Automated Monitoring of the Rhizosphere with Graphite Electrodes
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Rhizosphere, microbial sensors, real-time monitori↗
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Automated Monitoring with a BSP Fault-Detection Test
The figure schematically illustrates a method and procedure for automated monitoring of an asset, as well as a hardware- and-software system that implements the method and procedure. As used here, asset could signify an industrial process, power plant, medical instrument, aircraft, or any of a variety of other systems that generate electronic signals (e.g., sensor outputs). In automated monitoring, the signals are digitized and then processed in order to detect faults and otherwise monitor operational status and integrity of the monitored asset. The major distinguishing feature of the present method is that the fault-detection function is implemented by use of a Bayesian sequential probability (BSP) technique. This technique is superior to other techniques for automated monitoring because it affords sensitivity, not only to disturbances in the mean values, but also to very subtle changes in the statistical characteristics (variance, skewness, and bias) of the monitored signals.
Bickford, Randall L.↗
Automated monitor and control for deep space network subsystems
The problem of automating monitor and control loops for Deep Space Network (DSN) subsystems is considered and an overview of currently available automation techniques is given. The use of standard numerical models, knowledge-based systems, and neural networks is considered. It is argued that none of these techniques alone possess sufficient generality to deal with the demands imposed by the DSN environment. However, it is shown that schemes that integrate the better aspects of each approach and are referenced to a formal system model show considerable promise, although such an integrated technology is not yet available for implementation. Frequent reference is made to the receiver subsystem since this work was largely motivated by experience in developing an automated monitor and control loop for the advanced receiver.
Smyth, P.↗
Automated Monitoring with a BCP Fault-Decision Test
The Bayesian conditional probability (BCP) technique is a statistical fault-decision technique that is suitable as the mathematical basis of the fault-manager module in the automated-monitoring system and method described in the immediately preceding article. Within the automated-monitoring system, the fault-manager module operates in conjunction with the fault-detector module, which can be based on any one of several fault-detection techniques; examples include a threshold-limit-comparison technique or the BSP or SPRT technique mentioned in the preceding article. The present BCP technique is used to evaluate a series of one or more fault-detection events for the purpose of filtering out occasional false alarms produced by many types of statistical fault-detection procedures. The BCP technique increases the probability that an automated monitoring system produces a correct decision regarding the presence or absence of a fault. Because occasional false alarms are an inevitable consequence of the SPRT, BSP, or any other statistically based fault-detection test, there is a need for a logical procedure to distinguish between true and false alarms. Heretofore, it has been common practice to make a fault decision on an ad hoc basis for example by following a multiple-observation voting strategy in which a signal is declared to be indicative of a fault if m of the last n observations produced a fault-detection alarm. The BCP technique was developed to obtain results more reliable than those afforded by a voting strategy. The BCP technique involves a test in which one applies Bayesian inference techniques to a series of one or more single-observation alarms produced by a fault-detection test. One considers the last n decisions generated by a fault-detection test in order to evaluate the conditional probability that a failure is indicated (see figure). Each new decision reached by a fault-detection test is treated as a new piece of evidence about the state of the monitored asset, and the conditional probability of failure for the system is updated on the basis of this new evidence. The conditional probability of failure is compared with a predefined limit. For a probability below the limit, the asset is declared to be healthy. For a probability above the limit, the asset is declared to be faulty.
Bickford, Randall L.↗
Apparatus and method for automated monitoring of airborne bacterial spores
An apparatus and method for automated monitoring of airborne bacterial spores. The apparatus is provided with an air sampler, a surface for capturing airborne spores, a thermal lysis unit to release DPA from bacterial spores, a source of lanthanide ions, and a spectrometer for excitation and detection of the characteristic fluorescence of the aromatic molecules in bacterial spores complexed with lanthanide ions. In accordance with the method: computer-programmed steps allow for automation of the apparatus for the monitoring of airborne bacterial spores.
Ponce, Adrian↗
Automated patient monitor
Automated patient monitor applying telemetry system of Saturn launch vehicle
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Automated Monitoring of Pipeline Rights-of-Way
NASA Ames Research Center and the Pipeline Research Council International, Inc. have partnered in the formation of a research program to identify and develop the key technologies required to enable automated detection of threats to gas and oil transmission and distribution pipelines. This presentation describes the Right-of-way Automated Monitoring (RAM) program and highlights research successes to date, continuing challenges to implementing the RAM objectives, and the program's ongoing work and plans.
Frost, Chard Ritchie↗
Automated Monitoring of the Rhizosphere with Graphite Electrodes, Final Technical Report
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54 ENVIRONMENTAL SCIENCES↗
Applications of AI for automated monitoring - The SHARP system
This paper describes a software system which utilizes artificial intelligence technology to automate several real-time mission operations functions. The paper gives an overview of Voyager spacecraft telecommunications operations at the Jet Propulsion Laboratory to highlight requirements for operations automation in the area of real-time monitoring and analysis. A knowledge-based system, called the 'Spacecraft Health Automated Reasoning Prototype' (SHARP), was developed to explore methods for automated ground data system health and status analysis. The prototype system was applied to Voyager spacecraft telecommunications operations, and installed in the Voyager real-time telecommunications operations area during the spacecraft's encounter with the planet Neptune. The paper reviews the design of the fault detection and diagnosis portions of SHARP, and discusses the performance of SHARP during the encounter. Two follow-on systems based on SHARP which are now in development are also discussed.
Atkinson, David J.↗
Automated monitoring of electrocatalyst corrosion as a function of electrochemical history and electrolyte formulation
We present an automated platform for studying electrode corrosion as a function of electrochemical history and electrolyte composition using Pt ORR as a proof-of-concept relevant to next-generation phosphoric acid fuel cells.
Chemistry↗
SHARP: Automated monitoring of spacecraft health and status
Briefly discussed here are the spacecraft and ground systems monitoring process at the Jet Propulsion Laboratory (JPL). Some of the difficulties associated with the existing technology used in mission operations are highlighted. A new automated system based on artificial intelligence technology is described which seeks to overcome many of these limitations. The system, called the Spacecraft Health Automated Reasoning Prototype (SHARP), is designed to automate health and status analysis for multi-mission spacecraft and ground data systems operations. The system has proved to be effective for detecting and analyzing potential spacecraft and ground systems problems by performing real-time analysis of spacecraft and ground data systems engineering telemetry. Telecommunications link analysis of the Voyager 2 spacecraft was the initial focus for evaluation of the system in real-time operations during the Voyager spacecraft encounter with Neptune in August 1989.
Atkinson, David J.↗
SHARP - Automated monitoring of spacecraft health and status
Briefly discussed here are the spacecraft and ground systems monitoring process at the Jet Propulsion Laboratory (JPL). Some of the difficulties associated with the existing technology used in mission operations are highlighted. A new automated system based on artificial intelligence technology is described which seeks to overcome many of these limitations. The system, called the Spacecraft Health Automated Reasoning Prototype (SHARP), is designed to automate health and status analysis for multi-mission spacecraft and ground data systems operations. The system has proved to be effective for detecting and analyzing potential spacecraft and ground systems problems by performing real-time analysis of spacecraft and ground data systems engineering telemetry. Telecommunications link analysis of the Voyager 2 spacecraft was the initial focus for evaluation of the system in real-time operations during the Voyager spacecraft encounter with Neptune in August 1989.
Atkinson, David J.↗
Automated monitoring of recovered water quality
Laboratory prototype water quality monitoring system provides automatic system for online monitoring of chemical, physical, and bacteriological properties of recovered water and for signaling malfunction in water recovery system. Monitor incorporates whenever possible commercially available sensors suitably modified.
Misselhorn, J. E.↗
Cooperating Expert Systems For Automated Monitoring And Diagnostics
Human operators freer to perform nonrepetitive tasks. Expert software system for monitoring multiple streams of sensory data from interacting equipment subsystems undergoing development. Organized hierarchically to monitor multiple equipment subsystems simultaneously. Each expert software subsystem responsible for one of equipment subsystems. Intended to assist human analysts in interpretation of telemetry data indicating operating statuses of subsystems of spacecraft, enables analysts to maintain desired mode(s) of operation, and helps to correct anomalies in subsystems or in overall spacecraft system. Other versions devised for industrial monitoring and analysis tasks similarly complicated.
Schwuttke, Ursula M.↗
Automated Monitoring Of Dielectric Properties Of Tree Trunks
Semiautomated instrumentation system called "dielectric monitoring system" (DMS) developed for measuring microwave permittivities of selected components of plants, in particular, of active xylems in tree trunks. System set up with coaxial probes inserted in tree trunks to measure dielectric properties. Can be left to operate unattended to gather data on permittivities as function of time.
Mcdonald, Kyle C.↗
Automation-induced monitoring inefficiency: role of display location
Operators can be poor monitors of automation if they are engaged concurrently in other tasks. However, in previous studies of this phenomenon the automated task was always presented in the periphery, away from the primary manual tasks that were centrally displayed. In this study we examined whether centrally locating an automated task would boost monitoring performance during a flight-simulation task consisting of system monitoring, tracking and fuel resource management sub-tasks. Twelve nonpilot subjects were required to perform the tracking and fuel management tasks manually while watching the automated system monitoring task for occasional failures. The automation reliability was constant at 87.5% for six subjects and variable (alternating between 87.5% and 56.25%) for the other six subjects. Each subject completed four 30 min sessions over a period of 2 days. In each automation reliability condition the automation routine was disabled for the last 20 min of the fourth session in order to simulate catastrophic automation failure (0 % reliability). Monitoring for automation failure was inefficient when automation reliability was constant but not when it varied over time, replicating previous results. Furthermore, there was no evidence of resource or speed accuracy trade-off between tasks. Thus, automation-induced failures of monitoring cannot be prevented by centrally locating the automated task.
Automation↗
Effects of adaptive task allocation on monitoring of automated systems
The effects of adaptive task allocation on monitoring for automation failure during multitask flight simulation were examined. Participants monitored an automated engine status task while simultaneously performing tracking and fuel management tasks over three 30-min sessions. Two methods of adaptive task allocation, both involving temporary return of the automated engine status task to the human operator ("human control"), were examined as a possible countermeasure to monitoring inefficiency. For the model-based adaptive group, the engine status task was allocated to all participants in the middle of the second session for 10 min, following which it was again returned to automation control. The same occurred for the performance-based adaptive group, but only if an individual participant's monitoring performance up to that point did not meet a specified criterion. For the nonadaptive control groups, the engine status task remained automated throughout the experiment. All groups had low probabilities of detection of automation failures for the first 40 min spent with automation. However, following the 10-min intervening period of human control, both adaptive groups detected significantly more automation failures during the subsequent blocks under automation control. The results show that adaptive task allocation can enhance monitoring of automated systems. Both model-based and performance-based allocation improved monitoring of automation. Implications for the design of automated systems are discussed.
Task Performance and Analysis↗
Remote Maintenance Monitoring
Automated system gives new life to aging network of computers. Remote maintenance monitoring system developed to diagnose problems in large distributed computer network. Consists of data links, displays, controls, software, and more than 200 computers. Uses sensors to collect data on failures and expert system to examine data, diagnose causes of failures, and recommend cures. Designed to be retrofitted into launch processing system at Kennedy Space Center. Reduces downtime, lowers workload and expense of maintenance, and makes network less dependent on human expertise.
Owens, Richard C.↗