Dalian University of Technology

Tinghua Yi

Hongnan Li

Haibin Huang

Chunxu Qu

Xiaomei Yang

Xiaojun Yao

Shuwei Ma

A method for automatically detecting the free vibration response segment of the high-speed railway bridges after trains passing. First, pre-select the test response sequence to be decomposed based on the maximum of the time instants corresponding to the absolute maximums of the response vectors at various measuring point. Then, Extract the single-frequency modal response from the test response by the iterative variational mode decomposition and fit the envelope amplitude of the modal response by Hilbert transform. Finally, the vibration features at each time instants are marked as decay vibration or non-decay vibration. The longest structural response segment that meets the decay vibration features is determined as the detected free vibration response segment for modal identification. This invention can effectively detect the free vibration data segment without human participation, which is of great significance for the real-time accurate modal analysis of high-speed railway bridges.

Method for automatically detecting free vibration response of high-speed railway bridge for modal identification

Structural health monitoring providing sparse component analysis method for structural modal identification with incomplete number of sensors. Transforming structural acceleration response data into time frequency domain by short time Fourier transform, detecting time frequency points contributed by only one-order mode where real and imaginary parts have the same direction, and taking the detection result as the initial result of single-source-points; refining the initial result of detection of single-source-point located near the peak of power spectral density, and clustering single-source-points to obtain a mode shape matrix; constructing generalized spectral matrixes using short time Fourier transform coefficients, conducting singular value decomposition on generalized spectral matrix at a single-source-point, taking the first singular value as an auto-spectrum of single-order mode, obtaining the frequency of each order by picking the peak of auto-spectrum, and extracting damping ratio of each order by transforming the auto-spectrum into a time domain through inverse Fourier transform. By means of this method, structural modal parameters are obtained when the number of sensors is incomplete, thereby increasing the identification accuracy of the sparse component analysis method.

Sparse component analysis method for structural modal identification when the number of sensors is incomplete

Structural health monitoring relating to an automatic method for tracking structural modal parameters. First, Natural Excitation Technique is used to transform the random responses into correlation functions and Eigensystem Realization Algorithm combined with the stabilization diagram is used to estimate modal parameters from various response segments. Then, modes from the latter response segment are classified as traceable modes or untraceable modes according to correlations between their observability vectors and subspaces of the existing reference modes. Final, traceable modes will be grouped into specified clusters with the same structural characteristics on the basis of maximum modal observability vector correlation and minimum frequency difference. Meanwhile, union of the untraceable modes and existing reference modes are updated as the new reference modes which can be applied into the next tracking process. This can track the modal parameters automatically without artificial thresholds and the specified reference modes.

Automatic method for tracking structural modal parameters

The present invention belongs to the technical field of health monitoring for civil structures, and a dynamically non-Gaussian anomaly identification method is proposed for structural monitoring data. First, define past and current observation vectors for the monitoring data and pre-whiten them; second, establish a statistical correlation model for the whitened past and current observation vectors to obtain dynamically whitened data; then, divide the dynamically whitened data into two parts, i.e., the system-related and system-unrelated parts, which are further modelled by the independent component analysis; finally, define two statistics and determine their corresponding control limits, respectively, it can be decided that there is anomaly in the monitoring data when each of the statistics exceeds its corresponding control limit. The non-Gaussian and dynamic characteristics of structural monitoring data are simultaneously taken into account, based on that the defined statistics can effectively identify anomalies in the data.

Dynamically non-gaussian anomaly identification method for structural monitoring data

The present invention belongs to the technical field of health monitoring for civil structures, and a performance alarming method for bridge expansion joints based on temperature displacement relationship model is proposed. First, the canonically correlated temperature is proposed to maximize the correlation between bridge temperature field and expansion joint displacement; second, a temperature displacement relationship model for bridge expansion joints is established based on canonically correlated temperatures; then, a mean-value control chart is constructed to the error of temperature displacement relationship model; finally, reasonable control limits are determined for the mean-value control chart. A more accurate temperature displacement relationship model can be established based on canonically correlated temperatures, which is of important value to improve the performance alarming ability for expansion joint.

Performance alarming method for bridge expansion joints based on temperature displacement relationship model

The present invention belongs to the technical field of health monitoring for civil structures, and an anomaly identification method considering spatial-temporal correlation is proposed for structural monitoring data. First, define current and past observation vectors for the monitoring data and pre-whiten them; second, establish a statistical correlation model for the pre-whitened current and past observation vectors to simultaneously consider the spatial-temporal correlation in the monitoring data; then, divide the model into two parts, i.e., the system-related and system-unrelated parts, and define two corresponding statistics; finally, determine the corresponding control limits of the statistics, and it can be decided that there is anomaly in the monitoring data when each of the statistics exceeds its corresponding control limit.

Anomaly identification method for structural monitoring data considering spatial-temporal correlation

Data analysis for structural health monitoring relating to a method of modal identification for structures with non-proportional damping based on extended sparse component analysis. Hilbert transform constructs analytical signal of acceleration response. Analytical signal is transformed into time-frequency domain using short-time-Fourier transform. The criterion is taken as the correlation coefficient of adjacent frequency points is close to 1. Points contributed by only one mode are detected from the time-frequency plane. Phases calculated at single-source-points are used to remove local outliers through local outlier factor method. Amplitudes of complex-valued mode shapes are estimated by Hierarchical clustering of amplitudes for time-frequency coefficients at single-source-points. Averaged phases of grouped single-source-points are estimated phases of complex-valued mode shapes. Finally, complex-valued mode shapes are acquired. Modal responses are estimated by sparse reconstruction method. This method extends application range of sparse component analysis method, and can identify complex modes of non-proportionally damped structures.

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Dalian, China

Tinghua Yi