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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.

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This paper investigates the impact on operating time delay and relay maloperation i.e. selectivity, when high sampling rate is used in numerical distance protection (NDP). Simulation is carried out in an on-line way, for different source to line reach impedance ratio (SIR), fault type, fault locations and for different loading conditions. The voltage and current signals from capacitive voltage transformers and current transformers respectively are fed to the numerical distance relay (NDR). Sampling rate of 12, 16, 24, 32 and 64 times the fundamental frequency (50 Hz) are chosen for this investigation, since these are typically used in conventional distance relays.

Existing sensing solutions facilitating continuous condition assessment of wind turbine blades are limited by a lack of scalability and clear link signal-to-prognosis. With recent advances in conducting polymers, it is now possible to deploy networks of thin film sensors over large areas, enabling low cost sensing of large-scale systems. Here, we propose to use a novel sensing skin consisting of a network of soft elastomeric capacitors (SECs). Each SEC acts as a surface strain gage transducing local strain into measurable changes in capacitance. Using surface strain data facilitates the extraction of physics-based features from the signals that can be used to conduct condition assessment. We investigate the performance of an SEC network at detecting damages. Diffusion maps are constructed from the time series data, and changes in point-wise diffusion distances evaluated to determine the presence of damage. Results are benchmarked against time-series data produced from off-the-shelf resistive strain gauges. This paper presents data from a preliminary study. Results show that the SECs are promising, but the capability to perform damage detection is currently reduced by the presence of parasitic noise in the signal.