SEARCH · Search NASA
Results for “alarm algorithm”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
SETI Pulse Detection Algorithm: Analysis of False-alarm Rates
Some earlier work by the Search for Extraterrestrial Intelligence (SETI) Science Working Group (SWG) on the derivation of spectrum analyzer thresholds for a pulse detection algorithm based on an analysis of false alarm rates is extended. The algorithm previously analyzed was intended to detect a finite sequence of i periodically spaced pulses that did not necessarily occupy the entire observation interval. This algorithm would recognize the presence of such a signal only if all i-received pulse powers exceeded a threshold T(i): these thresholds were selected to achieve a desired false alarm rate, independent of i. To simplify the analysis, it was assumed that the pulses were synchronous with the spectrum sample times. This analysis extends the earlier effort to include infinite and/or asynchronous pulse trains. Furthermore, to decrease the possibility of missing an extraterrestrial intelligence signal, the algorithm was modified to detect a pulse train even if some of the received pulse powers fall below the threshold. The analysis employs geometrical arguments that make it conceptually easy to incorporate boundary conditions imposed on the derivation of the false alarm rates. While the exact results can be somewhat complex, simple closed form approximations are derived that produce a negligible loss of accuracy.
Central alarm station interface for situation awareness
A method for analyzing security alarms is provided. The method comprising receiving alarm data from a number of security sensors generating alarms, wherein the alarm data includes spatio-temporal data regarding where and when an alarm is triggered. The alarm data is displayed on a user interface. The alarm data is analyzed for spatio-temporal relationships among the alarms according to a number of pattern recognition algorithms. The alarms are identified as genuine alarms if a discernable pattern is recognized among the alarms.
Field-based AFDD for refrigerant undercharge in residential HVAC systems: enhancing reliability through false alarm mitigation
This study evaluated rule-based and machine learning (ML) based automated fault detection and diagnostics (AFDD) algorithms for detecting refrigerant undercharge faults in residential heating, ventilation, and air conditioning (HVAC) systems, using actual building data and a minimal set of features. The ML-based algorithms included Decision Tree (DT) and K-Nearest Neighbors (KNN). Both the rule-based and ML-based algorithms demonstrated the capability to detect refrigerant undercharge faults of -30% or more. Both types of algorithms exhibited false alarms before the implementation of a false alarm mitigation algorithm, which motivated the development of such a mitigation strategy. After applying the mitigation, false alarms were substantially reduced, with the rule-based algorithm decreasing to 0.6% and the ML-based algorithms reaching 0%, while maintaining strong detection performance. Although the rule-based algorithm initially showed lower performance compared to the ML-based algorithms, its detection accuracy improved after mitigation to a level comparable to the ML-based algorithms. These results confirm that combining false alarm mitigation with both rule-based and ML-based AFDD algorithms significantly enhances practical reliability while preserving robust fault detection capabilities. Furthermore, the findings demonstrate the potential for field deployment of these algorithms in residential HVAC systems and highlight the importance of minimizing false alarms.
Competitive evaluation of failure detection algorithms for strapdown redundant inertial instruments
Algorithms for failure detection, isolation, and correction of redundant inertial instruments in the strapdown dodecahedron configuration are competitively evaluated in a digital computer simulation that subjects them to identical environments. Their performance is compared in terms of orientation and inertial velocity errors and in terms of missed and false alarms. The algorithms appear in the simulation program in modular form, so that they may be readily extracted for use elsewhere. The simulation program and its inputs and outputs are described. The algorithms, along with an eight algorithm that was not simulated, also compared analytically to show the relationships among them.
Three-phase Four-leg Inverter LabVIEW FPGA Control Code [SWR-16-26]
In the area of power electronics control, Field Programmable Gate Arrays (FPGAs) have the capability to outperform their Digital Signal Processor (DSP) counterparts due to the FPGA’s ability to implement true parallel processing and therefore facilitate higher switching frequencies, higher control bandwidth, and/or enhanced functionality. National Instruments (NI) has developed two platforms, Compact RIO (cRIO) and Single Board RIO (sbRIO), which combine a real-time processor with an FPGA. The FPGA can be programmed with a subset of the well-known LabVIEW graphical programming language. The use of cRIO and sbRIO for power electronics control has developed over the last few years to include control of three-phase inverters. Most three-phase inverter topologies include three switching legs. The addition of a fourth-leg to natively generate the neutral connection allows the inverter to serve single-phase loads in a microgrid or stand-alone power system and to balance the three-phase voltages in the presence of significant load imbalance. However, the control of a four-leg inverter is much more complex. In particular, instead of standard two-dimensional space vector modulation (SVM), the inverter requires three-dimensional space vector modulation (3D-SVM). The candidate software implements complete control algorithms in LabVIEW FPGA for a three-phase four-leg inverter. The software includes feedback control loops, three-dimensional space vector modulation gate-drive algorithms, advanced alarm handling capabilities, contactor control, power measurements, and debugging and tuning tools. The feedback control loops allow inverter operation in AC voltage control, AC current control, or DC bus voltage control modes based on external mode selection by a user or supervisory controller. The software includes the ability to synchronize its AC output to the grid or other voltage-source before connection. The software also includes provisions to allow inverter operation in parallel with other voltage regulating devices on the AC or DC buses. This flexibility allows the Inverter to operate as a stand-alone voltage source, connected to the grid, or in parallel with other controllable voltage sources as part of a microgrid or remote power system. In addition, as the inverter is expected to operate under severe unbalanced conditions, the software includes algorithms to accurately compute real and reactive power for each phase based on definitions provided in the IEEE Standard 1459: IEEE Standard Definitions for the Measurement of Electric Power Quantities Under Sinusoidal, Nonsinusoidal, Balanced, or Unbalanced Conditions. Finally, the software includes code to output analog signals for debugging and for tuning of control loops. The software fits on the Xilinx Virtex V LX110 FPGA embedded in the NI cRIO-9118 FPGA chassis, and with a 40 MHz base clock, supports a modulation update rate of 40 MHz, user-settable switching frequencies and synchronized control loop update rates of tens of kHz, and reference waveform generation, including Phase Lock Loop (PLL), update rate of 100 kHz.
List-Mode source injection algorithm for detectors with arbitrary pose and trajectory
environments, introducing actual sources is usually neither feasible nor economical. It is more practical to move the detector (whether airborne, vehicle-borne, or human-portable) through the test environment and then later artificially superimpose either measured or simulated source signatures on the recorded background data. We present a source injection (SI) algorithm that inserts source events into list-mode background data. It is designed to accommodate cases where “injection data” are available only at a limited set of locations. Stochastic methods are employed both to select source events from a measured dataset containing both source and background events, and to scale the number of selected events to match the field conditions of detector-source distance, air attenuation, acquisition duration, and the relative strength of the measured and injected sources. The algorithm is deployed as part of the Airborne Radiation Enhanced-sensor System (ARES) Advanced Technology Demonstration, and is applied to field data obtained using the ARES detector system. For this helicopter-borne detection system, we describe a practical ground-based measurement scheme for acquiring data for later injection. These data are then injected into background data acquired during flight, where arbitrary detector poses and trajectories are possible. Similar flight paths are traversed with and without actual 137Cs and 60Co sources present. SI is applied to the source-absent datasets, and the results are compared to the source-present datasets. SI output is found to be consistent with measured activity within the estimated uncertainties in GPS position and source strength.
Saberliner flight test for airborne wind shear forward looking detection and avoidance radar systems
Westinghouse conducted a flight test with its Sabreliner AN/APG-68 instrumented radar to assess the urban discrete/ground moving vehicle clutter environment. Glideslope approaches were flown into Washington National, BWI, and Georgetown, Delaware, airports employing radar mode timing, waveform, and processing configurations plausible for microburst windshear avoidance. The perceptions, both general and specific, of the clutter environment furnish an empirical foundation for beginning low false alarm detection algorithm development.
Deliberate Motion Analytics Fused Radar and Video Test Results Deployed Beyond the Perimeter Fence in a High Noise Environment
Security systems that protect the nation’s critical facilities must be capable of detecting physical intrusions in all weather conditions. Intrusion detection sensors in a perimeter with a high nuisance alarm rate (NAR) significantly undermine detection performance and degrade security system effectiveness. This research demonstrated a fused sensor system that can differentiate foliage and weather-induced nuisance alarms from those caused by intruders, providing reliable detection within a two-fence perimeter or beyond the fence. A key element of this work is the creation and application of a “deliberate motion algorithm” that fuses alarm data from radar and video analytics to create video motion detection fused radar system. The two-layer architecture of the algorithm uses machine learning, multi-hypothesis tracking, and Dynamic Bayes Nets to differentiate intruder alarms from weather induced alarms.
Evaluation of a fault tolerant system for an integrated avionics sensor configuration with TSRV flight data
The performance analysis results of a fault inferring nonlinear detection system (FINDS) using sensor flight data for the NASA ATOPS B-737 aircraft in a Microwave Landing System (MLS) environment is presented. First, a statistical analysis of the flight recorded sensor data was made in order to determine the characteristics of sensor inaccuracies. Next, modifications were made to the detection and decision functions in the FINDS algorithm in order to improve false alarm and failure detection performance under real modelling errors present in the flight data. Finally, the failure detection and false alarm performance of the FINDS algorithm were analyzed by injecting bias failures into fourteen sensor outputs over six repetitive runs of the five minute flight data. In general, the detection speed, failure level estimation, and false alarm performance showed a marked improvement over the previously reported simulation runs. In agreement with earlier results, detection speed was faster for filter measurement sensors soon as MLS than for filter input sensors such as flight control accelerometers.
Low-Complexity System and Algorithm for an Emergency Ventilator Sensor and Alarm
Not Available
Integrating Machine-learning-assisted Computer Vision with RICH System
Developments in artificial intelligence have vastly expanded the capabilities of robots. Currently, the Spallation Neutron Source (SNS) beamlines at Oak Ridge National Lab (ORNL) have robotic sample loaders to increase the efficiency of running experiments. However, they require retraining if anything about the situation changes, e.g., where the samples are, and cannot notice if errors occur. So, the viability of using computer vision and machine learning to enhance these sample loaders’ functionality was investigated. In this project, the RICH system with a Dobot CR3 6-axis robot present at the VULCAN beamline assisted by an Intel Realsense D435i camera, a unique camera that enables convenient translation of 2D pixel coordinates to 3D world points, was programmed to load ceramic crucibles into a thermogravimetric analyzer (TGA) furnace. An algorithm was constructed in Python with three major phases planned: (1) obtaining a sample, (2) moving it to the target location, and then (3) bringing the sample back to its original location once the experiment finished. In the first phase, the algorithm would dynamically detect sample locations using ArUco markers to recognize the samples’ general location and a custom-trained yolov5 object detection model to locate the crucibles’ centers. Afterward, the robot would be directed to pick up samples based on the crucibles’ calculated positions. In the second phase, the robot would move the sample to a secondary point, reorient its grip, and place the sample at the target location. In the final phase, the robot would determine whether the sample was intact and would bring it back to its original place if it was or raise an alarm. Using this algorithm, the robot was able to pick up different types of crucibles at varying positions. These results indicate that integrating machine-learning-assisted computer vision with robotic sample loaders can result in effective autonomous detection of samples.
Plural-wavelength flame detector that discriminates between direct and reflected radiation
A flame detector employs a plurality of wavelength selective radiation detectors and a digital signal processor programmed to analyze each of the detector signals, and determine whether radiation is received directly from a small flame source that warrants generation of an alarm. The processor's algorithm employs a normalized cross-correlation analysis of the detector signals to discriminate between radiation received directly from a flame and radiation received from a reflection of a flame to insure that reflections will not trigger an alarm. In addition, the algorithm employs a Fast Fourier Transform (FFT) frequency spectrum analysis of one of the detector signals to discriminate between flames of different sizes. In a specific application, the detector incorporates two infrared (IR) detectors and one ultraviolet (UV) detector for discriminating between a directly sensed small hydrogen flame, and reflections from a large hydrogen flame. The signals generated by each of the detectors are sampled and digitized for analysis by the digital signal processor, preferably 250 times a second. A sliding time window of approximately 30 seconds of detector data is created using FIFO memories.
Comparison Between GOES-12 Overshooting-Top Detections, WSR-88D Radar Reflectivity, and Severe Storm Reports
Studies have found that convective storms with overshooting-top (OT) signatures in weather satellite imagery are often associated with hazardous weather, such as heavy rainfall, tornadoes, damaging winds, and large hail. An objective satellite-based OT detection product has been developed using 11-micrometer infrared window (IRW) channel brightness temperatures (BTs) for the upcoming R series of the Geostationary Operational Environmental Satellite (GOES-R) Advanced Baseline Imager. In this study, this method is applied to GOES-12 IRW data and the OT detections are compared with radar data, severe storm reports, and severe weather warnings over the eastern United States. The goals of this study are to 1) improve forecaster understanding of satellite OT signatures relative to commonly available radar products, 2) assess OT detection product accuracy, and 3) evaluate the utility of an OT detection product for diagnosing hazardous convective storms. The coevolution of radar-derived products and satellite OT signatures indicates that an OT often corresponds with the highest radar echo top and reflectivity maximum aloft. Validation of OT detections relative to composite reflectivity indicates an algorithm false-alarm ratio of 16%, with OTs within the coldest IRW BT range (less than 200 K) being the most accurate. A significant IRW BT minimum typically present with an OT is more often associated with heavy precipitation than a region with a spatially uniform BT. Severe weather was often associated with OT detections during the warm season (April September) and over the southern United States. The severe weather to OT relationship increased by 15% when GOES operated in rapid-scan mode, showing the importance of high temporal resolution for observing and detecting rapidly evolving cloud-top features. Comparison of the earliest OT detection associated with a severe weather report showed that 75% of the cases occur before severe weather and that 42% of collocated severe weather reports had either an OT detected before a severe weather warning or no warning issued at all. The relationships between satellite OT signatures, severe weather, and heavy rainfall shown in this paper suggest that 1) when an OT is detected, the particular storm is likely producing heavy rainfall and/or possibly severe weather; 2) an objective OT detection product can be used to increase situational awareness and forecaster confidence that a given storm is severe; and 3) this product may be particularly useful in regions with insufficient radar coverage.
Multi-sensor analysis techniques for SSME safety monitoring
Two algorithms were developed which utilized multi-sensor analysis techniques to complement the current Space Shuttle Main Engine (SSME) safety monitoring system. The first algorithm analyzed the accumulative error between actual and predicted values of the engine parameter set, while the second algorithm combined these error terms into a response pattern and correlated each pattern with a standard pattern. These algorithms were applied to twelve SSME anomalous test firings and were found to produce improved failure detection times in eight of those twelve compared to the current engine safety monitoring system. Of the eight detected anomalous test firings, the first algorithm detected all eight, while the second algorithm detected seven of the eight. No false alarms were indicated by either algorithm for twelve nominal test firings. An initial parametric study of these algorithms for optimized parameter selection is presented and algorithm robustness to sensor failure is demonstrated.
Sensory redundancy management: The development of a design methodology for determining threshold values through a statistical analysis of sensor output data
Sensor redundancy management (SRM) requires a system which will detect failures and reconstruct avionics accordingly. A probability density function to determine false alarm rates, using an algorithmic approach was generated. Microcomputer software was developed which will print out tables of values for the cummulative probability of being in the domain of failure; system reliability; and false alarm probability, given a signal is in the domain of failure. The microcomputer software was applied to the sensor output data for various AFT1 F-16 flights and sensor parameters. Practical recommendations for further research were made.
Systematic study of variation in radiation data in cluttered environments
There are many challenges to overcome during nuclear nonproliferation measurements, including a wide variety of clutter sources that may be introduced into a scene during acquisition. The ability to understand how the dissimilarity of various objects, or clutter, obscures a detector’s field of view and affects the measured response of the detector improves the input to detection alarm and nuclide identification algorithms. Additionally, having the capability to input clutter variation into radiation transport models would allow for more real-world scenarios to be modeled and understood. Efforts at Oak Ridge National Laboratory have been exploring the ability to determine specific natural background radiation contributions from surroundings, such as buildings, soil, asphalt, and concrete, and use those source terms in large-scale (100 + m) models. The capability to quantify different clutter terms, ranging from humans to large vehicles, allows researchers to inject realistic noise into these models. The work reported herein seeks to explore the effects that clutter introduces to quantify the expected signal variation inherent to both controlled and uncontrolled scenarios, specifically how the introduction of clutter into a scene introduces a variation in signals due to the attenuation of naturally occurring radioactive material distributed within the scene or by introducing non-threatening radioactive materials into the scene. Controlled measurements aim to quantify how different types of clutter of varying size and composition affects the response of a radiation detector. Using results from various controlled tests, the systematic effects of clutter and its effect on radiation readings is analyzed.
A vector-based failure detection and isolation algorithm for a dual fail-operational redundant strapdown inertial measurement unit
A vector-based failure detection and isolation technique for a skewed array of two degree-of-freedom inertial sensors is developed. Failure detection is based on comparison of parity equations with a threshold, and isolation is based on comparison of logic variables which are keyed to pass/fail results of the parity test. A multi-level approach to failure detection is used to ensure adequate coverage for the flight control, display, and navigation avionics functions. Sensor error models are introduced to expose the susceptibility of the parity equations to sensor errors and physical separation effects. The algorithm is evaluated in a simulation of a commercial transport operating in a range of light to severe turbulence environments. A bias-jump failure level of 0.2 deg/hr was detected and isolated properly in the light and moderate turbulence environments, but not detected in the extreme turbulence environment. An accelerometer bias-jump failure level of 1.5 milli-g was detected over all turbulence environments. For both types of inertial sensor, hard-over, and null type failures were detected in all environments without incident. The algorithm functioned without false alarm or isolation over all turbulence environments for the runs tested.