以通过明显的临界值,准确区分判定出模型的阶次,这会导致模态参数识别不准确,从而造成 工程结构性能评估的也不准确。因此,如何在模态参数识别过程中对进行准确地模型定阶,是十分必要的。
发明内容
本发明旨在提供一种新的工程结构模态识别的模型定阶方法,解决模态识别过程中的模型准确定阶问题。
本发明的技术方案:推导一种模态识别中的模型定阶方法,其特点是依据对工程结构带有环境干扰的脉冲响应信号,形成Hankel矩阵,然后对Hankel矩阵进行奇异值分解,将分解得到的奇异值矩阵的秩作为模型的假设阶次,求出结构的频率;接着,通过求出的频率,将实测响应根据振型叠加法进行模态响应分解,得到各个频率对应的模态响应;将得到的模态响应各个自由度上的时程响应做均方根,得到了该模态响应下单个自由度响应大小的度量,再将各个自由度的均方根值相加,可得到该阶模态响应贡献量大小的度量,即模态贡献量指标(Modal Response Contribution Index,简称MRCI);以阶次作为横坐标,MRCI作为纵坐标,绘出阶次与MRCI的关系图,找出两个相邻阶次的MRCI值比值最大时对应的阶次,作为模型的阶次,即可完成模型定阶。将该阶次作为特征实现算法中奇异值矩阵的截断阶次,进行模态参数识别,可得到准确的模态模态参数。
一种工程结构模态识别的模型定阶方法,步骤如下:
第一步,采集工程结构的脉冲响应及后处理
首先实时采集工程结构的脉冲响应y
k,再对工程结构的实测脉冲响应y
k建立Hankel矩阵H(k-1)和H(k),H(k)如下:
式中:向量y
k为实测信号;k+i表示第k+i时刻;k到k+rH+cH-2为选择的实测时程数据点个数;将k-1代替k代入到上式得到H(k-1);
然后,对Hankel矩阵H(k-1)进行奇异值分解:
H(k-1)=UΓ
2V
T
式中:Γ为奇异值矩阵;U和V为酉阵;
第二步,获得模态振型矩阵
以奇异值矩阵Γ的秩作为结构模型阶次,设秩为cH,通过特征***实现方法求出特征值λ
j;从求出的cH个特征值λ
j中选取N个特征值进行分析,建立模态响应与结构响应的关系式,得到实测数据下的模态振型矩阵Φ
j:
式中:符号“
+”表示矩阵的逆或伪逆;p为第p个时刻,即p=rH+cH-1;
第三步,进一步获取第j阶模态的时程响应和第j阶模态的时程响应均方根值
第j阶模态的时程响应Y
p,j:
第j阶模态的时程响应均方根值:
第四步,得到模型阶次
将第j阶模态各个自由度时程响应的均方根值相加,得到第j阶模态响应贡献指标MRCI:
式中:r为自由度数;
以阶次为横坐标,标准化的MRCI为纵坐标,即MRCI值除以MRCI最大值,绘出两者间的关系图;从关系图中找出两个相邻阶次的MRCI值比值最大时对应的阶次,即为模型阶次。
本发明的有益效果:利用实测数据,通过模态响应贡献量指标,可获取模型的准确阶次。该阶次的获取途径简单,不用进行迭代计算或反复计算。而且获取的准确阶次有助于得到准确结构模态参数。
图1是模型阶次与MRCI值关系图。
以下结合技术方案,进一步阐明本发明的实施方式。
以一个8层框架结构为例,设其各层质量为1.1×10
6kg,层间刚度均为862.07×10
6N/m,阻尼采用瑞利阻尼,瑞利阻尼系数由前两阶5%的阻尼比来确定,激励形式为脉冲激励,噪声水平为实际信号方差的20%,响应信号为结构的每层位移。方法具体实施方式如下:
(1)令rH=150,cH=130;并令k=1,选取第1时刻到第279时刻的实测信号y,组成Hankel矩阵H(k-1)和H(k),如下形式:
式中:向量y为带有噪声干扰的实测信号;角标k+i表示第k+i时刻,其中i=0...rH+cH+k-2;k到rH+cH+k-2为选择的实测时程数据点个数。
(2)对Hankel矩阵H(k-1)进行奇异值分解:
H(k-1)=UΓ
2V
T
式中:Γ为奇异值矩阵;U和V为酉阵;Γ维数为130×130。
(3)以奇异值矩阵Γ的秩130作为结构模型阶次,通过特征***实现方法求出特征值λ
j。
(4)从求出的130个特征值λ
j中选取40个特征值进行分析,建立模态响应与结构响应的关系式,求解实测数据下的模态振型矩阵Φ
j:
(5)求出第j阶模态的时程响应Y
279,j,其中j=1…40:
(6)求出第j阶模态的时程响应均方根值:
式中:ε
j为8×1的向量。
(7)将第j阶模态各个自由度时程响应的均方根值相加,得到第j阶模态响应贡献指标(MRCI):
(8)以阶次为横坐标,标准化的MRCI值为纵坐标,即MRCI值除以MRCI最大值绘出两者之间的关系图,如附图1所示。由图中可见在16阶与17阶之间MRCI值比值最大,因此选取16阶为模型阶次。
由于采用状态空间模型进行分析,因此模态参数以共轭对的形式出现,算例为8个自由度的结构,所对应的真实模型阶次为16阶。由此可见,通过本发明的方法可准确识别出模型的阶次。
Claims (1)
- 一种工程结构模态识别的模型定阶方法,其特征在于,步骤如下:第一步,采集工程结构的脉冲响应及后处理首先实时采集工程结构的脉冲响应y k,再对工程结构的实测脉冲响应y k建立Hankel矩阵H(k-1)和H(k),H(k)如下:式中:向量y k为实测信号;k+i表示第k+i时刻;k到k+rH+cH-2为选择的实测时程数据点个数;将k-1代替k代入到上式得到H(k-1);然后,对Hankel矩阵H(k-1)进行奇异值分解:H(k-1)=UΓ 2V T式中:Γ为奇异值矩阵;U和V为酉阵;第二步,获得模态振型矩阵以奇异值矩阵Γ的秩作为结构模型阶次,设秩为cH,通过特征***实现方法求出特征值λ j;从求出的cH个特征值λ j中选取N个特征值进行分析,建立模态响应与结构响应的关系式,得到实测数据下的模态振型矩阵Φ j:式中:符号“+”表示矩阵的逆或伪逆;p为第p个时刻,即p=rH+cH-1;第三步,进一步获取第j阶模态的时程响应和第j阶模态的时程响应均方根值第j阶模态的时程响应Y p,j:第j阶模态的时程响应均方根值:第四步,得到模型阶次将第j阶模态各个自由度时程响应的均方根值相加,得到第j阶模态响应贡献指标MRCI:式中:r为自由度数;以阶次为横坐标,标准化的MRCI为纵坐标,即MRCI值除以MRCI最大值,绘出两者间的关系图;从关系图中找出两个相邻阶次的MRCI值比值最大时对应的阶次,即为模型阶次。
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US16/321,183 US20190171691A1 (en) | 2017-04-14 | 2018-03-06 | A method of mode order determination for engineering structural modal identification |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201710236267.4 | 2017-04-14 | ||
CN201710236267.4A CN107133195B (zh) | 2017-04-14 | 2017-04-14 | 一种工程结构模态识别的模型定阶方法 |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2018188432A1 true WO2018188432A1 (zh) | 2018-10-18 |
Family
ID=59716558
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/CN2018/078134 WO2018188432A1 (zh) | 2017-04-14 | 2018-03-06 | 一种工程结构模态识别的模型定阶方法 |
Country Status (3)
Country | Link |
---|---|
US (1) | US20190171691A1 (zh) |
CN (1) | CN107133195B (zh) |
WO (1) | WO2018188432A1 (zh) |
Cited By (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110413943A (zh) * | 2019-08-06 | 2019-11-05 | 海洋石油工程股份有限公司 | 海洋平台结构模态参数的识别方法 |
CN111444608A (zh) * | 2020-03-24 | 2020-07-24 | 成都理工大学 | 一种岩土工程可靠度分析中最佳截断阶数确定方法 |
CN113946795A (zh) * | 2021-09-17 | 2022-01-18 | 山东大学 | 一种超声波飞行时间估计方法 |
CN114925526A (zh) * | 2022-05-20 | 2022-08-19 | 南京航空航天大学 | 一种结合多工况响应的结构模态参数识别方法 |
CN117313501A (zh) * | 2023-11-30 | 2023-12-29 | 国网浙江省电力有限公司电力科学研究院 | 一种油浸式电力变压器绕组温升快速计算方法及*** |
Families Citing this family (13)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107133195B (zh) * | 2017-04-14 | 2019-08-09 | 大连理工大学 | 一种工程结构模态识别的模型定阶方法 |
CN108415884B (zh) * | 2018-02-24 | 2021-07-02 | 大连理工大学 | 一种结构模态参数实时追踪方法 |
US11170070B2 (en) | 2018-03-06 | 2021-11-09 | Dalian University Of Technology | Sparse component analysis method for structural modal identification when the number of sensors is incomplete |
CN108491608B (zh) * | 2018-03-06 | 2021-06-08 | 大连理工大学 | 传感器数量不完备时结构模态识别的稀疏分量分析方法 |
CN110133396B (zh) * | 2019-04-09 | 2021-07-20 | 上海电力学院 | 一种基于二分递推svd分解的居民电器开关事件检测方法 |
CN110555235A (zh) * | 2019-07-30 | 2019-12-10 | 同济大学 | 基于向量自回归模型的结构局部缺陷检测方法 |
KR102301201B1 (ko) * | 2019-08-09 | 2021-09-13 | 주식회사 아프로스 | IoT 센서 기반 회전 기기의 정밀 상태 진단 장치 및 방법 |
CN110749655B (zh) * | 2019-10-24 | 2021-05-07 | 大连理工大学 | 一种针对比例阻尼结构的复模态辨识方法 |
CN111274630B (zh) * | 2020-01-15 | 2022-09-20 | 大连理工大学 | 一种用于工程结构柔度识别的物理模态提取方法 |
CN112834243B (zh) * | 2021-01-18 | 2022-12-20 | 中国铁道科学研究院集团有限公司 | 一种车体的异常振动监测方法及装置 |
CN115238494B (zh) * | 2022-07-21 | 2023-10-20 | 中国核动力研究设计院 | 一种管道流弹失稳的部件位置识别方法 |
CN115357853B (zh) * | 2022-08-22 | 2023-08-04 | 河海大学 | 一种基于快速随机子空间的工程结构模态参数识别方法 |
CN116484512B (zh) * | 2023-06-22 | 2023-09-01 | 西北工业大学 | 一种航空发动机盘鼓类转子预紧状态的辨识方法 |
Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20060025973A1 (en) * | 2004-04-21 | 2006-02-02 | Taehyoun Kim | Methods and systems for model reduction and system identification of dynamic systems with multiple inputs |
CN101950375A (zh) * | 2010-09-03 | 2011-01-19 | 清华大学 | 基于遗传算法遍历辨识的未知模型阶次确定方法 |
CN101964050A (zh) * | 2010-09-06 | 2011-02-02 | 中国海洋大学 | 一种基于模型定阶和信号消噪的模态参数识别方法 |
CN102520071A (zh) * | 2011-12-20 | 2012-06-27 | 江苏方天电力技术有限公司 | 基于改进子空间算法的输电塔模态参数识别方法 |
CN105205461A (zh) * | 2015-09-18 | 2015-12-30 | 中国石油大学(华东) | 一种用于模态参数识别的信号降噪方法 |
CN107133195A (zh) * | 2017-04-14 | 2017-09-05 | 大连理工大学 | 一种工程结构模态识别的模型定阶方法 |
Family Cites Families (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US10990713B1 (en) * | 2014-08-13 | 2021-04-27 | Ansys, Inc. | Systems and methods for fast matrix decomposition in model generation |
CN108292105B (zh) * | 2015-09-24 | 2021-03-26 | Asml荷兰有限公司 | 减少光刻工艺中掩模版的加热和/或冷却的影响的方法 |
WO2018075400A1 (en) * | 2016-10-19 | 2018-04-26 | Sas Institute Inc. | Advanced control systems for machines |
-
2017
- 2017-04-14 CN CN201710236267.4A patent/CN107133195B/zh not_active Expired - Fee Related
-
2018
- 2018-03-06 US US16/321,183 patent/US20190171691A1/en not_active Abandoned
- 2018-03-06 WO PCT/CN2018/078134 patent/WO2018188432A1/zh active Application Filing
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20060025973A1 (en) * | 2004-04-21 | 2006-02-02 | Taehyoun Kim | Methods and systems for model reduction and system identification of dynamic systems with multiple inputs |
CN101950375A (zh) * | 2010-09-03 | 2011-01-19 | 清华大学 | 基于遗传算法遍历辨识的未知模型阶次确定方法 |
CN101964050A (zh) * | 2010-09-06 | 2011-02-02 | 中国海洋大学 | 一种基于模型定阶和信号消噪的模态参数识别方法 |
CN102520071A (zh) * | 2011-12-20 | 2012-06-27 | 江苏方天电力技术有限公司 | 基于改进子空间算法的输电塔模态参数识别方法 |
CN105205461A (zh) * | 2015-09-18 | 2015-12-30 | 中国石油大学(华东) | 一种用于模态参数识别的信号降噪方法 |
CN107133195A (zh) * | 2017-04-14 | 2017-09-05 | 大连理工大学 | 一种工程结构模态识别的模型定阶方法 |
Cited By (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110413943A (zh) * | 2019-08-06 | 2019-11-05 | 海洋石油工程股份有限公司 | 海洋平台结构模态参数的识别方法 |
CN111444608A (zh) * | 2020-03-24 | 2020-07-24 | 成都理工大学 | 一种岩土工程可靠度分析中最佳截断阶数确定方法 |
CN111444608B (zh) * | 2020-03-24 | 2022-04-19 | 成都理工大学 | 一种岩土工程可靠度分析中最佳截断阶数确定方法 |
CN113946795A (zh) * | 2021-09-17 | 2022-01-18 | 山东大学 | 一种超声波飞行时间估计方法 |
CN114925526A (zh) * | 2022-05-20 | 2022-08-19 | 南京航空航天大学 | 一种结合多工况响应的结构模态参数识别方法 |
CN114925526B (zh) * | 2022-05-20 | 2023-11-17 | 南京航空航天大学 | 一种结合多工况响应的结构模态参数识别方法 |
CN117313501A (zh) * | 2023-11-30 | 2023-12-29 | 国网浙江省电力有限公司电力科学研究院 | 一种油浸式电力变压器绕组温升快速计算方法及*** |
CN117313501B (zh) * | 2023-11-30 | 2024-03-19 | 国网浙江省电力有限公司电力科学研究院 | 一种油浸式电力变压器绕组温升快速计算方法及*** |
Also Published As
Publication number | Publication date |
---|---|
US20190171691A1 (en) | 2019-06-06 |
CN107133195A (zh) | 2017-09-05 |
CN107133195B (zh) | 2019-08-09 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
WO2018188432A1 (zh) | 一种工程结构模态识别的模型定阶方法 | |
Wang et al. | Improving forecasting accuracy of annual runoff time series using ARIMA based on EEMD decomposition | |
CN103824137B (zh) | 一种复杂机械设备多工况故障预测方法 | |
CN103577710B (zh) | 基于分数阶upf的航空功率变换器故障预测方法 | |
CN105719023A (zh) | 一种基于混合高斯分布的风电功率实时预测误差分析方法 | |
CN110059295B (zh) | 一种基于三点筛选法的***谐波阻抗估计方法 | |
CN107590317A (zh) | 一种计及模型参数不确定性的发电机动态估计方法 | |
CN109522948A (zh) | 一种基于正交局部保持投影的故障检测方法 | |
CN101446994A (zh) | 数控机床的热误差最小二乘支持向量机建模方法 | |
WO2019173943A1 (zh) | 一种自动追踪结构模态参数的方法 | |
CN103065052B (zh) | 一种测定机械产品理论寿命的方法 | |
CN111191191A (zh) | 一种精准预测混凝土坝变形效应的组合模型的构建方法 | |
CN104008304B (zh) | 一种乏信息多传感器神经网络‑熵测量不确定度评定方法 | |
CN109145418B (zh) | 一种基于截断响应稀疏分解法的外荷载实时识别方法 | |
Gunn et al. | On validating numerical hydrodynamic models of complex tidal flow | |
Trenary et al. | Does the Atlantic multidecadal oscillation get its predictability from the Atlantic meridional overturning circulation? | |
Saeed Far et al. | Evaluation of peaks-over-threshold method | |
CN111046327A (zh) | 适用于低频振荡与次同步振荡辨识的Prony分析方法 | |
CN111626000B (zh) | 一种桥梁状态的评估方法、装置、设备和存储介质 | |
CN106021685A (zh) | 一种考虑测量误差的退化可靠性分析方法 | |
CN114266289A (zh) | 一种复杂装备健康状态评估方法 | |
CN103134433B (zh) | 一种利用位移监测鉴别边坡失稳致滑因子的方法 | |
CN116432071A (zh) | 一种滚动轴承剩余寿命预测方法 | |
CN104298650A (zh) | 基于多方法融合的量化卡尔曼滤波方法 | |
CN106779354B (zh) | 用于飞行器性能评估的Bayes数据融合评估方法 |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 18783828 Country of ref document: EP Kind code of ref document: A1 |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
122 | Ep: pct application non-entry in european phase |
Ref document number: 18783828 Country of ref document: EP Kind code of ref document: A1 |