Damage assessment with state–space embedding strategy and singular value decomposition under stochastic excitation

Liu, Gang; Mao, Zhu; Todd, Michael D.; Huang, Zheng

doi:10.1177/1475921713513973

Cited by 32 publications

(29 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ref. [29] used a combined statespace embedding strategy and singular value decomposition to detect structural damage. Refs [30,31,17,32] used a diffusion map-based approach for detection of anomaly in dynamic systems.…”

Section: Introductionmentioning

confidence: 99%

Bridge damage detection using spatiotemporal patterns extracted from dense sensor network

Liu

Gong

Laflamme

et al. 2016

Meas. Sci. Technol.

View full text Add to dashboard Cite

Abstract. The alarmingly degrading state of transportation infrastructures combined with their key societal and economic importance calls for automatic condition assessment methods to facilitate smart management of maintenance and repairs. With the advent of ubiquitous sensing and communication capabilities, scalable data-driven approaches is of great interest, as it can utilize large volume of streaming data without requiring detailed physical models that can be inaccurate and computationally expensive to run. Properly designed, a data-driven methodology could enable fast and automatic evaluation of infrastructures, discovery of causal dependencies among various sub-system dynamic responses, and decision making with uncertainties and lack of labeled data. In this work, a spatiotemporal pattern network (STPN) strategy built on symbolic dynamic filtering (SDF) is proposed to explore spatiotemporal behaviors in a bridge network. Data from strain gauges installed on two bridges are generated using finite element simulation for three types of sensor networks from a density perspective (dense, nominal, sparse). Causal relationships among spatially distributed strain data streams are extracted and analyzed for vehicle identification and detection, and for localization of structural degradation in bridges. Multiple case studies show significant capabilities of the proposed approach in: (i) capturing spatiotemporal features to discover causality between bridges (geographically close), (ii) robustness to noise in data for feature extraction, (iii) detecting and localizing damage via comparison of bridge responses to similar vehicle loads, and (iv) implementing real-time health monitoring and decision making work flow for bridge networks. Also, the results demonstrate increased sensitivity in detecting damages and higher reliability in quantifying the damage level with increase in sensor network density.

show abstract

Section: Introductionmentioning

confidence: 99%

Bridge damage detection using spatiotemporal patterns extracted from dense sensor network

Liu

Gong

Laflamme

et al. 2016

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…The application of data-driven and data-dimensionality based methods for SHM of civil structures is not new [10]- [14]. These applications consist of constructing an attractor of proper dimension and time delay from one or many observations, and studying changes in the phase-space to detect damages.…”

Section: Introductionmentioning

confidence: 99%

Spectral diffusion map approach for structural health monitoring of wind turbine blades

Venkatesh¹,

Cao²,

Vaidya³

et al. 2015

2015 American Control Conference (ACC)

View full text Add to dashboard Cite

In this paper, we develop data-driven method for the diagnosis of damage in mechanical structures using an array of distributed sensors. The proposed approach relies on comparing intrinsic geometry of data sets corresponding to the undamage and damage state of the system. We use spectral diffusion map approach for identifying the intrinsic geometry of the data set. In particular, time series data from distributed sensors is used for the construction of diffusion map. The low dimensional embedding of the data set corresponding to different damage level is done using singular value decomposition of the diffusion map to identify the intrinsic geometry. We construct appropriate metric in diffusion space to compare the different data set corresponding to different damage cases. The application of this approach is demonstrated for damage diagnosis of wind turbine blades. Our simulation results show that the proposed diffusion map-based metric is not only able to distinguish the damage from undamage system state, but can also determine the extent and the location of the damage. Abstract-In this paper, we develop data-driven method for the diagnosis of damage in mechanical structures using an array of distributed sensors. The proposed approach relies on comparing intrinsic geometry of data sets corresponding to the undamage and damage state of the system. We use spectral diffusion map approach for identifying the intrinsic geometry of the data set. In particular, time series data from distributed sensors is used for the construction of diffusion map. The low dimensional embedding of the data set corresponding to different damage level is done using singular value decomposition of the diffusion map to identify the intrinsic geometry. We construct appropriate metric in diffusion space to compare the different data set corresponding to different damage cases. The application of this approach is demonstrated for damage diagnosis of wind turbine blades. Our simulation results show that the proposed diffusion map-based metric is not only able to distinguish the damage from undamage system state, but can also determine the extent and the location of the damage.

show abstract

“…A large number of model-based and data-driven damage detection approaches have been proposed for vibration-based SHM (Liu et al 2014, Xia et al 2014, and most of them have been employed successfully on numerical examples and well-controlled lab-scale structures. For in-situ SHM applications, however, the detection results may be unreliable due to uncertainties (Mao 2012).…”

Section: Introductionmentioning

confidence: 99%

Damage detection with interval analysis for uncertainties quantification

Liu¹,

Mao²,

Jun³

2015

Proceedings of the Second International Conference on Performance-Based and Life-Cycle Structural Engineering (PLSE 2015)

Self Cite

View full text Add to dashboard Cite

Infrastructure damage detection is widely adopted to prevent structural collapse and cut down the maintenance for owners with timely repair, but most damage detection methods only obtain marginal performance for in-situ structures due to uncertainties. A non-probabilistic damage detection method for uncertainty quantification is presented in this study. The diagnosis elements are extracted from the coefficient matrix of the vector auto-regressive (VAR) model which is identified from the measured acceleration, and the Mahalanobis distance (MD) of these diagnosis elements between pristine and unknown condition is employed as damage feature. The interval of MD due to physical variability is computed by optimal interval analysis with the differential evolution algorithm. A modified receiver operating characteristic (ROC) curve is developed and the area under ROC is utilized to localize damage. The overlap rate (OR) of MD interval are defined to quantify damage severity. The numerical simulation results demonstrate that the interval analysis method can successfully detect damage when the physical variability is considering. KEY WORDSDamage detection, uncertainty quantification, interval analysis, differential evolution algorithm, overlap rate.

show abstract

Damage assessment with state–space embedding strategy and singular value decomposition under stochastic excitation

Cited by 32 publications

References 33 publications

Bridge damage detection using spatiotemporal patterns extracted from dense sensor network

Bridge damage detection using spatiotemporal patterns extracted from dense sensor network

Spectral diffusion map approach for structural health monitoring of wind turbine blades

Damage detection with interval analysis for uncertainties quantification

Contact Info

Product

Resources

About