Existing methods for structural health monitoring pose a formidable challenge to real time implementation due to the significantly large computational loads. The proposed algorithm is suitable for online applications because it maintains good pattern recognition capabilities while possessing a computationally compact network topology. This study employs the computational efficiency of single layer radial basis function (RBF) approximaters to create a subspace capable of isolating faults in multi-degree of freedom systems which involve coupled and uncoupled stiffness changes in real time. The RBF network transforms the displacement-time history of the varying plant into a decoupled output space which is then compared to a baseline healthy observer which undergoes the same decoupling transformation. The online comparison of the output of the time varying plant and the healthy observer in a decoupled subspace comprises the observer based error function. The error function is shown to not only detect the existence of faults, but also isolate these faults in real time in the presence of base excitation. The method is validated for systems that experience earthquake induced damage, as well as an experimental system using a semi-active independent variable stiffness device which is capable of varying system stiffness in real time. By simply observing the displacement-time history responses, the RBF augmented observer formulation is capable identifying changes in the stiffness at each degree of freedom.
Kamodo is a functional application programing interface (API) for scientific models and data. In Kamodo, all scientific resources are registered as symbolic fields which are mapped to model and data interpolators or algebraic expressions. Kamodo performs function composition and employs a unit conversion system that mimics hand-written notation: units are declared in bracket notation and conversion factors are automatically inserted into user expressions. Kamodo includes a LaTeX interface, automated plots, and a browser-based dashboard interface suitable for interactive data exploration. Kamodo's json API provides context-dependent queries and allows compositions of models and data hosted in separate docker containers. Kamodo is built primarily on sympy (Meurer et al., 2017) and plotly (Plotly Technologies Inc., 2015). While Kamodo was designed to solve the cross-disciplinary challenges of the space weather community, it is general enough to be applied in other fields of study.
This study validates an adaptive control algorithm capable of compensating for online sensor failure. Online failure is a relevant problem when considering actively damped, multi-story smart buildings experiencing a disturbance event. In recent years, Artificial Neural Networks (ANNs) have proven very efficient in pattern classification and control applications. In this study, the unique application of ANNs involving Radial Basis Functions (RBFs) combined with H∞ optimal control has demonstrated three significant characteristic advantages: (1) real time adaptability, (2) optimal convergence and computation time, and (3) most importantly, no offline training. The novelty of the proposed controller is elucidated by performing disturbance rejection tests involving a scaled two degree of freedom shear frame subjected to a combined H∞ and ANN control. A bench scale structural model is instrumented with piezoelectric sensors and actuators. After the onset of a first mode disturbance, the structural frame is subjected to a complete sensor failure. The proposed controller is shown to enhance the performance of a baseline H∞ controller in the presence of sensor failure.
A wheel experiencing sinkage and slippage events poses a high risk to rover missions as evidenced by recent mobility challenges on the Mars Exploration Rover (MER) project. Because several factors contribute to wheel sinkage and slippage conditions such as soil composition, large deformation soil behavior, wheel geometry, nonlinear contact forces, terrain irregularity, etc., there are significant benefits to modeling these events to a sufficient degree of complexity. For the purposes of modeling wheel sinkage and slippage at an engineering scale, meshfree finite element approaches enable simulations that capture sufficient detail of wheel-soil interaction while remaining computationally feasible. This study demonstrates some of the large deformation modeling capability of meshfree methods and the realistic solutions obtained by accounting for the soil material properties. A benchmark wheel-soil interaction problem is developed and analyzed using a specific class of meshfree methods called Reproducing Kernel Particle Method (RKPM). The benchmark problem is also analyzed using a commercially available finite element approach with Lagrangian meshing for comparison. RKPM results are comparable to classical pressure-sinkage terramechanics relationships proposed by Bekker-Wong. Pending experimental calibration by future work, the meshfree modeling technique will be a viable simulation tool for trade studies assisting rover wheel design.
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