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Application of Topological Data Analysis to Multi-Resolution Matching and Anomaly Detection
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Vibration Anomaly Detection by Clustering in Unmanned Aerial Vehicles.
One of the critical factors affecting flight safety of unmanned aerial vehicles (UAVs) is the amount of vibration they are exposed to during a flight. On one hand, external causes such as wind gusts and turbulences or internal vehicle-centric faults such as incorrect sensor mounting or propeller imbalances can cause high vibrations in UAVs. On the other hand, high vibration itself may induce noise in the onboard miniature sensors of the UAV such as its accelerometers, gyroscopes and GPS that can lead to uncertain state estimation causing the multi-rotor to drift from its desired position or even result in loss-of-control. Hence, it is important to monitor the vibration levels during a UAV flight. This paper specifically looks into vibrations recorded by the autopilot system of a multi-rotor in presence of varying magnitudes of wind. Using data from experimental flights conducted at two separate flight test regions under varying wind conditions, we aim to classify between a nominal and anomalous vibration level for small UAV systems. Further, we analyse other parameters of interest that affect vibrations in UAVs such as UAV air speed and any propeller imbalance signatures. Analysis results from experimental flights demonstrate the effect of wind on vibration magnitude in unmanned aircrafts.
Enabling Intelligent Data Downlink Prioritization of In-Situ Observations through Generalizable and Computationally Inexpensive Anomaly Detection
High-fidelity measurements of magnetic fields and other observed properties, such as energetic particle fluxes, are a necessary component to our understanding of the highly dynamic near-Earth space environment. As our desire to study smaller-scale phenomena such as shocks and dipolorizations has increased, we have been driven to take and telemeter measurements at higher cadences. Unfortunately, many missions are unable to downlink all their captured data due to the well-known data transmission bottleneck at the DSN. These missions must then prioritize their high-cadence data such that the most scientifically useful intervals are transmitted. One simple prioritization technique uses the spacecraft position to telemeter data from only the region of interest. Although easy to implement, this method does not leverage the available scientific data and can omit intervals of useful scientific data when they lie outside the region of interest. The Magnetospheric Multiscale Mission (MMS) uses mission-specific parameterization of several data products to automatically prioritize scientifically useful intervals. Then, MMS verifies the automatically selected intervals by having a domain expert manually select intervals for downlink. The overall complexity required by this technique make it prohibitive for deployment on low-cost platforms (i.e., CubeSats) or on future missions featuring large constellations of satellites such as the Geospace Dynamics Constellation (GDC). We present preliminary results for a simple, generic, and data-driven method of downlink prioritization for magnetic field (and other) measurements. Specifically, Principal Components Analysis (PCA) and One-Class Support Vector Machines (OC-SVMs) are used to detect intervals containing anomalous activity, which can then be prioritized for subsequent downlink. The computational simplicity of this algorithm makes it an excellent candidate for implementation on spaceflight hardware, as well as provide generalizability to a broad range of missions and data products. Initial analysis of this technique has been performed using magnetic field measurements from the Magnetospheric Multiscale Mission and CASSIOP, where it automatically identified scientifically interesting intervals containing Alfvén waves and EMIC activity.
Enabling Intelligent Data Downlink Prioritization of In-Situ Observations through Generalizable and Computationally Inexpensive Anomaly Detection
High-fidelity measurements of magnetic fields and other observed properties, such as energetic particle fluxes, are a necessary component to our understanding of the highly dynamic near-Earth space environment. As our desire to study smaller-scale phenomena such as shocks and dipolorizations has increased, we have been driven to take and telemeter measurements at higher cadences. Unfortunately, many missions are unable to downlink all their captured data due to the well-known data transmission bottleneck at the DSN. These missions must then prioritize their high-cadence data such that the most scientifically useful intervals are transmitted. One simple prioritization technique uses the spacecraft position to telemeter data from only the region of interest. Although easy to implement, this method does not leverage the available scientific data and can omit intervals of useful scientific data when they lie outside the region of interest. The Magnetospheric Multiscale Mission (MMS) uses mission-specific parameterization of several data products to automatically prioritize scientifically useful intervals. Then, MMS verifies the automatically selected intervals by having a domain expert manually select intervals for downlink. The overall complexity required by this technique make it prohibitive for deployment on low-cost platforms (i.e., CubeSats) or on future missions featuring large constellations of satellites such as the Geospace Dynamics Constellation (GDC). We present preliminary results for a simple, generic, and data-driven method of downlink prioritization for magnetic field (and other) measurements. Specifically, Principal Components Analysis (PCA) and One-Class Support Vector Machines (OC-SVMs) are used to detect intervals containing anomalous activity, which can then be prioritized for subsequent downlink. The computational simplicity of this algorithm makes it an excellent candidate for implementation on spaceflight hardware, as well as provide generalizability to a broad range of missions and data products. Initial analysis of this technique has been performed using magnetic field measurements from the Magnetospheric Multiscale Mission and CASSIOP, where it automatically identified scientifically interesting intervals containing Alfvén waves and EMIC activity.
Applying Time Series Models for Flight Data Insights: Prediction, MSE Analysis, and Anomaly Detection
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Anomalies Detection on GRACE and GRACE-FO residuals Data Using AI/ML
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Robust and Explainable Semi-Supervised Deep Learning Model for Anomaly Detection in Aviation
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Multiclass Anomaly Detection in Flight Data Using Semi-Supervised Explainable Deep Learning Model
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A Novel Utilization of Image Registration Techniques to Process Mastcam Images in Mars Rover With Applications to Image Fusion, Pixel Clustering, and Anomaly Detection
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Scalable Anomaly Detection in Batch Polishing Processes for Inertial Confinement Fusion Shells
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Streaming Analytics for Enhancing Anomaly Detection in Satellite State-of-Health Telemetry
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Anomaly detection in all-sky images: An approach using robust ensemble modeling of cloud cover fraction and prediction bounds
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Sequence-Based Anomaly Detection in Critical Infrastructure Networks
United States critical infrastructure faces new cyber threats from adversarial nation-state actors in the form of malware-free attacks. Traditional cybersecurity techniques use rules-based methods to identify indicators of compromise on networks, often missing these sophisticated attacks. Our approach leverages multiple state of the art machine learning models in a pipeline to identify abnormal network events through sequential analysis. We combine both device and packet-level information into individual events to characterize anomalous network actions. The model is trained and tested on real network traffic from the Idaho National Lab High Performance Computing (HPC) with greater than 98% precision. It is capable of flagging malicious tactics used by adversaries in malware-free attacks, severe changes to the network, and abnormal user activity by network devices.
Wasserstein Normalized Autoencoder for Anomaly Detection in ProtoDUNE Vertical-Drift Detector
ProtoDUNE Vertical Drift needs a selective triggering algorithm. The detector sits on Earth's surface, so cosmic activity dominates its data. Our goal in this paper is to trigger on neutrino events more robustly than the current deployed Analog-to-Digital Converter Simple Window (ADCSW) model and, eventually, search for signals of Beyond Standard Model (BSM) physics at DUNE as our ultimate North Star objective. As a step towards this goal, we evaluate a Wasserstein Normalized Autoencoder (WNAE) on simulated collection-plane only windows of shape $1\times10\times10$ where Neutrinos act as our BSM-proxy and Cosmic-ray Muons serve as our learned background. The network parameters are fitted using only cosmic-ray muon events as background in order to maintain an unsupervised pipeline. Training uses finite-step Langevin $x^-$ samples, positive-sample reconstruction energy, and an empirical sliced $2$-Wasserstein objective to learn a normalized Boltzmann energy model. We then calibrate on a nominal $5\,\mathrm{Hz}$ operating threshold calculated from cosmic validation data. Both WNAE and ADCSW accept 311 of 194,083 held-out cosmic background events at this $5\,\mathrm{Hz}$ threshold. We found that WNAE accepts 9,677 of 34,634 neutrino-proxy events $(27.9\pm0.24)\%$, compared with 10,076 $(29.1\pm0.24)\%$ for ADCSW, an observed WNAE-minus-ADCSW difference of $-1.15\%$. At another nominal $2\,\mathrm{Hz}$ target threshold, the corresponding efficiencies are $(20.5\pm0.22)\%$ and $(22.6\pm0.22)\%$, respectively. Of the WNAE-selected neutrino proxies at $5\,\mathrm{Hz}$, $(20.8\pm0.4)\%$ of the classified neutrino-proxy events are unique to WNAE, where the uncertainty is an absolute binomial standard error of $0.4\%$.
Integrated System Health Management (ISHM) for Test Stand and J-2X Engine: Core Implementation
ISHM capability enables a system to detect anomalies, determine causes and effects, predict future anomalies, and provides an integrated awareness of the health of the system to users (operators, customers, management, etc.). NASA Stennis Space Center, NASA Ames Research Center, and Pratt & Whitney Rocketdyne have implemented a core ISHM capability that encompasses the A1 Test Stand and the J-2X Engine. The implementation incorporates all aspects of ISHM; from anomaly detection (e.g. leaks) to root-cause-analysis based on failure mode and effects analysis (FMEA), to a user interface for an integrated visualization of the health of the system (Test Stand and Engine). The implementation provides a low functional capability level (FCL) in that it is populated with few algorithms and approaches for anomaly detection, and root-cause trees from a limited FMEA effort. However, it is a demonstration of a credible ISHM capability, and it is inherently designed for continuous and systematic augmentation of the capability. The ISHM capability is grounded on an integrating software environment used to create an ISHM model of the system. The ISHM model follows an object-oriented approach: includes all elements of the system (from schematics) and provides for compartmentalized storage of information associated with each element. For instance, a sensor object contains a transducer electronic data sheet (TEDS) with information that might be used by algorithms and approaches for anomaly detection, diagnostics, etc. Similarly, a component, such as a tank, contains a Component Electronic Data Sheet (CEDS). Each element also includes a Health Electronic Data Sheet (HEDS) that contains health-related information such as anomalies and health state. Some practical aspects of the implementation include: (1) near real-time data flow from the test stand data acquisition system through the ISHM model, for near real-time detection of anomalies and diagnostics, (2) insertion of the J-2X predictive model providing predicted sensor values for comparison with measured values and use in anomaly detection and diagnostics, and (3) insertion of third-party anomaly detection algorithms into the integrated ISHM model.