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

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 undamaged and damaged states of the system. We use a spectral diffusion map approach to identify 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 a 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. To achieve 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 a 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

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

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

A VOLTTRON based implementation of Supervisory Control using Generalized Gossip for Building Energy Systems

 Building energy systems comprising of many subsystems with local information and heterogenous preferences demand the need for coordination in order to perform optimally. The performance required by a typical airside HVAC system involving a large number of zones are multifaceted, involves attainment of various objectives (such as optimal supply air temperature) which requires coordination among zones. The use of traditional centralized optimization involving a large number of variables is very difficult to solve in near real time. This paper presents a novel distributed optimization framework to achieve energy efficiency in large-scale buildings. The primary goals are to achieve scalability, robustness, flexibility and low-cost commissioning. The results are presented using the proposed distributed optimization framework based on a physical testbed in the Iowa Energy Center and demonstrate the advantages of the proposed methodology compared to a typical baseline strategy. The paper outlines a real-life implementation of the proposed framework based on the VOLTTRONTM platform, recently developed by the Pacific Northwest National Laboratory (PNNL)

A Data-driven Approach towards Integration of Microclimate Conditions for Predicting Building Energy Performance

Energy consumption in buildings is a major part of the overall energy usage in the United States and across the world. Energy performance of buildings is primarily affected by the heat exchange with the building outer skin and the surrounding environment. Building energy simulation (BES) tools are capable of predicting energy usage with variable degree of accuracy using the building geometry, construction information and weather data. In this regard, it is a common practice in BES tools to include boundary conditions of the building shell based on the local weather station. However, to account for accurate building energy consumption, especially in urban environments with significant amount of anthropogenic heat source, it is necessary to consider the microclimate around the building. These conditions are influenced by the immediate environment such as surrounding buildings, hard surfaces and trees. However, deployment of sensors to monitor microclimate of a building can be quite expensive and hence, not scalable. Therefore, a model to predict the microclimate information based on local weather station is essential to provide a more reasonable outdoor weather information for the BES tools, and hence predicting energy consumption in buildings more accurately. In this work, we propose a scalable, computationally inexpensive data-driven approach for predicting microclimate information (e.g., temperature) under multiple weather conditions. We demonstrate that such a framework can be implemented based on machine learning techniques such as spatiotemporal pattern network (STPN) and neural networks (NNs). We demonstrate the efficacy of our proposed framework by using the predicted microclimate data to predict the building energy consumption with higher accuracy compared to the prediction using local weather station data alone

Modeling and control of complex building energy systems

Building energy sector is one of the important sources of energy consumption and especially in the United States, it accounts for approximately 40% of the total energy consumption. Besides energy consumption, it also contributes to CO2 emissions due to the combustion of fossil fuels for building operation. Preventive measures have to be taken in order to limit the greenhouse gas emission and meet the increasing load demand, energy efficiency and savings have been the primary objective globally. Heating, Ventilation, and air-conditioning (HVAC) system is a major source of energy consumption in buildings and is the principal building system of interest. These energy systems comprising of many subsystems with local information and heterogeneous preferences demand the need for coordination in order to perform optimally. The performance required by a typical airside HVAC system involving a large number of zones are multifaceted, involves attainment of various objectives (such as optimal supply air temperature) which requires coordination among zones. The required performance demands the need for accurate models (especially zones), control design at the individual (local-VAV (Variable Air Volume)) subsystems and a supervisory control (AHU (Air Handling Unit) level) to coordinate the individual controllers. In this thesis, an airside HVAC system is studied and the following considerations are addressed: a) A comparative evaluation among representative methods of different classes of models, such as physics-based (e.g., lumped parameter autoregressive models using simple physical relationships), data-driven (e.g., artificial neural networks, Gaussian processes) and hybrid (e.g., semi-parametric) methods for different physical zone locations; b) A framework for control of building HVAC systems using a methodology based on power shaping paradigm that exploits the passivity property of a system. The system dynamics are expressed in the Brayton-Moser (BM) form which exhibits a gradient structure with the mixed-potential function, which has the units of power. The power shaping technique is used to synthesize the controller by assigning a desired power function to the closed loop dynamics so as to make the equilibrium point asymptotically stable, and c) The BM framework and the passivity tool are further utilized for stability analysis of constrained optimization dynamics using the compositional property of passivity, illustrated with energy management problem in buildings. Also, distributed optimization (such as subgradient) techniques are used to generate the optimal setpoints for the individual local controllers and this framework is realized on a distributed control platform VOLTTRON, developed by the Pacific Northwest National Laboratory (PNNL).</p