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104 results for “State estimation”
This data set is six 30-day ocean state estimation products for LTER-CCE cruises. The time periods of data assimilation fits for each LTER-CCE cruise are the following: 2006: 5/8 - 6/6, 2007: 4/1- 4/30, 2008 : 10/1 - 10/30, 2009 : 4/15 - 5/14, 2011 : 6/17 - 7/16, 2012 : 7/27 - 8/25
Used the sea surface height, sea surface temperature data measured from the space, and in situ temperature and salinity data from varied observational platforms including CCE-LTER cruises, we constrained the numerical ocean circulation model, Regional Ocean Modeling System (ROMS), so that the model simulation closely follows the observed ocean states. Data files are in netcdf format containing longitude, latitude, bathymetry, sea surface height, ocean current, temperature, salinity, density anomaly and surface fluxes. ROMS has a terrain following vertical coordinate and resolves shallow coastal regions better by construction. As a result, the depth of vertical level changes depending on the depth of bathymetry. This data set is the product of the investigations presented in Miller, A. J., H. Song and A. C. Subramanian, 2015: The physical oceanographic environment during the CCE-LTER years: Changes in climate and concepts. Deep-Sea Research II, 112, 6-17. Song, H., A. J. Miller, S. McClatchie, E. D. Weber, K. M. Nieto and D. M. Checkley, 2012: Application of a data-assimilation model to variability of Pacific sardine spawning and survivor habitats with ENSO in the California Current System. Journal of Geophysical Research-Oceans, 117, C03009, doi:10.1029/2011JC007302. We gratefully acknowledge continued funding from NSF (California Current Ecosystem LTER: OCE-0417616 and OCE-1026607) and NOAA (IOOS: NA17RJ1231)
Vehicle-Level Reasoning Systems: Integrating System-Wide data to Estimate Instantaneous Health State
One of the primary goals of Integrated Vehicle Health Management (IVHM) is to detect, diagnose, predict, and mitigate adverse events during the flight of an aircraft, regardless of the subsystem(s) from which the adverse event arises. To properly address this problem, it is critical to develop technologies that can integrate large, heterogeneous (meaning that they contain both continuous and discrete signals), asynchronous data streams from multiple subsystems in order to detect a potential adverse event, diagnose its cause, predict the effect of that event on the remaining useful life of the vehicle, and then take appropriate steps to mitigate the event if warranted. These data streams may have highly non-Gaussian distributions and can also contain discrete signals such as caution and warning messages which exhibit non-stationary and obey arbitrary noise models. At the aircraft level, a Vehicle-Level Reasoning System (VLRS) can be developed to provide aircraft with at least two significant capabilities: improvement of aircraft safety due to enhanced monitoring and reasoning about the aircraft’s health state, and also potential cost savings through Condition Based Maintenance (CBM). Along with the achieving the benefits of CBM, an important challenge facing aviation safety today is safeguarding against system- and component-level failures and malfunctions. Citation: A. N. Srivastava, D. Mylaraswamy, R. Mah, and E. Cooper, “Vehicle Level Reasoning Systems: Concept and Future Directions,” Society of Automotive Engineers Integrated Vehicle Health Management Book, Ian Jennions, Ed., 2011.
DYNAMIC STRAIN MAPPING AND REAL-TIME DAMAGE STATE ESTIMATION UNDER BIAXIAL RANDOM FATIGUE LOADING
DYNAMIC STRAIN MAPPING AND REAL-TIME DAMAGE STATE ESTIMATION UNDER BIAXIAL RANDOM FATIGUE LOADING SUBHASISH MOHANTY*, ADITI CHATTOPADHYAY*, JOHN N. RAJADAS**, AND CLYDE COELHO* Abstract. Fatigue damage and its prediction is one of the foremost concerns of structural integrity research community. The current research in structural health monitoring (SHM) is to provide continuous (or on demand) information about the state of a structure. The SHM system can be based on either active or passive sensor measurements. Though the current research on ultrasonic wave propagation based active sensing approach has the potential to estimate very small damage, it has severe drawbacks in terms of low sensing radius and external power requirements. To alleviate these disadvantages passive sensing based SHM techniques can be used. Currently, few efforts have been made towards, time-series fatigue damage state estimation over the entire fatigue life (stage-I, II & III). A majority of the available literature on passive sensing SHM techniques demonstrates the clear trend in damage growth during the final failure regime (stage-III regime) or during when the damage is comparatively large enough. The present paper proposes a passive sensing technique that demonstrates a clear trend in damage growth almost over the entire stage-II and III damage growth regime. A strain gauge measurement based passive SHM frameworks that can estimate the time-series fatigue damage state under random loading is proposed. For this purpose, a Bayesian Gaussian process nonlinear dynamic model is developed to map the reference condition dynamic strain at a given instant of time. The predicted strains are compared with the actual sensor measurements to estimate the corresponding error signals. The error signals estimated at two different locations are correlated to estimate the corresponding fatigue damage state. The approach is demonstrated for an Al-2434 complex cruciform structure applied with biaxial random loading.
An Integrated Approach to Battery Health Monitoring using Bayesian Regression, Classification and State Estimation
The application of the Bayesian theory of managing uncertainty and complexity to regression and classification in the form of Relevance Vector Machine (RVM), and to state estimation via Particle Filters (PF), proves to be a powerful tool to integrate the diagnosis and prognosis of battery health. Accurate estimates of the state-of-charge (SOC), the state-of-health (SOH) and state-of- life (SOL) for batteries provide a significant value addition to the management of any operation involving electrical systems. This is especially true for aerospace systems, where unanticipated battery performance may lead to catastrophic failures. Batteries, composed of multiple electro- chemical cells, are complex systems whose internal state variables are either inaccessible to sensors or hard to measure under operational conditions. In addition, battery performance is strongly influenced by ambient environmental and load conditions. Consequently, inference and estimation techniques need to be applied on indirect measurements, anticipated operational conditions and historical data, for which a Bayesian statistical approach is suitable. Accurate models of electro-chemical processes in the form of equivalent electric circuit parameters need to be combined with statistical models of state transitions, aging processes and measurement fidelity, need to be combined in a formal framework to make the approach viable. The RVM, which is a Bayesian treatment of the Support Vector Machine (SVM), is used for diagnosis as well as for model development. The PF framework uses this model and statistical estimates of the noise in the system and anticipated operational conditions to provide estimates of SOC, SOH and SOL. Validation of this approach on experimental data from Li-ion batteries is presented.
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OpenNeuro
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