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NASA NTRS2010

Anomaly Detection for Next-Generation Space Launch Ground Operations

NASA is developing new capabilities that will enable future human exploration missions while reducing mission risk and cost. The Fault Detection, Isolation, and Recovery (FDIR) project aims to demonstrate the utility of integrated vehicle health management (IVHM) tools in the domain of ground support equipment (GSE) to be used for the next generation launch vehicles. In addition to demonstrating the utility of IVHM tools for GSE, FDIR aims to mature promising tools for use on future missions and document the level of effort - and hence cost - required to implement an application with each selected tool. One of the FDIR capabilities is anomaly detection, i.e., detecting off-nominal behavior. The tool we selected for this task uses a data-driven approach. Unlike rule-based and model-based systems that require manual extraction of system knowledge, data-driven systems take a radically different approach to reasoning. At the basic level, they start with data that represent nominal functioning of the system and automatically learn expected system behavior. The behavior is encoded in a knowledge base that represents "in-family" system operations. During real-time system monitoring or during post-flight analysis, incoming data is compared to that nominal system operating behavior knowledge base; a distance representing deviation from nominal is computed, providing a measure of how far "out of family" current behavior is. We describe the selected tool for FDIR anomaly detection - Inductive Monitoring System (IMS), how it fits into the FDIR architecture, the operations concept for the GSE anomaly monitoring, and some preliminary results of applying IMS to a Space Shuttle GSE anomaly.

Spirkovska, Lilly↗

NASA NTRS2010

Investigation of Surface Flux Feedbacks for Coupled Atmosphere-Ocean Anomalies

The use of "dynamical coupling" rules allows for identifying coupled vs. uncoupled anomalies and one-way interaction. Results of this study are consistent with those of Pena et al. (2003,2004) although using a more recent reanalysis at higher resolution. Find more atmosphere-forcing coupled anomalies in the extratropics and ocean-forcing anomalies in the tropics. The LHF and SWR show the largest magnitude anomalies in the composite analysis. The turbulent flux responses are due to interactions between the differing responses in wind speed and near-surface gradients. The radiative fluxes responses are primarily tied to changes in cloud fraction, as expected, though longwave response can be tied more to changes in the upwelling component.

Roberts, J. Brent↗

NASA NTRS2011

Lunar Ion Transport Near Magnetic Anomalies: Possible Implications for Swirl Formation

The bright swirling features on the lunar surface in areas around the Moon but most prominently at Reiner Gamma, have intrigued scientists for many years. After Apollo and later Lunar Prospector (LP} mapped the Lunar magnetic fields from orbit, it was observed that these features are generally associated with crustal magnetic anomalies. This led researchers to propose a number of explanations for the swirls that invoke these fields. Prominent among these include magnetic shielding in the form of a mini-magnetosphere which impedes space weathering by the solar wind, magnetically controlled dust transport, and cometary or asteroidal impacts that would result in shock magnetization with concomitant formation ofthe swirls. In this presentation, we will consider another possibility, that the ambient magnetic and electric fields can transport and channel secondary ions produced by micrometeorite or solar wind ion impacts. In this scenario, ions that are created in these impacts are under the influence of these fields and can drift for significant distances before encountering the magnetic anomalies when their trajectories are disrupted and concentrated onto nearby areas. These ions may then be responsible for chemical alteration of the surface leading either to a brightening effect or a disruption of space weathering processes. To test this hypothesis we have run ion trajectory simulations that show ions from regions about the magnetic anomalies can be channeled into very small areas near the anomalies and although questions remain as to nature of the mechanisms that could lead to brightening of the surface it appears that the channeling effect is consistent with the existence of the swirls.

Keller, J. W.↗

NASA NTRS2011

Geopotential Field Anomaly Continuation with Multi-Altitude Observations

Conventional gravity and magnetic anomaly continuation invokes the standard Poisson boundary condition of a zero anomaly at an infinite vertical distance from the observation surface. This simple continuation is limited, however, where multiple altitude slices of the anomaly field have been observed. Increasingly, areas are becoming available constrained by multiple boundary conditions from surface, airborne, and satellite surveys. This paper describes the implementation of continuation with multi-altitude boundary conditions in Cartesian and spherical coordinates and investigates the advantages and limitations of these applications. Continuations by EPS (Equivalent Point Source) inversion and the FT (Fourier Transform), as well as by SCHA (Spherical Cap Harmonic Analysis) are considered. These methods were selected because they are especially well suited for analyzing multi-altitude data over finite patches of the earth such as covered by the ADMAP database. In general, continuations constrained by multi-altitude data surfaces are invariably superior to those constrained by a single altitude data surface due to anomaly measurement errors and the non-uniqueness of continuation.

Kim, Jeong Woo↗

NASA NTRS2012

Anomaly Detection in Test Equipment via Sliding Mode Observers

Nonlinear observers were originally developed based on the ideas of variable structure control, and for the purpose of detecting disturbances in complex systems. In this anomaly detection application, these observers were designed for estimating the distributed state of fluid flow in a pipe described by a class of advection equations. The observer algorithm uses collected data in a piping system to estimate the distributed system state (pressure and velocity along a pipe containing liquid gas propellant flow) using only boundary measurements. These estimates are then used to further estimate and localize possible anomalies such as leaks or foreign objects, and instrumentation metering problems such as incorrect flow meter orifice plate size. The observer algorithm has the following parts: a mathematical model of the fluid flow, observer control algorithm, and an anomaly identification algorithm. The main functional operation of the algorithm is in creating the sliding mode in the observer system implemented as software. Once the sliding mode starts in the system, the equivalent value of the discontinuous function in sliding mode can be obtained by filtering out the high-frequency chattering component. In control theory, "observers" are dynamic algorithms for the online estimation of the current state of a dynamic system by measurements of an output of the system. Classical linear observers can provide optimal estimates of a system state in case of uncertainty modeled by white noise. For nonlinear cases, the theory of nonlinear observers has been developed and its success is mainly due to the sliding mode approach. Using the mathematical theory of variable structure systems with sliding modes, the observer algorithm is designed in such a way that it steers the output of the model to the output of the system obtained via a variety of sensors, in spite of possible mismatches between the assumed model and actual system. The unique properties of sliding mode control allow not only control of the model internal states to the states of the real-life system, but also identification of the disturbance or anomaly that may occur.

Solano, Wanda M.↗

NASA NTRS2012

Geopotential Field Anomaly Continuation with Multi-Altitude Observations

Kim, Jeong Woo↗

NASA NTRS2012

Interpretation of the Total Magnetic Field Anomalies Measured by the CHAMP Satellite Over a Part of Europe and the Pannonian Basin

In this study we interpret the magnetic anomalies at satellite altitude over a part of Europe and the Pannonian Basin. These anomalies are derived from the total magnetic measurements from the CHAMP satellite. The anomalies reduced to an elevation of 324 km. An inversion method is used to interpret the total magnetic anomalies over the Pannonian Basin. A three dimensional triangular model is used in the inversion. Two parameter distributions: Laplacian and Gaussian are investigated. The regularized inversion is numerically calculated with the Simplex and Simulated Annealing methods and the anomalous source is located in the upper crust. A probable source of the magnetization is due to the exsolution of the hematite-ilmenite minerals.

Kis, K. I.↗

NASA NTRS2012

Experimental Investigation into the Radar Anomalies on the Surface of Venus

Radar mapping of thc surface of Venus shows areas of high reflectivity (low emissivity) in the Venusian highlands at altitudes between 2.5-4.75 kilometers. The origin of the radar anomalies found in the Venusian highlands remains unclear. Most explanations of the potential causes for these radar anomalies come from theoretical work. Previous studies suggest increased surface roughness or materials with higher dielectric constants as well as surface atmospheric interactions. Several possible candidates of high-dielectric materials are tellurium) ferroelectric materials, and lead or bismuth sulfides. While previous studies have been influential in determining possible sources for the Venus anomalies, only a very few hypotheses have been verified via experimentation. This work intends to experimentally constrain the source of the radar anomalies on Venus. This study proposes to investigate four possible materials that could potentially cause the high reflectivities on the surface of Venus and tests their behavior under simulated Venusian conditions.

Kohler, E.↗

NASA NTRS2012

CloudSat Anomaly Recovery and Operational Lessons Learned

In April 2011, NASA's pioneering cloud profiling radar satellite, CloudSat, experienced a battery anomaly that placed it into emergency mode and rendered it operations incapable. All initial attempts to recover the spacecraft failed as the resultant power limitations could not support even the lowest power mode. Originally part of a six-satellite constellation known as the "A-Train", CloudSat was unable to stay within its assigned control box, posing a threat to other A-Train satellites. CloudSat needed to exit the constellation, but with the tenuous power profile, conducting maneuvers was very risky. The team was able to execute a complex sequence of operations which recovered control, conducted an orbit lower maneuver, and returned the satellite to safe mode, within one 65 minute sunlit period. During the course of the anomaly recovery, the team developed several bold, innovative operational strategies. Details of the investigation into the root-cause and the multiple approaches to revive CloudSat are examined. Satellite communication and commanding during the anomaly are presented. A radical new system of "Daylight Only Operations" (DO-OP) was developed, which cycles the payload and subsystem components off in tune with earth eclipse entry and exit in order to maintain positive power and thermal profiles. The scientific methodology and operational results behind the graduated testing and ramp-up to DO-OP are analyzed. In November 2011, the CloudSat team successfully restored the vehicle to consistent operational collection of cloud radar data during sunlit portions of the orbit. Lessons learned throughout the six-month return-to-operations recovery effort are discussed and offered for application to other R&D satellites, in the context of on-orbit anomaly resolution efforts.

cloud profiling radar satellite↗

NASA NTRS2012

Particle Filtering for Model-Based Anomaly Detection in Sensor Networks

A novel technique has been developed for anomaly detection of rocket engine test stand (RETS) data. The objective was to develop a system that postprocesses a csv file containing the sensor readings and activities (time-series) from a rocket engine test, and detects any anomalies that might have occurred during the test. The output consists of the names of the sensors that show anomalous behavior, and the start and end time of each anomaly. In order to reduce the involvement of domain experts significantly, several data-driven approaches have been proposed where models are automatically acquired from the data, thus bypassing the cost and effort of building system models. Many supervised learning methods can efficiently learn operational and fault models, given large amounts of both nominal and fault data. However, for domains such as RETS data, the amount of anomalous data that is actually available is relatively small, making most supervised learning methods rather ineffective, and in general met with limited success in anomaly detection. The fundamental problem with existing approaches is that they assume that the data are iid, i.e., independent and identically distributed, which is violated in typical RETS data. None of these techniques naturally exploit the temporal information inherent in time series data from the sensor networks. There are correlations among the sensor readings, not only at the same time, but also across time. However, these approaches have not explicitly identified and exploited such correlations. Given these limitations of model-free methods, there has been renewed interest in model-based methods, specifically graphical methods that explicitly reason temporally. The Gaussian Mixture Model (GMM) in a Linear Dynamic System approach assumes that the multi-dimensional test data is a mixture of multi-variate Gaussians, and fits a given number of Gaussian clusters with the help of the wellknown Expectation Maximization (EM) algorithm. The parameters thus learned are used for calculating the joint distribution of the observations. However, this GMM assumption is essentially an approximation and signals the potential viability of non-parametric density estimators. This is the key idea underlying the new approach.

Solano, Wanda↗

NASA NTRS2012

Toward Baseline Software Anomalies in NASA Missions

In this fast abstract, we provide preliminary findings an analysis of 14,500 spacecraft anomalies from unmanned NASA missions. We provide some baselines for the distributions of software vs. non-software anomalies in spaceflight systems, the risk ratings of software anomalies, and the corrective actions associated with software anomalies.

anonaly↗

NASA NTRS2016

Method of Mapping Anomalies in Homogenous Material

An electrical conductor and antenna are positioned in a fixed relationship to one another. Relative lateral movement is generated between the electrical conductor and a homogenous material while maintaining the electrical conductor at a fixed distance from the homogenous material. The antenna supplies a time-varying magnetic field that causes the electrical conductor to resonate and generate harmonic electric and magnetic field responses. Disruptions in at least one of the electric and magnetic field responses during this lateral movement are indicative of a lateral location of a subsurface anomaly. Next, relative out-of-plane movement is generated between the electrical conductor and the homogenous material in the vicinity of the anomaly's lateral location. Disruptions in at least one of the electric and magnetic field responses during this out-of-plane movement are indicative of a depth location of the subsurface anomaly. A recording of the disruptions provides a mapping of the anomaly.

Woodard, Stanley E.↗

NASA NTRS2020

Unsupervised Anomaly Detection in High-Dimensional Flight Data Using Convolutional Variational Auto-Encoder

The modern National Airspace System (NAS) is an extremely safe system and the aviation industry has experienced a steady decrease in fatalities over the years. This can be attributed to both improved flight critical systems with redundant hardware and software protections, as well as an increased focus on active monitoring and response to real time and historically identified vulnerabilities by implementing more resilient procedures and protocols. The main approach for identifying vulnerabilities in operations leverages domain expertise using knowledge about how the system should behave within the expected tolerances to known safety margins. This approach works well when the system has a well-defined operating condition. However, the operations in the NAS can be highly complex with various nuances that render it difficult to clearly pre-define all known safety vulnerabilities. With the advancement of data science and machine learning techniques, the potential to automatically identify emerging vulnerabilities in the observed operations has become more practical in recent years. The state-of-the-art anomaly detection approaches in aerospace data usually rely on supervised or semi-supervised learning. However, in many real-world problems such as flight safety, creating labels for the data requires huge amount of effort and is largely impractical. To address this challenge, we developed a Convolutional Variational Auto-Encoder (CVAE), which is an unsupervised learning approach for anomaly detection in high-dimensional heterogeneous time-series data. We validate performance of CVAE compared to the state-of-the-art supervised learning approach as well as unsupervised clustering-based approach using KMeans++ and kernel-based approach using One-Class Support Vector Machine (OC-SVM) on Yahoo!'s benchmark time series anomaly detection data. Finally, we showcase performance of CVAE on a case study of identifying anomalies in the first 60 seconds of commercial flights' take-offs using Flight Operational Quality Assurance (FOQA) data.

Memarzadeh, Milad↗

NASA NTRSDate not supplied

Unsupervised Anomaly Detection in High-Dimensional Flight Data Using Convolutional Variational Auto-Encoder

The modern National Airspace System (NAS) is an extremely safe system. The industry has experienced a steady decrease in fatalities over the years. This can be contributed to both improved flight critical systems with redundant hardware and software protections as well as an increased focus on active monitoring and response to real time and historically identified vulnerabilities by implementing more resilient procedures and protocols. The main practice for identifying vulnerabilities in operations leverages domain expertise using knowledge about how the system should behave with the expected tolerances to known safety margins. This approach works well when the system has a well-defined operating condition. However, the operations in the NAS can be highly complex with various nuances that render it difficult to clearly pre-define all known safety vulnerabilities. With the advancement of data science and machine learning techniques, the potential to automatically identify emerging vulnerabilities in the observed operations has become more practical in recent years. The state-of-the-art anomaly detection approaches in aerospace data usually rely on supervised or semi-supervised learning. However, in many real-world problems such as flight safety creating labels for the data requires huge amount of efforts and is largely expensive. As a result, in this article, we develop a Convolutional Variational Auto-Encoder (CVAE), an unsupervised learning approach for anomaly detection in high-dimensional heterogeneous time-series data. We validate performance of CVAE compared to the state-of-the-art supervised learning approach (as an upper bound) as well as an supervised clustering based on K-Means (as a lower bound) on Yahoo!'s benchmark time series anomaly detection data. Finally, we showcase performance of CVAE on a case study of identifying anomalies in the first 60 seconds of commercial flights' take-offs using Flight Operational Quality Assurance (FOQA) data.

Milad Memarzadeh↗

NASA NTRS2008

The Mars climate sounder in-flight positioning anomaly

The paper discusses the Mars Climate Sounder (MCS) instrument’s in-flight positioning errors and presents background material about it. A short overview of the instrument’s science objectives and data acquisition techniques is provided. The brief mechanical description familiarizes the reader with the MCS instrument. Several key items of the flight qualification program, which had a rigorous joint drive test program but some limitations in overall system testing, are discussed. Implications this might have had for the flight anomaly, which began after several months of flawless space operation, are mentioned. The detection, interpretation, and instrument response to the errors is discussed. The anomaly prompted engineering reviews, renewed ground, and some in-flight testing. A summary of these events, including a timeline, is included. Several items of concern were uncovered during the anomaly investigation, the root cause, however, was never found. The instrument is now used with two operational constraints that work around the anomaly. It continues science gathering at an only slightly diminished pace that will yield approximately 90% of the originally intended science.

Kass, David↗

NASA NTRS2023

Testing Cosmic Microwave Background Anomalies in E-mode Polarization with Current and Future Data

In this paper, we explore the power of the cosmic microwave background (CMB) polarization (E-mode) data to corroborate four potential anomalies in CMB temperature data: the lack of large angular-scale correlations, the alignment of the quadrupole and octupole (Q–O), the point-parity asymmetry, and the hemispherical power asymmetry. We use CMB simulations with noise representative of three experiments—the Planck satellite, the Cosmology Large Angular Scale Surveyor (CLASS), and the LiteBIRD satellite—to test how current and future data constrain the anomalies. We find the correlation coefficients ρ between temperature and E-mode estimators to be less than 0.1, except for the point-parity asymmetry (ρ = 0.17 for cosmic-variance-limited simulations), confirming that E-modes provide a check on the anomalies that is largely independent of temperature data. Compared to Planck component-separated CMB data (smica), the putative LiteBIRD survey would reduce errors on E-mode anomaly estimators by factors of ∼3 for hemispherical power asymmetry and point-parity asymmetry, and by ∼26 for lack of large-scale correlation. The improvement in Q–O alignment is not obvious due to large cosmic variance, but we found the ability to pin down the estimator value will be improved by a factor ≳100. Improvements with CLASS are intermediate to these.

Cosmic microwave background radiation↗

DOE OSTI2022

Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection

In the era of big astronomical surveys, our ability to leverage artificial intelligence algorithms simultaneously for multiple datasets will open new avenues for scientific discovery. Unfortunately, simply training a deep neural network on images from one data domain often leads to very poor performance on any other dataset. Here we develop a Universal Domain Adaptation method DeepAstroUDA, capable of performing semi-supervised domain alignment that can be applied to datasets with different types of class overlap. Extra classes can be present in any of the two datasets, and the method can even be used in the presence of unknown classes. For the first time, we demonstrate the successful use of domain adaptation on two very different observational datasets (from SDSS and DECaLS). We show that our method is capable of bridging the gap between two astronomical surveys, and also performs well for anomaly detection and clustering of unknown data in the unlabeled dataset. We apply our model to two examples of galaxy morphology classification tasks with anomaly detection: 1) classifying spiral and elliptical galaxies with detection of merging galaxies (three classes including one unknown anomaly class); 2) a more granular problem where the classes describe more detailed morphological properties of galaxies, with the detection of gravitational lenses (ten classes including one unknown anomaly class).

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Classification based anomaly detection

An example device includes processing circuitry and a memory. The memory includes instructions that cause the device to perform various functions. The functions include receiving datastreams from a plurality of sensors of a high performance computing system, classifying each datastream of the each sensor to one of a plurality of datastream models, selecting an anomaly detection algorithm from a plurality of anomaly detection algorithms for each datastream, determining parameters of the each anomaly detection algorithm, determining an anomaly threshold for each datastream, and generating an indication that the sensor associated with the datastream is acting anomalously.

Serebryakov, Sergey↗