Damage detection on mesosurfaces using distributed sensor network and spectral diffusion maps

Abstract

Abstract. In this work, we develop a data-driven method for the diagnosis of damage in mesoscale mechanical structures using an array of distributed sensor networks. The proposed approach relies on comparing intrinsic geometries of data sets corresponding to the undamage and damage states 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 maps. The low dimensional embedding of the data set corresponding to different damage levels is obtained using singular value decomposition of the diffusion map. We construct appropriate metrics in the diffusion space to compare the different data sets corresponding to different damage cases. The developed algorithm is applied for damage diagnosis of wind turbine blades. Towards this goal, we developed a detailed finite element-based model of CX-100 blade in ANSYS using shell elements. Typical damage, such as crack or delamination, will lead to a loss of stiffness, is modeled by altering the stiffness of the laminate layer. One of the main challenges in the development of health monitoring algorithms is the ability to use sensor data with relatively small signal-to-noise ratio. Our developed diffusion map-based algorithm is shown to be robust to the presence of sensor noise. The proposed diffusion map-based algorithm is advantageous by enabling the comparison of data from numerous sensors of similar or different types of data through data fusion, hereby making it attractive to exploit the distributed nature of sensor arrays. This distributed nature is further exploited for the purpose of damage localization. We perform extensive numerical simulations to demonstrate that the proposed method can successfully determine the extent of damage on the wind turbine blade and also localize the damage. We also present preliminary results for the application of the developed algorithm on the experimental data. These preliminary results obtained using experimental data are promising and is a topic of our ongoing investigation.

Vibration-based damage localization and quantification in a pretensioned concrete girder using stochastic subspace identification and particle swarm model updating

“…A challenge in deploying SHM solutions to civil structures and structural members is in the large geometries under consideration, whereas sensors need to be strategically deployed in order to provide rich-enough data that can yield to condition-based information. 3 This can be done using sparse 4 and dense 5 networks of sensors measuring strain, 6,7 acceleration, 8–11 and other 12–14 states.…”

Structural health monitoring of rotary aerospace structures based on electromechanical impedance of integrated piezoelectric transducers

“…In the past decade, the majority of research works in the field of structural health monitoring (SHM) have been thus far focused on the development of damage detection and localization algorithms using various approaches such as laser Doppler velocimetry, fiber Bragg grating, piezoelectric wafer active sensors (PWASs), piezoelectric paint, and intelligent resistive coating materials (Bai et al, 2015; Barazanchy et al (2014); Cao and Ouyang, 2016; Chinde et al, 2016; Fan and Qiao, 2011; Feng et al, 2014; Giurgiutiu, 2014; Kaloop and Hu, 2015; Kharroub et al, 2015; Kumar and Reddy, 2016; Peters and Webb, 2015; Statham, 2011; Wang et al (2016); Witos, 2008; Xu and Giurgiutiu, 2005, 2006; Yang and Fritzen, 2011a, 2011b, 2012) Zhao et al (2007). SHM based on electromechanical impedance (EMI) of piezoelectric transducers was first reported by Giurgiutiu and Zagrai (2000) to compare the impedance/admittance frequency spectrum of pristine and damaged specimens, particularly at an ultrasonic frequency range of 100 Hz–12 MHz.…”

The Stretching Method for Vibration‐Based Structural Health Monitoring of Civil Structures

“…The efficient management of aging infrastructure network, as well as the preventive conservation of civil structures and, in particular, of heritage ones, that are exposed to various natural and anthropogenic hazards, are stimulating scientific research toward the development of automated structural condition assessment strategies. Similar strategies are commonly referred to as Structural Health Monitoring (SHM) systems, whose main goal is using field observations of the response of a structure in operational conditions for damage diagnosis and health prognosis (Farrar and Worden, ; Jiang and Adeli, ; Laflamme et al., ; Park et al., ; Shan et al., ; Zhong and Xiang, ; Chinde et al., ).…”