Diagnostics and Prognostics at NASA Ames
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Quick and effective accident management is essential in any industry in order to limit and contain possible threats to both people and environment/assets. This is in particular relevant in the nuclear industry where accidents may have major impacts from an economic, health and societal point of view. As an example, the Fukushima Dai-ichi nuclear power plant accident highlighted the importance of the ability of plant operators and plant staff to react quickly and effectively in accident conditions. This particular event showed the importance of being able to: • Determine/estimate the actual status of the plant (i.e., diagnosis) when the monitoring system is corrupted or partially unavailable, and, • Forecast its future evolution (i.e., prognosis). In this paper we want to describe a research direction geared toward the development of a new set of advanced diagnosis and prognosis tools. We employ innovative data mining and machine learning techniques that are able to infer plant status and mimic the plant’s full temporal behavior in order to assist the reactor operators during an accident scenario.
Determining at least one of a prognosis or a therapy for a patient based on a stained tissue section of the patient. An image of a stained tissue section of a patient is processed by a processing device. A set of features values for a set of cell-based features is extracted from the processed image, and the processed image is associated with a particular cluster of a plurality of clusters based on the set of feature values, where the plurality of clusters is defined with respect to a feature space corresponding to the set of features.
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The various technologies presented herein relate to providing prognosis and health management (PHM) of a photovoltaic (PV) system. A PV PHM system can eliminate long-standing issues associated with detecting performance reduction in PV systems. The PV PHM system can utilize an ANN model with meteorological and power input data to facilitate alert generation in the event of a performance reduction without the need for information about the PV PHM system components and design. Comparisons between system data and the PHM model can provide scheduling of maintenance on an as-needed basis. The PHM can also provide an approach for monitoring system/component degradation over the lifetime of the PV system.
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The electrical interconnection and isolation design aspects within an inverter must also provide high thermal performance and thermomechanical reliability. In collaboration with ORNL, DuPont, and Indiana Integrated Circuits (IIC), NREL is evaluating new packaging materials and manufacturing techniques for wide-bandgap-based traction inverters. Thermal modeling and experimental evaluation revealed that an organic direct-bond-copper (ODBC) substrate has similar thermal resistance to traditional ceramic substrates. High-temperature thermal cycling has shown the ODBC substrates to have greater reliability than ceramic substrates which is a significant finding in the quest for low-cost, power-dense, high-temperature power electronics. IIC's direct chip-to-chip topside electrical connection will be attached to ODBC substrates and reliability evaluation for both technologies will be performed at NREL in Q4.
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The primary deliverable for this project will be to construct a power electronics package utilizing an organic electrically insulating substrate material with a direct chip-to-chip interconnect technology and demonstrate superior thermal performance and greater reliability under thermal cycling, thermal aging, vibration, power cycling, and electrical high-potential evaluation over traditional packages.