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Multichannel, medical monitoring system allows almost complete freedom of movement for subject during monitoring periods. System comprises monitoring unit (biobelt), transmission line, and data acquisition unit. Belt, made of polybenzimidizole fabric, is wrapped around individual's waist and held in place by overlapping sections of Velcro closure material.
The paper describes the significant milestones in the use of satellites for snow and ice monitoring. The feasibility of such monitoring was demonstrated by the Tiros 2 satellite in 1961. Nimbus 1 showed that breaks in the sea ice can be easily monitored during continuous nighttime conditions; Nimbus 3 showed the practicality of delineating regions of active melting of ice and snow in temperate areas. Landsat data have been found to be particularly useful for monitoring and studying glaciers and their attendant surface features. Ice concentration can be determined with reasonable accuracy from a sequence of electronically scanned microwave radiomenter images made aboard Nimbus 5. In the future we can expect improved sensors and spacecraft systems with longer operating lives.
Purge flow is bled from the upstream compressor and supplied to the under-platform region to prevent hot main gas path ingress that damages vulnerable under-platform hardware components. A majority of turbine rim seal research has sought to identify methods of improving sealing technologies and understanding the physical mechanisms that drive ingress. While these studies directly support the design and analysis of advanced rim seal geometries and purge flow systems, the studies are limited in their applicability to real-time monitoring required for condition-based operation and maintenance. As operational hours increase for in-service engines, this lack of rim seal performance feedback results in progressive degradation of sealing effectiveness, thereby leading to reduced hardware life. To address this need for rim seal performance monitoring, this study utilizes measurements from a one-stage turbine research facility operating with true-scale engine hardware at engine-relevant conditions. Time-resolved pressure measurements collected from the rim seal region are regressed with sealing effectiveness through the use of common machine learning techniques to provide real-time feedback of sealing effectiveness. Two modeling approaches are presented that use a single sensor to predict sealing effectiveness accurately over a range of two turbine operating conditions. Here, the results show that an initial purely data-driven model can be further improved using domain knowledge of relevant turbine operations, which yields sealing effectiveness predictions within 3% of measured values.
Purge flow is bled from the upstream compressor and supplied to the under-platform region to prevent hot main gas path ingress that damages vulnerable under-platform hardware components. A majority of turbine rim seal research has sought to identify methods of improving sealing technologies and understanding the physical mechanisms that drive ingress. While these studies directly support the design and analysis of advanced rim seal geometries and purge flow systems, the studies are limited in their applicability to real-time monitoring required for condition-based operation and maintenance. As operational hours increase for in-service engines, this lack of rim seal performance feedback results in progressive degradation of sealing effectiveness, thereby leading to reduced hardware life. To address this need for rim seal performance monitoring, the present study utilizes measurements from a one-stage turbine research facility operating with true-scale engine hardware at engine-relevant conditions. Time-resolved pressure measurements collected from the rim seal region are regressed with sealing effectiveness through the use of common machine learning techniques to provide real-time feedback of sealing effectiveness. Two modelling approaches are presented that use a single sensor to predict sealing effectiveness accurately over a range of two turbine operating conditions. Results show that an initial purely data-driven model can be further improved using domain knowledge of relevant turbine operations, which yields sealing effectiveness predictions within three percent of measured values.
A new robust method of automated real-time diagnosis of faults in an aircraft engine or a similar complex system involves the use of a bank of Kalman filters. In order to be highly reliable, a diagnostic system must be designed to account for the numerous failure conditions that an aircraft engine may encounter in operation. The method achieves this objective though the utilization of multiple Kalman filters, each of which is uniquely designed based on a specific failure hypothesis. A fault-detection-and-isolation (FDI) system, developed based on this method, is able to isolate faults in sensors and actuators while detecting component faults (abrupt degradation in engine component performance). By affording a capability for real-time identification of minor faults before they grow into major ones, the method promises to enhance safety and reduce operating costs. The robustness of this method is further enhanced by incorporating information regarding the aging condition of an engine. In general, real-time fault diagnostic methods use the nominal performance of a "healthy" new engine as a reference condition in the diagnostic process. Such an approach does not account for gradual changes in performance associated with aging of an otherwise healthy engine. By incorporating information on gradual, aging-related changes, the new method makes it possible to retain at least some of the sensitivity and accuracy needed to detect incipient faults while preventing false alarms that could result from erroneous interpretation of symptoms of aging as symptoms of failures. The figure schematically depicts an FDI system according to the new method. The FDI system is integrated with an engine, from which it accepts two sets of input signals: sensor readings and actuator commands. Two main parts of the FDI system are a bank of Kalman filters and a subsystem that implements FDI decision rules. Each Kalman filter is designed to detect a specific sensor or actuator fault. When a sensor or actuator fault occurs, large estimation errors are generated by all filters except the one using the correct hypothesis. By monitoring the residual output of each filter, the specific fault that has occurred can be detected and isolated on the basis of the decision rules. A set of parameters that indicate the performance of the engine components is estimated by the "correct" Kalman filter for use in detecting component faults. To reduce the loss of diagnostic accuracy and sensitivity in the face of aging, the FDI system accepts information from a steady-state-condition-monitoring system. This information is used to update the Kalman filters and a data bank of trim values representative of the current aging condition.
Data from the Advanced Very High Resolution Radiometer aboard the NOAA polar orbiting satellite are being operationally applied to provide estimates of vegetation cover and/or condition over a large part of the earth by the USDA. The wide scan angle (+ or - 54 deg) of this system permits daily views of the earth when used to its limits. Five-day repetitive coverage is acquired, assuming cloud-free conditions, in current operations which limit the use of the scan to the center + or - 14 deg of swath. While use of the full scan width would provide clear acquisitions frequent enough to monitor crop development and condition even with normal cloudiness, these off-nadir data are made difficult to interpret due to the non-Lambertian nature of the surface, enhanced effect of the atmosphere, inclusion of subpixel and thin invisible clouds in the scene, and differences in illumination across the scene; all of which contribute to variations in observed reflected radiation. Some approaches to provide corrections for these effects are discussed here.
Surface parameter measurements are routinely used during deep well construction to monitor and guide drilling conditions for improved performance and reduced costs. However, these measurements are of reduced value without a standard to aid in evaluation and decision making. A method is demonstrated whereby drill bit constraint models are used to interpret drilling response parameters. Drill rig parameter data for well GDC-36 at the Geysers Geothermal Field Power were acquired by Geysers Power Company and drilling contractor Kenai Drilling using Pason US DataHub and evaluated. Drilling parameters are evaluated using laboratory-validated rock reduction models for predicting the phenomenological response of drag bits (Detournay and Defourny, 1992) along with other model constraints in computational algorithms. The method is used to evaluate overall bit performance, monitor bit integrity, and detect the presence of drillstring vibrations and other conditions contributing to bit failure; comparisons are made to observations of bit wear and damage. The method will be applied in real-time to improve decision-making on subsequent wells and has applicability to development of advanced analytics on future geothermal wells using real-time electronic drilling recorder (EDR) data for improved performance and reduced drilling costs.
Primarily designed to acquire data at steady state test conditions, the system can also monitor slow transients such as those generated in moving to a new test condition. The system configuration makes use of a microcomputer at the test site which acts as a communications multiplexer between the measurement and display devices and a centrally located minicomputer. A variety of measurement and display devices are supported using a modular approach. This allows each system to be configured with the proper combination of devices to meet the specific test requirements, while still leaving the option to add special interfaces when needed. Centralization of the minicomputer improves utilization through sharing. The creation of a pool of minis to provide data acquisition and display services to a variable number of running tests also offers other important advantages.
Electrochemical tests were conducted to assess the corrosion performance of a developmental multiphase cermet nuclear waste form in different conditions. Corrosion currents were monitored as the surface equilibrated in an acidic brine solution during potentiostatic tests at different imposed potentials. Electrochemical impedance spectroscopy was used to quantify differences in the electrical properties of surfaces stabilised at different potentials. The passivating oxides that will stabilise the surface under disposal conditions are formed and maintained during testing. The measured corrosion currents were two orders of magnitude lower for passivated surfaces than for polished surfaces over a wide range of redox conditions. The electrochemical results were correlated with microscopic analyses of the corroded surfaces and physical models of the surface passivation and degradation processes are proposed. The measured corrosion rates are suitable for the long-term performance modeling of cermet nuclear waste forms.
In one embodiment, a system includes a bioreactor coupled to a substrate. The bioreactor includes: a plurality of walls defining a reservoir; a plurality of fluidic channels in at least some of the walls; a fluidic inlet in fluidic communication with the reservoir via, the fluidic channels; a fluidic outlet in fluidic communication with the reservoir via the fluidic channels; and one or more sensors coupled to the reservoir, each sensor being configured to detect one or more of: environmental conditions in the reservoir; and physiological conditions of one or more cells optionally present in the reservoir. In another embodiment, a method includes delivering media to a reservoir of a bioreactor; delivering a plurality of cells to the reservoir, controlling a reservoir temperature and a reservoir gas composition; using one or more of the sensors to monitor environmental and physiological conditions; and reporting the environmental and/or physiological conditions.
Tests were carried out to evaluate a variety of runway surface types and wetness conditions, using specially instrumented NASA B-737 and B-727 aircraft and several ground friction measuring devices. The performance data for aircraft braking on dry, wet, snow-covered, and ice-covered runway conditions are presented and compared to ground-vehicle friction data obtained under similar runway conditions. The relationships between ground vehicles and the aircraft friction data are identified, and the effects on friction of major test parameters, such as the speed, the tire characteristics, and the type of surface-contaminant are discussed. The results demonstrated that properly maintained and calibrated ground vehicles can be used to monitor the runway friction conditions.
The NASA Science Internet (NSI) Network Operations Staff is responsible for providing reliable communication connectivity for the NASA science community. As the NSI user community expands, so does the demand for greater interoperability with users and resources on other networks (e.g., NSFnet, ESnet), both nationally and internationally. Coupled with the science community's demand for greater access to other resources is the demand for more reliable communication connectivity. Recognizing this, the NASA Science Internet Project Office (NSIPO) expands its Operations activities. By January 1990, Network Operations was equipped with a telephone hotline, and its staff was expanded to six Network Operations Analysts. These six analysts provide 24-hour-a-day, 7-day-a-week coverage to assist site managers with problem determination and resolution. The NSI Operations staff monitors network circuits and their associated routers. In most instances, NSI Operations diagnoses and reports problems before users realize a problem exists. Monitoring of the NSI TCP/IP Network is currently being done with Proteon's Overview monitoring system. The Overview monitoring system displays a map of the NSI network utilizing various colors to indicate the conditions of the components being monitored. Each node or site is polled via the Simple Network Monitoring Protocol (SNMP). If a circuit goes down, Overview alerts the Network Operations staff with an audible alarm and changes the color of the component. When an alert is received, Network Operations personnel immediately verify and diagnose the problem, coordinate repair with other networking service groups, track problems, and document problem and resolution into a trouble ticket data base. NSI Operations offers the NSI science community reliable connectivity by exercising prompt assessment and resolution of network problems.
As part of an initiative to steward research, development, and innovation into the nuclear fuel cycle, Idaho National Laboratory is building the Beartooth testbed. Beartooth will include a cascade of centrifugal contactors, glove box lines, solidification, and dissolution equipment to aid in the progression of novel separation techniques and provide hands-on opportunities to early-career separation scientists. Beartooth will incorporate novel monitoring techniques using sensors and machine learning algorithms to inform a process operator of separation conditions. This research is examining monitoring technologies not typically used in nuclear separation processes such as acoustic microphones, accelerometers, infrared cameras, red-green-blue color sensors, among others. These sensors are being examined for their functionality within a separation process and their ability to detect applicable signals. Machine learning methods are being developed to determine their utility in detecting faults and alerting operators of expected and unexpected events. These methods have the potential to impact Safeguards by Design efforts and real-time decision making. This overview will detail preliminary results from acoustic, vibration, and color sensors.
Landslides triggered by rainfall can possibly be foreseen in real time by jointly using rainfall intensity-duration thresholds and information related to land surface susceptibility. However, no system exists at either a national or a global scale to monitor or detect rainfall conditions that may trigger landslides due to the lack of extensive ground-based observing network in many parts of the world. Recent advances in satellite remote sensing technology and increasing availability of high-resolution geospatial products around the globe have provided an unprecedented opportunity for such a study. In this paper, a framework for developing an experimental real-time monitoring system to detect rainfall-triggered landslides is proposed by combining two necessary components: surface landslide susceptibility and a real-time space-based rainfall analysis system (http://trmm.gsfc.nasa.aov). First, a global landslide susceptibility map is derived from a combination of semi-static global surface characteristics (digital elevation topography, slope, soil types, soil texture, and land cover classification etc.) using a GIs weighted linear combination approach. Second, an adjusted empirical relationship between rainfall intensity-duration and landslide occurrence is used to assess landslide risks at areas with high susceptibility. A major outcome of this work is the availability of a first-time global assessment of landslide risk, which is only possible because of the utilization of global satellite remote sensing products. This experimental system can be updated continuously due to the availability of new satellite remote sensing products. This proposed system, if pursued through wide interdisciplinary efforts as recommended herein, bears the promise to grow many local landslide hazard analyses into a global decision-making support system for landslide disaster preparedness and risk mitigation activities across the world.
Methane (CH 4 ), an abundant greenhouse gas, is the second largest contributor to global warming after carbon dioxide (CO 2 ). In comparison to CO 2 , CH 4 has a larger warming effect over a much shorter lifetime. While technologies to radically reduce global carbon dioxide emissions are materializing, rapid reductions in methane emissions are needed to limit near-term warming. Methane is primarily emitted as a byproduct from agricultural activities and energy extraction/utilization and is currently monitored via bottom-up (i.e., activity level) or top-down (via airborne or satellite retrievals) approaches. However, significant methane leaks remain undetected, and emission rates are challenging to characterize with current monitoring frameworks. In this report, we study methane leaks from oil and gas infrastructure using a tiered monitoring approach that combines bottom-up and top-down approaches in an integrated framework. We describe the individual advantages of bottom-up and top-down sensors in both stationary and mobile settings before characterizing how a fully integrated framework can improve predictions and uncertainties of potential leak locations and their emission rates. Further, we study the impact of different atmospheric (wind) conditions on integrated methane monitoring and develop a probabilistic approach to optimal sensor placement, thereby shortening detection times and improving monitoring capabilities. Last, we discuss how biogenic flux modeling can be used to improve assessment of background methane concentrations needed to fully assess the sensitivity of a tiered monitoring system.
Recently, multiple wind turbine failure databases have reviewed that the pitch system is one of the subassemblies with the highest failure rates and largest contributors to the overall downtime. Therefore, there has been an increasing interest to provide remote health monitoring for wind turbine pitch system. While most of the research articles are discussing pitch actuation system (hydraulic or electric actuator) faults only, there is very limited research on pitch-bearing-defect detection. This article provides a remote and hardware-free solution to monitor multiaxis pitch-bearing health condition called pitch symmetrical-component analysis. It leverages readily available low-resolution (100 Hz) electrical measurements, mechanical measurements, and control signals from the existing pitch control platform, and innovatively applies symmetrical-component analysis in multiphase ac system to multiaxis pitch control system and introduces multiaxis pitch-bearing degradation trending curves. This hardware-free solution can be directly applied to the existing wind turbines and successfully give the wind farm operator an early warning before multiaxis pitch bearing fails. It has been proved to be accurate, low cost, and has minimum impacts on turbine normal operation, and has been validated by field data from several North America MW-scale wind farms. This approach turns out to be the first hardware-free (no additional hardware needed) method to remotely monitor and diagnose multiaxis wind turbine pitch-bearing condition.
The costs associated with the increasing maintenance and surveillance needs of aging structures are rising at an unexpected rate. Multi-site fatigue damage, hidden cracks in hard-to-reach locations, disbonded joints, erosion, impact, and corrosion are among the major flaws encountered in today’s extensive fleet of aging aircraft and space vehicles. Aircraft maintenance and repairs represent about a quarter of a commercial fleet’s operating costs. The application of Structural Health Monitoring (SHM) systems using distributed sensor networks can reduce these costs by facilitating rapid and global assessments of structural integrity. The use of in-situ sensors for real-time health monitoring can overcome inspection impediments stemming from accessibility limitations, complex geometries, and the location and depth of hidden damage. Reliable, structural health monitoring systems can automatically process data, assess structural condition, and signal the need for human intervention. The ease of monitoring an entire on-board network of distributed sensors means that structural health assessments can occur more often, allowing operators to be even more vigilant with respect to flaw onset. SHM systems also allow for condition-based maintenance practices to be substituted for the current time-based or cycle-based maintenance approach thus optimizing maintenance labor. The Federal Aviation Administration has conducted a series of SHM validation and certification programs intended to comprehensively support the evolution and adoption of SHM practices into routine aircraft maintenance practices. This report presents one of those programs involving a Sandia Labs-aviation industry effort to move SHM into routine use for aircraft maintenance. The Airworthiness Assurance NDI Validation Center (AANC) at Sandia Labs, in conjunction with Sikorsky, Structural Monitoring Systems Ltd., Anodyne Electronics Manufacturing Corp., Acellent Technologies Inc., and the Federal Aviation Administration (FAA) carried out a trial validation and certification program to evaluate Comparative Vacuum Monitoring (CVM) and Piezoelectric Transducers (PZT) as a structural health monitoring solution to specific rotorcraft applications. Validation tasks were designed to address the SHM equipment, the health monitoring task, the resolution required, the sensor interrogation procedures, the conditions under which the monitoring will occur, the potential inspector population, adoption of CVM and PZT systems into rotorcraft maintenance programs and the document revisions necessary to allow for their routine use as an alternate means of performing periodic structural inspections. This program addressed formal SHM technology validation and certification issues so that the full spectrum of concerns, including design, deployment, performance and certification were appropriately considered. Sandia Labs designed, implemented, and analyzed the results from a focused and statistically relevant experimental effort to quantify the reliability of a CVM system applied to Sikorsky S-92 fuselage frame application and a PZT system applied to an S-92 main gearbox mount beam application. The applications included both local and global damage detection assessments. All factors that affect SHM sensitivity were included in this program: flaw size, shape, orientation and location relative to the sensors, as well as operational and environmental variables. Statistical methods were applied to performance data to derive Probability of Detection (POD) values for SHM sensors in a manner that agrees with current nondestructive inspection (NDI) validation requirements and is acceptable to both the aviation industry and regulatory bodies. The validation work completed in this program demonstrated the ability of both CVM and PZT SHM systems to detect cracks in rotorcraft components. It proved the ability to use final system response parameters to provide a Green Light/Red Light (“GO” – “NO GO”) decision on the presence of damage. In additional to quantifying the performance of each SHM system for the trial applications on the S-92 platform, this study also identified specific methods that can be used to optimize damage detection, guidance on deployment scenarios that can affect performance and considerations that must be made to properly apply CVM and PZT sensors. These results support the main goal of safely integrating SHM sensors into rotorcraft maintenance programs. Additional benefits from deploying rotorcraft Health and Usage Monitoring Systems (HUMS) may be realized when structural assessment data, collected by an SHM system, is also used to detect structural damage to compliment the operational environment monitoring. The use of in-situ sensors for health monitoring of rotorcraft structures can be a viable option for both flaw detection and maintenance planning activities. This formal SHM validation will allow aircraft manufacturers and airlines to confidently make informed decisions about the proper utilization of CVM and PZT technology. It will also streamline future regulatory actions and formal certification measures needed to assure the safe application of SHM solutions.
The decommissioned Hallam Nuclear Power Facility (Hallam), located in Lancaster County, Nebraska, approximately 19 miles south of Lincoln, was developed as a prototype to investigate the technical and economic feasibility of a sodium-cooled, graphite-moderated nuclear reactor. As part of its Power Demonstration Program, the US Atomic Energy Commission (US AEC), a predecessor agency of the US Department of Energy (US DOE), operated the Hallam reactor from 1962 to 1964. The radiological surveillance of the Hallam site was established when the operating agreement between AEC and the Nebraska Public Power District was terminated. The US DOE Chicago Operations Office and the Nebraska Department of Health (NDH) agreed to install a shallow groundwater monitoring system as part of the environmental surveillance program. Surveillance at the site was scheduled to end in fiscal year 2005, with no further activities planned after that. At that time, the plan was to transition the site to the Nebraska Public Power District with future use of the facility to remain restricted. US DOE has title to and responsibility for the entombed radioactive materials. A surveillance and monitoring program was initiated in 1970 by NDH and was funded by US AEC. It included the analysis of samples from deep production wells (groundwater from the regional aquifer at depths greater than 55 meters [180 feet]) at the Sheldon Station. NDH was concerned in 1990 about the possibility of shallow groundwater coming in contact with the buried radiological materials along the buried walls of the reactor. Subsequently, DOE and NDH agreed to further characterize hydrologic conditions and establish a monitoring program in the shallow, perched groundwater zones. The current monitoring program focuses on the shallow, perched groundwater. Today, decades since facility decommissioning, site inspections and groundwater monitoring continue at this Cold War-era legacy site. The US DOE Office of Legacy Management (LM) monitors groundwater as a best management practice in response to a request from the Nebraska Department of Health and Human Services (DHHS). In 2006, LM recommended discontinuing groundwater monitoring because analytical results since 1970 demonstrated there had been no impact to shallow, perched groundwater and no current or anticipated unacceptable risk to human health and the environment. The State of Nebraska did not concur with the recommendation to stop monitoring but did agree to a reduction in sampling and analysis from once a year to once every 2 years--a schedule that began in 2008. An updated assessment of the groundwater monitoring effort was issued in 2016. The entire body of water-quality data continues to demonstrate that there have been no negative impacts to the shallow, perched groundwater from radioactive materials entombed at the site 46 years ago. LM, with concurrence from Nebraska DHHS, changed the sampling frequency, effective in 2018, as follows: once every 5 years from 2021 to 2041 and once every 10 years from 2041 to 2071. In summary, LM remains committed to its primary mission to protect human health and the environment at each of its 92 legacy sites. With results from groundwater sampling events demonstrating compliance for 48 years, the Hallam site was able to set an LM benchmark to optimize long-term stewardship and define an end-state strategy for other decommissioned reactor facilities across the DOE complex. Continued collaboration with local communities, stakeholder, and regulators often leads to outcomes of sound protectiveness without compromise to safety or compliance, as demonstrated by the optimization of the long-term stewardship at the Hallam site. (authors)