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NASA NTRS2001

Obstacle Detection Algorithms for Rotorcraft Navigation

In this research we addressed the problem of obstacle detection for low altitude rotorcraft flight. In particular, the problem of detecting thin wires in the presence of image clutter and noise was studied. Wires present a serious hazard to rotorcrafts. Since they are very thin, their detection early enough so that the pilot has enough time to take evasive action is difficult, as their images can be less than one or two pixels wide. After reviewing the line detection literature, an algorithm for sub-pixel edge detection proposed by Steger was identified as having good potential to solve the considered task. The algorithm was tested using a set of images synthetically generated by combining real outdoor images with computer generated wire images. The performance of the algorithm was evaluated both, at the pixel and the wire levels. It was observed that the algorithm performs well, provided that the wires are not too thin (or distant) and that some post processing is performed to remove false alarms due to clutter.

Kasturi, Rangachar↗

NASA NTRS2011

Comparison of ASGARD and UFOCapture

The Meteoroid Environment Office is undertaking a comparison between UFOCapture/Analyzer and ASGARD (All Sky and Guided Automatic Realtime Detection). To accomplish this, video output from a Watec video camera on a 17 mm Schneider lens (25 degree field of view) was split and input into the two different meteor detection softwares. The purpose of this study is to compare the sensitivity of the two systems, false alarm rates and trajectory information, among other quantities. The important components of each software will be highlighted and comments made about the detection/rejection algorithms and the amount of user-labor required for each system.

Blaauw, Rhiannon C.↗

NASA NTRS2011

Semi-Supervised Novelty Detection with Adaptive Eigenbases, and Application to Radio Transients

We present a semi-supervised online method for novelty detection and evaluate its performance for radio astronomy time series data. Our approach uses adaptive eigenbases to combine 1) prior knowledge about uninteresting signals with 2) online estimation of the current data properties to enable highly sensitive and precise detection of novel signals. We apply the method to the problem of detecting fast transient radio anomalies and compare it to current alternative algorithms. Tests based on observations from the Parkes Multibeam Survey show both effective detection of interesting rare events and robustness to known false alarm anomalies.

anomaly detection↗

NASA NTRS2016

Empirical Analysis and Automated Classification of Security Bug Reports

With the ever expanding amount of sensitive data being placed into computer systems, the need for effective cybersecurity is of utmost importance. However, there is a shortage of detailed empirical studies of security vulnerabilities from which cybersecurity metrics and best practices could be determined. This thesis has two main research goals: (1) to explore the distribution and characteristics of security vulnerabilities based on the information provided in bug tracking systems and (2) to develop data analytics approaches for automatic classification of bug reports as security or non-security related. This work is based on using three NASA datasets as case studies. The empirical analysis showed that the majority of software vulnerabilities belong only to a small number of types. Addressing these types of vulnerabilities will consequently lead to cost efficient improvement of software security. Since this analysis requires labeling of each bug report in the bug tracking system, we explored using machine learning to automate the classification of each bug report as a security or non-security related (two-class classification), as well as each security related bug report as specific security type (multiclass classification). In addition to using supervised machine learning algorithms, a novel unsupervised machine learning approach is proposed. An ac- curacy of 92%, recall of 96%, precision of 92%, probability of false alarm of 4%, F-Score of 81% and G-Score of 90% were the best results achieved during two-class classification. Furthermore, an accuracy of 80%, recall of 80%, precision of 94%, and F-score of 85% were the best results achieved during multiclass classification.

Cybersecurity↗

DOE OSTI2019

Polarized Radar for Detection and Automatic Non-Visual Assessment of Unmanned Aerial Systems

This Laboratory Directed Research and Development (LDRD) effort performed fundamental Research and Development (R&D) to develop a robust radar processing algorithm capable of assessing the difference between an Unmanned Aerial System (UAS) and a biological target such as a bird, based on mathematics applied to the polarized radar returns of the target object, alone. The current threats of using such a UAS as a delivery platform for a host of destructive components is a major concern for the protection of various assets. Most recently, on 14th Sept. 2019, dozens of suicide or kamikaze drones (UAV-X) coordinated an attack on two Saudi oil facilities that demonstrated the potential to disrupt global oil supplies. While radar-based UAS detection systems can detect UAS at ranges greater than 1-km, the issues of excessive Nuisance/False Alarm Rates (NAR/FAR) from natural sources (birds in particular) has not been sufficiently addressed. In this effort we describe and utilize the Adaptive Polarization Difference Imaging-based (APDI) algorithms for the detection and automatic non-visual assessment of Unmanned Aerial System applications. Originally developed for optical imaging and sensing of polarization information in nature, the algorithms developed here are modified to serve for the target detection purposes in counter-UAS (cUAS) environments. We exploit the polarization statistics of the observing scene for detection and identification of changes within the scene and assess from these changes for UAS/bird classifications. Several cases are considered from independent data sources, including numerically generated data, anechoic chamber data as well as experimental radar data, to show the applicability of the techniques developed here. The methods developed in this effort are designed to be used in cUAS setups but have shown promise for a multitude of other radar-based classification uses as well.

47 OTHER INSTRUMENTATION↗

NASA NTRS1987

Performance analysis of a generalized upset detection procedure

A general procedure for upset detection in complex systems, called the data block capture and analysis upset monitoring process is described and analyzed. The process consists of repeatedly recording a fixed amount of data from a set of predetermined observation lines of the system being monitored (i.e., capturing a block of data), and then analyzing the captured block in an attempt to determine whether the system is functioning correctly. The algorithm which analyzes the data blocks can be characterized in terms of the amount of time it requires to examine a given length data block to ascertain the existence of features/conditions that have been predetermined to characterize the upset-free behavior of the system. The performance of linear, quadratic, and logarithmic data analysis algorithms is rigorously characterized in terms of three performance measures: (1) the probability of correctly detecting an upset; (2) the expected number of false alarms; and (3) the expected latency in detecting upsets.

Blough, Douglas M.↗

DOE OSTI2018

Self-Navigating Robotic Assistant for Long-term Wide Area Floor Contamination Monitoring - 18213

For Routine Contamination Testing (RCT) of an operational nuclear facility or one undergoing Decontamination and Decommissioning (D and D), workers utilize specified travel corridors (enclosed hallways or areas designated with tape or rope in open spaces) to minimize the risk of spreading contamination. Radiation health technicians must continually monitor these areas for contamination to minimize worker dose and prevent its spread to uncontrolled areas. For many sites, it is impractical for the technician to adequately survey such areas. The Nuclear Robotics Group (NRG) at UT Austin has developed a self-navigating robotic platform equipped with radiation detectors to augment the technician to improve both survey coverage and frequency. While maintaining a controlled velocity and floor-to-sensor distance, the system takes radiation measurements to identify and/or localize contamination sources. Readings above background levels are reported to a remote workstation to allow technicians to then respond accordingly. The completion of these mundane tasks by a robot allows the trained technicians to focus instead on operational tasks, D and D, or responding to alarms. The robot generates a map using a Lidar range sensor with established Simultaneous Localization and Mapping (SLAM) techniques. Range data is then compared to previously generated maps for robot localization and autonomous navigation. To maximize coverage, the robot executes a Complete Coverage Path Planning (CCPP) algorithm using a static map that is discretized into cells to assure comprehensive yet efficient coverage. As the system executes the generated plan, it builds a radiation map of the area for the technician to validate and verify the results. If contamination is detected, the robot will minimize the spread of contamination by stopping, sounding an alarm, and alerting the remote technician. The proposed system allows for long-term deployment with minimal human intervention. It is robust to both highly dynamic obstacles (i.e. personnel) and low dynamic changes (i.e. temporally stored cart) in the environment. This is accomplished through continuous spatiotemporal updating of the static map, and the utilization of a supervisory controller that allows the system to reliably respond to unexpected events or states (i.e. low battery, closed door, etc.). While the supervisory controller can respond to temporary complications, the system also recognizes the environment can change slowly over time necessitating that the CCPP automatically be updated to account for observed 'permanent' changes to the environment. To do this, the robot uses intermediate probabilistic data to make intelligent planning decisions offline while still maintaining local reactive obstacle avoidance and safety features. The self-updating of the map allows the robot to run longer without the need to remake the map, leading to more consistent monitoring data and less maintenance from the human technician. This paper will review the current system hardware, the CCPP algorithm, the supervisory controller, the probabilistic algorithm for identifying permanent changes, and recent experiments at UT Austin to experimentally validate the reported sensor coverage. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS2001

A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics

In this paper, a model-based diagnostic method, which utilizes Neural Networks and Genetic Algorithms, is investigated. Neural networks are applied to estimate the engine internal health, and Genetic Algorithms are applied for sensor bias detection and estimation. This hybrid approach takes advantage of the nonlinear estimation capability provided by neural networks while improving the robustness to measurement uncertainty through the application of Genetic Algorithms. The hybrid diagnostic technique also has the ability to rank multiple potential solutions for a given set of anomalous sensor measurements in order to reduce false alarms and missed detections. The performance of the hybrid diagnostic technique is evaluated through some case studies derived from a turbofan engine simulation. The results show this approach is promising for reliable diagnostics of aircraft engines.

Kobayashi, Takahisa↗

DOE OSTI2023

A bi-level data-driven framework for fault-detection and diagnosis of HVAC systems

Long-term operation of heating, ventilation, and air conditioning (HVAC) systems will eventually lead to a range of HVAC system failures, resulting in excessive energy consumption and maintenance costs. Here, to avoid HVAC malfunctioning, fault detection diagnostic (FDD) is utilized as a common practice. Machine learning methods have lately received considerable interest for FDD analysis of HVAC systems due to their high detection accuracy. Meanwhile, HVAC malfunctions are regarded as rare occurrences, hence normal operating data samples are much more accessible than data samples in faulty and malfunctioning conditions. The dominating frequency of normal operation in HVAC datasets has also led to heavily biased classification algorithms within the literature. Moreover, the focus of previous literature has been on increasing the accuracy of the models which leads to a high number of false positives (misleading alarms) in the system. In order to enhance the performance of diagnostic procedures and fill the mentioned gaps, this study proposes a novel data-driven framework. A bi-level machine learning framework is developed for diagnosing faults in air handling units (AHUs) and rooftop units (RTUs) based on principal component analysis (PCA), time series anomaly detection, and random forest (RF). It is shown that PCA can reduce the dataset dimension with one principal component accounting for 95% of data variance. Also, the random forest could classify the faults with 89% precision for single-zone AHU, 85% precision for RTU, and 79% for multi-zone AHU. By proposing this framework, three persistent challenges are addressed: (I) minimizing false positives; (II) accounting for data imbalance; and (III) normal condition monitoring of equipment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Trigger-DAQ and Slow Controls Systems in the Mu2e Experiment

The muon campus program at Fermilab includes the Mu2e experiment that will search for a charged-lepton flavor violating processes where a negative muon converts into an electron in the field of an aluminum nucleus, improving by four orders of magnitude the search sensitivity reached so far. Mu2e's Trigger and Data Acquisition System (TDAQ) uses otsdaq as its solution. Developed at Fermilab, otsdaq uses the artdaq DAQ framework and art analysis framework, under the-hood, for event transfer, filtering, and processing. otsdaq is an online DAQ software suite with a focus on flexibility and scalability, while providing a multi-user, web-based, interface accessible through the Chrome or Firefox web browser. The detector Read Out Controller (ROC), from the tracker and calorimeter, stream out zero-suppressed data continuously to the Data Transfer Controller (DTC). Data is then read over the PCIe bus to a software filter algorithm that selects events which are finally combined with the data flux that comes froma Cosmic Ray Ve to System (CRV). A Detector Control System (DCS) for monitoring, controlling, alarming, and archiving has been developed using the Experimental Physics and Industrial Control System (EPICS) Open Source Platform. The DCS System has also been itegrated into otsdaq. The installation of the TDAQ and the DCS systems in the Mu2e building is planned for 2021-2022, and a prototype has been built at Fermilab's Feynman Computing Center. We report here on the developments and achievements of the integration of Mu2e's DCS system into the online otsdaq software.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS1982

An adaptive tracking observer for failure-detection systems

The design problem of adaptive observers applied to linear, constant and variable parameters, multi-input, multi-output systems, is considered. It is shown that, in order to keep the observer's (or Kalman filter) false-alarm rate (FAR) under a certain specified value, it is necessary to have an acceptable proper matching between the observer (or KF) model and the system parameters. An adaptive observer algorithm is introduced in order to maintain desired system-observer model matching, despite initial mismatching and/or system parameter variations. Only a properly designed adaptive observer is able to detect abrupt changes in the system (actuator, sensor failures, etc.) with adequate reliability and FAR. Conditions for convergence for the adaptive process were obtained, leading to a simple adaptive law (algorithm) with the possibility of an a priori choice of fixed adaptive gains. Simulation results show good tracking performance with small observer output errors and accurate and fast parameter identification, in both deterministic and stochastic cases.

Sidar, M.↗

NASA NTRS2010

Integrity Monitoring of Mercury Discharge Lamps

Mercury discharge lamps are critical in many trapped ion frequency standard applications. An integrity monitoring system can be implemented using end-of-life signatures observed in operational mercury discharge lamps, making it possible to forecast imminent failure and to take action to mitigate the consequences (such as switching to a redundant system). Mercury lamps are used as a source of 194-nm ultraviolet radiation for optical pumping and state selection of mercury trapped ion frequency standards. Lamps are typically fabricated using 202Hg distilled into high-purity quartz, or other 194-nm transmitting material (e.g., sapphire). A buffer gas is also placed into the bulb, typically a noble gas such as argon, neon, or krypton. The bulbs are driven by strong RF fields oscillating at .200 MHz. The lamp output may age over time by two internal mechanisms: (1) the darkening of the bulb that attenuates light transmission and (2) the loss of mercury due to migration or chemical interactions with the bulb surface. During fabrication, excess mercury is placed into a bulb, so that the loss rate is compensated with new mercury emanating from a cool tip or adjacent reservoir. The light output is nearly constant or varies slightly at a constant rate for many months/years until the mercury source is depleted. At this point, the vapor pressure abruptly falls and the total light output and atomic clock SNR (signal-to-noise ratio) decrease. After several days to weeks, the light levels decrease to a point where the atomic clock SNR is no longer sufficient to stay in lock, or the lamp self-extinguishes. This signature has been observed in four separate end-of-life lamp failures while operating in the Deep Space Network (DSN). A simple integrator circuit can observe and document steady-state lamp behavior. When the light levels drop over a predetermined time interval by a specified amount (e.g., 20 percent), an alarm is set. For critical operational applications, such as the DSN or in space flight, this warning provides notice that a failure may be imminent, and for operators or control algorithm to take action.

Tjoelker, Robert L.↗

NASA NTRS1982

Design of analytical failure detection using secondary observers

The problem of designing analytical failure-detection systems (FDS) for sensors and actuators, using observers, is addressed. The use of observers in FDS is related to the examination of the n-dimensional observer error vector which carries the necessary information on possible failures. The problem is that in practical systems, in which only some of the components of the state vector are measured, one has access only to the m-dimensional observer-output error vector, with m or = to n. In order to cope with these cases, a secondary observer is synthesized to reconstruct the entire observer-error vector from the observer output error vector. This approach leads toward the design of highly sensitive and reliable FDS, with the possibility of obtaining a unique fingerprint for every possible failure. In order to keep the observer's (or Kalman filter) false-alarm rate under a certain specified value, it is necessary to have an acceptable matching between the observer (or Kalman filter) models and the system parameters. A previously developed adaptive observer algorithm is used to maintain the desired system-observer model matching, despite initial mismatching or system parameter variations. Conditions for convergence for the adaptive process are obtained, leading to a simple adaptive law (algorithm) with the possibility of an a priori choice of fixed adaptive gains. Simulation results show good tracking performance with small observer output errors, while accurate and fast parameter identification, in both deterministic and stochastic cases, is obtained.

Sisar, M.↗

NASA NTRS2007

Algorithm-Based Fault Tolerance for Numerical Subroutines

A software library implements a new methodology of detecting faults in numerical subroutines, thus enabling application programs that contain the subroutines to recover transparently from single-event upsets. The software library in question is fault-detecting middleware that is wrapped around the numericalsubroutines. Conventional serial versions (based on LAPACK and FFTW) and a parallel version (based on ScaLAPACK) exist. The source code of the application program that contains the numerical subroutines is not modified, and the middleware is transparent to the user. The methodology used is a type of algorithm- based fault tolerance (ABFT). In ABFT, a checksum is computed before a computation and compared with the checksum of the computational result; an error is declared if the difference between the checksums exceeds some threshold. Novel normalization methods are used in the checksum comparison to ensure correct fault detections independent of algorithm inputs. In tests of this software reported in the peer-reviewed literature, this library was shown to enable detection of 99.9 percent of significant faults while generating no false alarms.

Tumon, Michael↗

DOE OSTI2017

Simplex ACE: a constrained subspace detector

In hyperspectral target detection, one must contend with variability in both target materials and background clutter. While most algorithms focus on the background clutter, there are some materials for which there is substantial variability in the signatures of the target. When multiple signatures can be used to describe a target material, subspace detectors are often the detection algorithm of choice. However, as the number of variable target spectra increases, so does the size of the target subspace spanned by these spectra, which in turn increases the number of false alarms. Here in this paper, we propose a modification to this approach, wherein the target subspace is instead a constrained subspace, or a simplex without the sum-to-one constraint. We derive the simplex adaptive matched filter (simplex AMF) and the simplex adaptive cosine estimator (simplex ACE), which are constrained basis adaptations of the traditional subspace AMF and subspace ACE detectors. We present results using simplex AMF and simplex ACE for variable targets, and compare their performances against their subspace counterparts. Our primary interest is in the simplex ACE detector, and as such, the experiments herein seek to evaluate the robustness of simplex ACE, with simplex AMF included for comparison. Results are shown on hyperspectral images using both implanted and ground-truthed targets, and demonstrate the robustness of simplex ACE to target variability.

42 ENGINEERING↗

NASA NTRS1995

Improved Noise-Power Estimators Based On Order Statistics

Technique based on order statistics enables design of improved noise-power estimators. In original intended application, noise-power estimators part of microwave-signal-processing system of Search for Extraterrestrial Intelligence project. Involves limiting dynamic range of value to be estimated; making it possible to achieve performance of order-statistical estimator with simple algorithms and equipment and with only one pass over input data. Technique also applicable to other signal-detection systems and to image-detection systems required to exhibit constant false-alarm rates.

Zimmerman, George A.↗

DOE OSTI2021

Data acquisition and slow control interface for the Mu2e experiment

The Mu2e experiment at the Fermilab Muon Campus will search for the coherent neutrinoless conversion of a muon into an electron in the field of an aluminum nucleus with a sensitivity improvement by a factor of 10000 over existing limits. The Mu2e Trigger and Data Acquisition System (TDAQ) uses otsdaq as the online Data Acquisition System (DAQ) solution. Developed at Fermilab, otsdaq integrates both the artdaq DAQ and the art analysis frameworks for event transfer, filtering, and processing. otsdaq is an online DAQ software suite with a focus on flexibility and scalability and provides a multi-user, web-based, interface accessible through a web browser. The data stream from the detector subsystems is read by a software filter algorithm that selects events which are combined with the data flux coming from a cosmic ray veto system. The Detector Control System (DCS) has been developed using the Experimental Physics and Industrial Control System (EPICS) open source platform for monitoring, controlling, alarming, and archiving. The DCS system has been integrated into otsdaq. A prototype of the TDAQ and the DCS systems has been built at Fermilab's Feynman Computing Center. In this study, we report on the progress of the integration of this prototype in the online otsdaq software.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS2002

Towards Comprehensive Variation Models for Designing Vehicle Monitoring Systems

When designing vehicle vibration monitoring systems for aerospace devices, it is common to use well-established models of vibration features to determine whether failures or defects exist. Most of the algorithms used for failure detection rely on these models to detect significant changes in a flight environment. In actual practice, however, most vehicle vibration monitoring systems are corrupted by high rates of false alarms and missed detections. This crucial roadblock makes their implementation in real vehicles (e.g., helicopter transmissions and aircraft engines) difficult, making their operation costly and unreliable. Research conducted at the NASA Ames Research Center has determined that a major reason for the high rates of false alarms and missed detections is the numerous sources of statistical variations that are not taken into account in the modeling assumptions. In this paper, we address one such source of variations, namely, those caused during the design and manufacturing of rotating machinery components that make up aerospace systems. We present a novel way of modeling the vibration response by including design variations via probabilistic methods. Using such models, we develop a methodology to account for design and manufacturing variations, and explore the changes in the vibration response to determine its stochastic nature. We explore the potential of the methodology using a nonlinear cam-follower model, where the spring stiffness values are assumed to follow a normal distribution. The results demonstrate initial feasibility of the method, showing great promise in developing a general methodology for designing more accurate aerospace vehicle monitoring systems.

McAdams, Daniel A.↗