Real-time substructural identification by boundary force modeling

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In this paper, a hierarchical outlier detection approach is proposed for online distributed structural identification. In contrast to centralized identification, distributed identification extracts important features from the raw response data at the sensor nodes and transmits only them to the base station. Therefore, outlier detection is substantially more complicated than the traditional approach. In the proposed method, the local outliers in the raw data are detected directly at the corresponding sensor node, and they are excluded from further processing. However, if a sensor node is biased or exhibits other patterned outliers, these outliers will be undetectable at the sensor node level. It is necessary to conduct another level of outlier detection at the base station, namely, global outlier detection, before fusion. These two levels of outlier detection are of different nature. Local outlier detection concerns directly with the raw response data, whereas the targets of global outlier detection are the local estimation results of the stiffness parameters. Therefore, they require different mathematical tools. The proposed hierarchical outlier detection approach detects the local outliers according to the outlier probability of the data points at the sensor nodes, whereas it detects the global outliers according to the outlier probability of the local estimation results. By excluding both types of outliers, reliable online distributed structural identification can be achieved. Two examples are presented to demonstrate the proposed method.

Section: Introductionmentioning

confidence: 99%

Hierarchical outlier detection approach for online distributed structural identification

Huang

Yuen

2020

Self Cite

“…Numerous studies focused on modal identification methods but few studies took the tracking of modes from different response sets into consideration. 15 Although some identification methods have been considered to be online or real time, such as recursive subspace methods, [16][17][18][19] recursive Fourier transform, 20 the Hilbert-Huang transform, 21 the complexity pursuit, 22 and the Bayesian approach 23 proposed in recent years, the modal identifiability has not been discussed in them. Modal identifibility, 24 i.e., whether a mode could be identified from a response set, is influenced by the monitoring conditions (such as sensor locations), the excitation conditions (such as weak wind or typhoon), and the identification methods.…”

Section: Introductionmentioning

confidence: 99%

Continuous tracking of bridge modal parameters based on subspace correlations

Yang

et al. 2020

Summary Modal parameter variation plays an important role in bridge health monitoring. But tracking the long‐term variation of modal parameters is restricted by the modal identifiability. Since some modes cannot be identified due to the limitations of sensor locations or excitation conditions, the identified modes will be missing or misclassification in the tracking process. In this paper, the multistage tracking technique based on subspace correlations is proposed. To avoid missing the identified modes, the reference mode list should be complete to cover all identified modes. Thus, the first stage is to update the reference list adaptively, where the correlations between the observability vectors of identified modes and the subspaces of existing reference modes are taken. The second stage is to cluster each traceable mode and the specified reference mode together without misclassification, where the modal similarity is measured by the similarity of observability vectors (SOV). The proposed algorithm is verified by the vibration data of a numerical model and a highway bridge, respectively. The results show that the proposed method can link with modes in the same order correctly and update the reference mode list adaptively to avoid missing the identified modes.

“…The goal of system identification is to build a representative mathematical model for the underlying system based on the observed data and prior knowledge including assumptions, hypotheses, and paradigms. A number of system identification methods have been developed, for example, subspace identification 10,11 , substructural identification methods, 12–17 artificial neural networks, 18,19 Hilbert–Huang transform, 20,21 wavelet transform, 22–24 evolutionary strategy, 25,26 and the NEXT technique 27 .…”

Section: Introductionmentioning

confidence: 99%

Real‐time system identification using hierarchical interhealing model classes

Yuen

Dong

2020

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In this article, a novel algorithm is proposed for real-time system identification using hierarchical interhealing model classes. One major difficulty in the system identification for large structures is to determine the complexity of the structural model and the number of unknown parameters. One can detect finer damages with more unknown stiffness parameters, but this may cause fluctuating or even unidentifiable results. Although Bayesian model class selection allows one to choose among some prescribed model classes, the number of possible model classes for large structures is huge. In this paper, we propose a new method using hierarchical interhealing model classes. The modeling errors of these model classes can be corrected adaptively according to the data and the results from the optimal model class. This includes not only the errors in the parameters but also the deficiencies of the parametric models. Furthermore, the model classes are established in a hierarchical manner so that the proposed strategy requires only a small number of model classes, yet being able to explore a large solution space. Consequently, the proposed algorithm can handle a large number of damage possibilities while it maintains relatively low computational cost. Two examples are presented, and the results show that the proposed algorithm can detect, locate, and quantify damages reliably and efficiently.