Operational modal parameter identification with correlated colored noise excitation

2021 ◽  
pp. 107754632110113
Author(s):  
Xiangyu Lu ◽  
Huaihai Chen ◽  
Xudong He
Keyword(s):  
Modal Analysis ◽  
Cantilever Beam ◽  
Colored Noise ◽  
Matrix Theory ◽  
Vibration Signal ◽  
Operational Modal Analysis ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Analysis Algorithm ◽  
The Matrix

Operational modal analysis refers to the modal analysis of a structure in its operating state. The advantage of operational modal analysis is that only the output vibration signal of a system is used. The classical operational modal analysis algorithm is based on the white noise excitation assumption, and it is considered that there is no correlation between the excitations; several identification methods have been developed in time and frequency domains. But excitations are not completely independent with each other and not pure white. In this article, the matrix theory is used to prove that the operational modal analysis algorithm can still be used to identify modal parameters when the excitation is correlated. In the simulation, five kinds of colored noise excitations are applied to the cantilever beam with correlated excitations, which shows that the idea proposed in this article is rational. In the experiment, the foundation excitation of colored noise is added to the cantilever beam, which can be regarded as applying several related excitations. It also shows the rationality of this idea.

Identify Modal Parameters of a Real Offshore Platform From the Response Excited by Natural Ice Loading

2016 ◽  
Author(s):  
Wenlong Yang ◽  
Lei Li ◽  
Qiang Fu ◽  
Yao Teng ◽  
Shuqing Wang ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Modal Analysis ◽  
Offshore Platform ◽  
Operational Modal Analysis ◽  
Modal Parameters ◽  
Offshore Platforms ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Stability Diagrams ◽  
The Stability

Experimental modal analysis (EMA) is widely implemented to obtain the modal parameters of an offshore platform, which is crucial to many practical engineering issues, such as vibration control, finite element model updating and structural health monitoring. Traditionally, modal parameters are identified from the information of both the input excitation and output response. However, as the size of offshore platforms becomes huger, imposing artificial excitation is usually time-consuming, expensive, sophisticated and even impossible. To address this problem, a preferred solution is operational modal analysis (OMA), which means the modal testing and analysis for a structure is in its operational condition subjected to natural excitation with output-only measurements. This paper investigate the applicability of utilizing response from natural ice loading for operational modal analysis of real offshore platforms. The test platform is the JZ20-2MUQ Jacket platform located in the Bohai Bay, China. A field experiment is carried out in winter season, when the platform is excited by floating ices. An accelerometer is installed on a leg and two segments of acceleration response are employed for identifying the modal parameters. In the modal parameter identification, specifically applied is the data-driven stochastic sub-space identification (SSI-data) method. It is one of the most advanced methods based on the first-order stochastic model and the QR algorithm for computing the structural eigenvalues. To distinguish the structural modal information, stability diagrams are constructed by identifying parametric models of increasing order. Observing the stability diagrams, the modal frequencies and damping ratios of four structural modes can be successfully identified from both segments. The estimated information from both segments are almost identical, which demonstrates the identification is trustworthy. Besides, the stability diagrams from SSI-data method are very clean, and the poles associated with structural modes can become stabilized at very low model order. The research in this paper is meaningful for the platforms serving in cold regions, where the ices could be widespread. Utilizing the response from natural ice loading for modal parameter identification would be efficient and cost-effective.

Modal Parameter Identification of 500kV SF6 Current Transformer Based on Natural Excitation Method

2014 ◽  
Vol 1065-1069 ◽  
pp. 3400-3405
Author(s):  
Qian Zhang ◽  
Zhi Cheng Lu ◽  
Yu Han Sun ◽  
Min Zhong
Keyword(s):  
Correlation Function ◽  
Parameter Identification ◽  
Modal Analysis ◽  
Cross Correlation ◽  
Impulse Response Function ◽  
Current Transformer ◽  
Cross Correlation Function ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Excitation Method

In this paper the feasibility of natural excitation method which uses cross-correlation function instead of impulse response function of the response to identify the modal parameter of 500kV SF6 current transformer was discussed .Four different algorithms were used to extract the modal parameter of 500kV SF6 current transformer with the measured cross-correlation function obtained by natural excitation method. The results of modal parameter identification using natural excitation method and experimental modal analysis were compared in the experimental way.

scholarly journals Comparison of Different Independent Component Analysis Algorithms for Output-Only Modal Analysis

2016 ◽  
Vol 2016 ◽  
pp. 1-25 ◽  
Author(s):  
Jianying Wang ◽  
Cheng Wang ◽  
Tianshu Zhang ◽  
Bineng Zhong
Keyword(s):  
Independent Component Analysis ◽  
Parameter Identification ◽  
Modal Analysis ◽  
Optimization Methods ◽  
Component Analysis ◽  
Independent Component ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Modal Parameters Identification ◽  
Output Only

From the principle of independent component analysis (ICA) and the uncertainty of amplitude, order, and number of source signals, this paper expounds the root reasons for modal energy uncertainty, identified order uncertainty, and modal missing in output-only modal analysis based on ICA methods. Aiming at the problem of lack of comparison and evaluation of different ICA algorithms for output-only modal analysis, this paper studies the different objective functions and optimization methods of ICA for output-only modal parameter identification. Simulation results on simply supported beam verify the effectiveness, robustness, and convergence rate of five different ICA algorithms for output-only modal parameters identification and show that maximization negentropy with quasi-Newton iterative of ICA method is more suitable for modal parameter identification.

scholarly journals A new modal analysis method applied to changing machine tool using clustering

2020 ◽  
Vol 12 (10) ◽  
pp. 168781402096832
Author(s):  
Xuchu Jiang ◽  
Xinyong Mao ◽  
Yingjie Chen ◽  
Caihua Hao
Keyword(s):  
Machine Tool ◽  
Modal Analysis ◽  
Natural Frequency ◽  
Efficient Method ◽  
Machine Tools ◽  
Vibration Signal ◽  
Modal Parameters ◽  
Analysis Method ◽  
Modal Parameter ◽  
The Difference

The states of the machine tool, such as the components’ position and the spindle speed, play leading roles in the change of dynamic parameters. However, the traditional modal analysis method that modal parameters manually identified from vibration signal is greatly interfered by harmonics, and the process of eliminating interference is very inefficient and subjective. At present, there is a lack of a standard and efficient method to characterize modal parameter changes in different states of machine tools. This paper proposes a new machine tool modal classification analysis method based on clustering. The characteristics related to the modal parameters are extracted from the response signal in different states, and the clustering results are used to reflect the changes of machine tool modal parameters. After the amplitude of the frequency response function is normalized, the characteristics related to the natural frequency are acquired, and the clustering results further reflect the difference of the natural frequency of the signal. The new method based on clustering can be a standard and efficient method to characterize modal parameter changes in different states of machine tools.

Chatter prediction based on operational modal analysis and an adaptive complex Morlet filter

2020 ◽  
Vol 62 (8) ◽  
pp. 484-492
Author(s):  
Kai Yang ◽  
Guofeng Wang ◽  
Kaile Ma
Keyword(s):  
Modal Analysis ◽  
Machine Tools ◽  
Production Efficiency ◽  
Stability Boundary ◽  
Operational Modal Analysis ◽  
Modal Parameter ◽  
Local Character ◽  
Specific Frequency ◽  
Harmonic Components ◽  
The Stability

Chatter that occurs between a cutting tool and a workpiece greatly reduces the surface quality and production efficiency. Therefore, it is of great importance to predict and avoid chatter so as to guarantee the stability of the manufacturing process. To realise the accurate prediction of the stability boundary of machine tools, operational modal analysis (OMA) is increasingly receiving attention due to its adequate consideration of variations in working conditions in the industrial environment. However, because of the influence of harmonic components in the response signals, the accuracy in identifying the modal parameters is seriously compromised. In this paper, an adaptive complex Morlet filter (ACMF) is presented to remove the harmonic components by adaptively adjusting the centre frequency and bandwidth according to the local character of the ambient environment in a specific frequency range and filtering out harmonic components that are not strict integer multiples of the fundamental frequency owing to non-rigid periodic motion of the machine tool spindle. In order to show the effectiveness of the proposed method, milling experiments are carried out and experimental modal analysis (EMA) is utilised to make comparisons with the proposed method. Moreover, comparisons between the ACMF and two other typical filtering methods are made. The results indicate that the proposed method performs well in modal parameter recognition for machine tools.

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