Query: Stochastic subspace algorithm for modal identification

title: Modal parameter identification of subsynchronous oscillation based on stochastic subspace algorithm

title: Application of Improved Combined Deterministic Stochastic Subspace Algorithm in Bridge Modal Parameter Identification
abstract: Modal parameter identification is considered to be one of the most important tasks in structural health monitoring because it provides a reliable reference for structural vibration control, damage severity, and operational state. Moreover, at present, the combined deterministic stochastic subspace algorithm is cogitated as one of the key algorithms in the modal parameter identification, which is why it is widely used in the modal parameter identification of bridge structures. In this paper, a novel method is proposed, which is a time domain identification algorithm, based on sliding window fuzzy C means clustering algorithm combined with deterministic stochastic subspace identification (SC CDSI) to achieve online intelligent tracking and identification of modal parameters for nonlinear time varying structures. First of all, to realize the online tracking and identification process, it is necessary to divide the input and output signal of the nonlinear time varying structure by windowing; for that, to determine the window function, window size and window step length according to the characteristics of the signal are analyzed. Secondly, in order to satisfy the intelligent identification of effective modals in stability diagram, the fuzzy C means clustering algorithm is kept as a base, whereas frequency, damping ratio, and modal shapes serve as clustering elements, applied to fuzzy C means clustering algorithm, and then the intelligent selection of effective modals is achieved. Finally, a shaking table test bridge is used as a modal parameter identification in lab, and its results are compared with the MIDAS finite element results. The compared results show that the proposed SC CDSI identification algorithm can accurately achieve the intelligent identification of online tracking of the structural frequency, and the identification results are reliable to be used in real life bridge structures.

title: Automated operational modal analysis of an end supported pontoon bridge using covariance driven stochastic subspace identification and a density based hierarchical clustering algorithm

title: Modal Parameter Determination of Stay Cable with an Improved Algorithm Based on Stochastic Subspace Identification
abstract: The modal parameter determination of stay cable is generally a critical issue for either designing or monitoring cable stayed bridges. Stochastic subspace identification (SSI) has been proved in recent years to be an excellent tool for obtaining all the modal parameters together in the cases where only the output signals are available. Nevertheless, its feasibility and accuracy in practical applications remain to be extensively verified. Especially for the situations where the frequency content of excitation is narrowly banded and thus far away from the white noise assumption underneath the theoretical derivation of SSI, the problem of superfluously indentifying numerous deceptive modes usually occurs. Attempting to crack this difficulty, an improved algorithm based on SSI is developed in the current work for performing the modal parameter determination of stay cables. This study adopts the covariance type of SSI, usually with better stability and computation efficiency, to establish an effective methodology for extensively identifying the modal parameters of stay cables. Several details of choosing the computational parameters in performing SSI are first discussed, followed by proposing an alternative stabilization diagram such that most modal parameters of cable can stand out and then imposing appropriate sifting criteria to extract the most reliable modal parameters.

title: Modal identification of arch dams using balanced stochastic subspace identification
abstract: Dynamic characteristics extracted from ambient and forced vibration tests are always associated with some level of uncertainties because of unknown nature of applied forces, existence of ambient noises as well as measurement errors. Stochastic Subspace Methods are among the most accurate and consistent methods within the domain of operational modal analysis. In this research, a new technique for Operational Modal Analysis is proposed using Stochastic Realization Theory and Canonical Correlation Analysis, which in comparison with previous methodologies, identification process are directly performed in the prediction space by extracting orthonormal vectors of data space. Considering its optimized nature, the proposed method is expected to have superior accuracy in terms of elimination of unstable poles as well as low time consumption analysis. To indicate the efficiency and accuracy of the proposed algorithm, it is applied to reanalyze the results of the forced vibration tests performed on the Shahid Rajaee arch dam in northern Iran. These tests were conducted via steady state sinusoidal stimulation method. More accurate natural frequencies are obtained compared to those of previously reported results, besides the fact that the first three modes of the structure were identified by the new approach, while they were not observed via the previous one. In order to examine the capabilities of the proposed method for processing of ambient vibration records, the dynamic characteristics of the Pacoima dam was identified using the recorded responses during 2001 earthquake in San Fernando, California. The results indicated good accuracy in the obtained frequencies and damping ratios compared to those obtained via data driven subspace method. Time consumption of identification process were reduced significantly (up to 50% for both case studies indicating a faster convergence rate provided by the proposed method.

title: Finite element model updating of a large structure using multi setup stochastic subspace identification method and bees optimization algorithm
abstract: In the present contribution, operational modal analysis in conjunction with bees optimization algorithm are utilized to update the finite element model of a solar power plant structure. The physical parameters which required to be updated are uncertain parameters including geometry, material properties and boundary conditions of the aforementioned structure. To determine these uncertain parameters, local and global sensitivity analyses are performed to increase the solution accuracy. An objective function is determined using the sum of the squared errors between the natural frequencies calculated by finite element method and operational modal analysis, which is optimized using bees optimization algorithm. The natural frequencies of the solar power plant structure are estimated by multi setup stochastic subspace identification method which is considered as a strong and efficient method in operational modal analysis. The proposed algorithm is efficiently implemented on the solar power plant structure located in Shahid Chamran university of Ahvaz, Iran, to update parameters of its finite element model. Moreover, computed natural frequencies by numerical method are compared with those of the operational modal analysis. The results indicate that, bees optimization algorithm leads accurate results with fast convergence.

title: A novel recursive stochastic subspace identification algorithm with its application in long term structural health monitoring of office buildings
abstract: This study develops a novel recursive algorithm to significantly enhance the computation efficiency of a recently proposed stochastic subspace identification (SSI) methodology based on an alternative stabilization diagram. Exemplified by the measurements taken from the two investigated office buildings, it is first demonstrated that merely one sixth of computation time and one fifth of computer memory are required with the new recursive algorithm. Such a progress would enable the realization of on line and almost real time monitoring for these two steel framed structures. This recursive SSI algorithm is further applied to analyze 20 months of monitoring data and comprehensively assess the environmental effects. It is certified that the root mean square (RMS) response can be utilized as an excellent index to represent most of the environmental effects and its variation strongly correlates with that of the modal frequency. More detailed examination by comparing the monthly correlation coefficient discloses that larger variations in modal frequency induced by greater RMS responses would typically lead to a higher correlation.

title: Synergistic bridge modal analysis using frequency domain decomposition, observer Kalman filter identification, stochastic subspace identification, system realization using information matrix, and autoregressive exogenous model
abstract: This paper presents multiple system identification of large scale bridge structures proposing the combined usage of different modal parameter findings, namely from Frequency Domain Decomposition, Observer Kalman Filter Identification/Eigensystem Realization Algorithm, Combined Deterministic Stochastic Subspace Identification, System Realization Using Information Matrix, and Autoregressive Exogenous Model. A method centric democratic ranking approach visualizes and quantifies the harmony among different system identification methods in terms of modal parameters, then ranks them based on the correlation among each other, and consequently complies with the highest rank modal parameter outputs. The synergistic scheme is applied on a numerical beam and two bridge structures including one healthy and another subjected to progressive damage. Looking at the top rank selections, one can see that outlier identification results from a population of modal parameters can intuitively become extinct. The collaboration among methods is dependent on the chosen methods; therefore, method selection relies on care and fair representation of the identification features. Lack of agreement between methods can indicate low confidence in the outranking method and is quantified by median absolute deviation. Nevertheless, the majority of the algorithm population agrees on specific results, which are valuable to produce state knowledge despite low signal to noise ratio, especially without the presence of a reference. Thus, the collaborative usage of multiple methods in a systematic and ranking based manner reduces significant error and outlier possibilities in modal identification due to algorithm related issues, which is the novel contribution of this study.

title: Modal Parameter Identification of a Quayside Container Crane Based on Data Driven Stochastic Subspace Identification
abstract: Modal parameters of a quayside container crane can reflect the vibration performance of a structure at work. Identifying these parameters through modal tests can be used to improve the design scheme, evaluate performance, and detect damage.  This paper introduces the process of modal parameter identification based on data driven stochastic subspace identification (SSI) The proposed algorithm is programmed based on the MATLAB platform. The effectiveness of the algorithm is verified by a numerical three degree of freedom vibration model. In this paper, the overall structure of a quayside container crane was modally tested for the first time under ambient excitation. The test scheme using the mobile measurement method is introduced in detail. The signal data collected by the modal test are used as the input data of the data driven SSI, and the first seven natural frequencies, damping ratios, and mode shapes of the crane are identified successfully. Furthermore, the finite element model of the crane is established with ANSYS, and the first seven frequencies and mode shapes are also obtained.  By comparing the results of the two methods, it can be found that the parameters identified by the proposed algorithm have high accuracy. The proposed method can be applied to the health monitoring system of the quayside container crane.

title: Modal parameters identification of bridge by improved stochastic subspace identification method with Grubbs criterion
abstract: In the wind tunnel test of a long span bridge model, to ensure that the dynamic characteristics of the model can satisfy the test design requirements, it is particularly important to accurately identify the modal parameters of the model. First, the stochastic subspace identification algorithm was used to analyze the modal parameters of the model in the wind tunnel test; then, Grubbs criterion was introduced to effectively eliminate outliers in the damping ratio matrix. Stochastic subspace identification algorithm with Grubbs criterion improved the accuracy of the modal parameter identification and the ability to determine system matrix order and prevented the modal omissions caused by determining the stable condition of the damping ratio in the stability diagram. Finally, Oujiang Bridge was used as an example to verify the stochastic subspace identification algorithm with Grubbs criterion and compare with the results of the finite element method. The example shows that the improved method can be effectively applied to the modal parameter identification of bridges.