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NASA NTRS2014

Inflight and Preflight Detection of Pitot Tube Anomalies

The health and integrity of aircraft sensors play a critical role in aviation safety. Inaccurate or false readings from these sensors can lead to improper decision making, resulting in serious and sometimes fatal consequences. This project demonstrated the feasibility of using advanced data analysis techniques to identify anomalies in Pitot tubes resulting from blockage such as icing, moisture, or foreign objects. The core technology used in this project is referred to as noise analysis because it relates sensors' response time to the dynamic component (noise) found in the signal of these same sensors. This analysis technique has used existing electrical signals of Pitot tube sensors that result from measured processes during inflight conditions and/or induced signals in preflight conditions to detect anomalies in the sensor readings. Analysis and Measurement Services Corporation (AMS Corp.) has routinely used this technology to determine the health of pressure transmitters in nuclear power plants. The application of this technology for the detection of aircraft anomalies is innovative. Instead of determining the health of process monitoring at a steady-state condition, this technology will be used to quickly inform the pilot when an air-speed indication becomes faulty under any flight condition as well as during preflight preparation.

Mitchell, Darrell W.↗

NASA NTRS2011

Visual Object Recognition and Tracking of Tools

A method has been created to automatically build an algorithm off-line, using computer-aided design (CAD) models, and to apply this at runtime. The object type is discriminated, and the position and orientation are identified. This system can work with a single image and can provide improved performance using multiple images provided from videos. The spatial processing unit uses three stages: (1) segmentation; (2) initial type, pose, and geometry (ITPG) estimation; and (3) refined type, pose, and geometry (RTPG) calculation. The image segmentation module files all the tools in an image and isolates them from the background. For this, the system uses edge-detection and thresholding to find the pixels that are part of a tool. After the pixels are identified, nearby pixels are grouped into blobs. These blobs represent the potential tools in the image and are the product of the segmentation algorithm. The second module uses matched filtering (or template matching). This approach is used for condensing synthetic images using an image subspace that captures key information. Three degrees of orientation, three degrees of position, and any number of degrees of freedom in geometry change are included. To do this, a template-matching framework is applied. This framework uses an off-line system for calculating template images, measurement images, and the measurements of the template images. These results are used online to match segmented tools against the templates. The final module is the RTPG processor. Its role is to find the exact states of the tools given initial conditions provided by the ITPG module. The requirement that the initial conditions exist allows this module to make use of a local search (whereas the ITPG module had global scope). To perform the local search, 3D model matching is used, where a synthetic image of the object is created and compared to the sensed data. The availability of low-cost PC graphics hardware allows rapid creation of synthetic images. In this approach, a function of orientation, distance, and articulation is defined as a metric on the difference between the captured image and a synthetic image with an object in the given orientation, distance, and articulation. The synthetic image is created using a model that is looked up in an object-model database. A composable software architecture is used for implementation. Video is first preprocessed to remove sensor anomalies (like dead pixels), and then is processed sequentially by a prioritized list of tracker-identifiers.

English, James↗

NASA NTRS2013

Discovery: Under the Microscope at Kennedy Space Center

The National Aeronautics & Space Administration (NASA) is known for discovery, exploration, and advancement of knowledge. Since the days of Leeuwenhoek, microscopy has been at the forefront of discovery and knowledge. No truer is that statement than today at Kennedy Space Center (KSC), where microscopy plays a major role in contamination identification and is an integral part of failure analysis. Space exploration involves flight hardware undergoing rigorous "visually clean" inspections at every step of processing. The unknown contaminants that are discovered on these inspections can directly impact the mission by decreasing performance of sensors and scientific detectors on spacecraft and satellites, acting as micrometeorites, damaging critical sealing surfaces, and causing hazards to the crew of manned missions. This talk will discuss how microscopy has played a major role in all aspects of space port operations at KSC. Case studies will highlight years of analysis at the Materials Science Division including facility and payload contamination for the Navigation Signal Timing and Ranging Global Positioning Satellites (NA VST AR GPS) missions, quality control monitoring of monomethyl hydrazine fuel procurement for launch vehicle operations, Shuttle Solids Rocket Booster (SRB) foam processing failure analysis, and Space Shuttle Main Engine Cut-off (ECO) flight sensor anomaly analysis. What I hope to share with my fellow microscopists is some of the excitement of microscopy and how its discoveries has led to hardware processing, that has helped enable the successful launch of vehicles and space flight missions here at Kennedy Space Center.

Howard, Philip M.↗

NASA NTRS2000

Retinex Preprocessing for Improved Multi-Spectral Image Classification

The goal of multi-image classification is to identify and label "similar regions" within a scene. The ability to correctly classify a remotely sensed multi-image of a scene is affected by the ability of the classification process to adequately compensate for the effects of atmospheric variations and sensor anomalies. Better classification may be obtained if the multi-image is preprocessed before classification, so as to reduce the adverse effects of image formation. In this paper, we discuss the overall impact on multi-spectral image classification when the retinex image enhancement algorithm is used to preprocess multi-spectral images. The retinex is a multi-purpose image enhancement algorithm that performs dynamic range compression, reduces the dependence on lighting conditions, and generally enhances apparent spatial resolution. The retinex has been successfully applied to the enhancement of many different types of grayscale and color images. We show in this paper that retinex preprocessing improves the spatial structure of multi-spectral images and thus provides better within-class variations than would otherwise be obtained without the preprocessing. For a series of multi-spectral images obtained with diffuse and direct lighting, we show that without retinex preprocessing the class spectral signatures vary substantially with the lighting conditions. Whereas multi-dimensional clustering without preprocessing produced one-class homogeneous regions, the classification on the preprocessed images produced multi-class non-homogeneous regions. This lack of homogeneity is explained by the interaction between different agronomic treatments applied to the regions: the preprocessed images are closer to ground truth. The principle advantage that the retinex offers is that for different lighting conditions classifications derived from the retinex preprocessed images look remarkably "similar", and thus more consistent, whereas classifications derived from the original images, without preprocessing, are much less similar.

Thompson, B.↗

NASA NTRS1985

Thematic Mapper radiometric correction research and development results and performance

The present paper has the objective to discuss three modifications made to the Thematic Mapper Image Processing System (TIPS) radiometric correction process during the R&D period, before turnover of the Landsat Ground Segment to the National Oceanic and Atmospheric Administration. The R&D period was to enhance the correction performance of the ground processing of Thematic Mapper (TM) data, taking into account the correction of sensor anomalies. In the context of a brief review of the major steps in TM radiometric correction, a description is provided of the approaches employed to overcome the effects of the Landsat-5 light leak and the saturated calibration lamp states. Attention is also given to scene content correction limitations, and a performance bench mark.

Singh, A.↗

NASA NTRS2011

Network-Capable Application Process and Wireless Intelligent Sensors for ISHM

Intelligent sensor technology and systems are increasingly becoming attractive means to serve as frameworks for intelligent rocket test facilities with embedded intelligent sensor elements, distributed data acquisition elements, and onboard data acquisition elements. Networked intelligent processors enable users and systems integrators to automatically configure their measurement automation systems for analog sensors. NASA and leading sensor vendors are working together to apply the IEEE 1451 standard for adding plug-and-play capabilities for wireless analog transducers through the use of a Transducer Electronic Data Sheet (TEDS) in order to simplify sensor setup, use, and maintenance, to automatically obtain calibration data, and to eliminate manual data entry and error. A TEDS contains the critical information needed by an instrument or measurement system to identify, characterize, interface, and properly use the signal from an analog sensor. A TEDS is deployed for a sensor in one of two ways. First, the TEDS can reside in embedded, nonvolatile memory (typically flash memory) within the intelligent processor. Second, a virtual TEDS can exist as a separate file, downloadable from the Internet. This concept of virtual TEDS extends the benefits of the standardized TEDS to legacy sensors and applications where the embedded memory is not available. An HTML-based user interface provides a visual tool to interface with those distributed sensors that a TEDS is associated with, to automate the sensor management process. Implementing and deploying the IEEE 1451.1-based Network-Capable Application Process (NCAP) can achieve support for intelligent process in Integrated Systems Health Management (ISHM) for the purpose of monitoring, detection of anomalies, diagnosis of causes of anomalies, prediction of future anomalies, mitigation to maintain operability, and integrated awareness of system health by the operator. It can also support local data collection and storage. This invention enables wide-area sensing and employs numerous globally distributed sensing devices that observe the physical world through the existing sensor network. This innovation enables distributed storage, distributed processing, distributed intelligence, and the availability of DiaK (Data, Information, and Knowledge) to any element as needed. It also enables the simultaneous execution of multiple processes, and represents models that contribute to the determination of the condition and health of each element in the system. The NCAP (intelligent process) can configure data-collection and filtering processes in reaction to sensed data, allowing it to decide when and how to adapt collection and processing with regard to sophisticated analysis of data derived from multiple sensors. The user will be able to view the sensing device network as a single unit that supports a high-level query language. Each query would be able to operate over data collected from across the global sensor network just as a search query encompasses millions of Web pages. The sensor web can preserve ubiquitous information access between the querier and the queried data. Pervasive monitoring of the physical world raises significant data and privacy concerns. This innovation enables different authorities to control portions of the sensing infrastructure, and sensor service authors may wish to compose services across authority boundaries.

Figueroa, Fernando↗

NASA NTRS2012

Aircraft Anomaly Detection Using Performance Models Trained on Fleet Data

This paper describes an application of data mining technology called Distributed Fleet Monitoring (DFM) to Flight Operational Quality Assurance (FOQA) data collected from a fleet of commercial aircraft. DFM transforms the data into aircraft performance models, flight-to-flight trends, and individual flight anomalies by fitting a multi-level regression model to the data. The model represents aircraft flight performance and takes into account fixed effects: flight-to-flight and vehicle-to-vehicle variability. The regression parameters include aerodynamic coefficients and other aircraft performance parameters that are usually identified by aircraft manufacturers in flight tests. Using DFM, the multi-terabyte FOQA data set with half-million flights was processed in a few hours. The anomalies found include wrong values of competed variables, (e.g., aircraft weight), sensor failures and baises, failures, biases, and trends in flight actuators. These anomalies were missed by the existing airline monitoring of FOQA data exceedances.

Gorinevsky, Dimitry↗

NASA NTRS2004

Strategy Developed for Selecting Optimal Sensors for Monitoring Engine Health

Sensor indications during rocket engine operation are the primary means of assessing engine performance and health. Effective selection and location of sensors in the operating engine environment enables accurate real-time condition monitoring and rapid engine controller response to mitigate critical fault conditions. These capabilities are crucial to ensure crew safety and mission success. Effective sensor selection also facilitates postflight condition assessment, which contributes to efficient engine maintenance and reduced operating costs. Under the Next Generation Launch Technology program, the NASA Glenn Research Center, in partnership with Rocketdyne Propulsion and Power, has developed a model-based procedure for systematically selecting an optimal sensor suite for assessing rocket engine system health. This optimization process is termed the systematic sensor selection strategy. Engine health management (EHM) systems generally employ multiple diagnostic procedures including data validation, anomaly detection, fault-isolation, and information fusion. The effectiveness of each diagnostic component is affected by the quality, availability, and compatibility of sensor data. Therefore systematic sensor selection is an enabling technology for EHM. Information in three categories is required by the systematic sensor selection strategy. The first category consists of targeted engine fault information; including the description and estimated risk-reduction factor for each identified fault. Risk-reduction factors are used to define and rank the potential merit of timely fault diagnoses. The second category is composed of candidate sensor information; including type, location, and estimated variance in normal operation. The final category includes the definition of fault scenarios characteristic of each targeted engine fault. These scenarios are defined in terms of engine model hardware parameters. Values of these parameters define engine simulations that generate expected sensor values for targeted fault scenarios. Taken together, this information provides an efficient condensation of the engineering experience and engine flow physics needed for sensor selection. The systematic sensor selection strategy is composed of three primary algorithms. The core of the selection process is a genetic algorithm that iteratively improves a defined quality measure of selected sensor suites. A merit algorithm is employed to compute the quality measure for each test sensor suite presented by the selection process. The quality measure is based on the fidelity of fault detection and the level of fault source discrimination provided by the test sensor suite. An inverse engine model, whose function is to derive hardware performance parameters from sensor data, is an integral part of the merit algorithm. The final component is a statistical evaluation algorithm that characterizes the impact of interference effects, such as control-induced sensor variation and sensor noise, on the probability of fault detection and isolation for optimal and near-optimal sensor suites.

Source record↗

NASA NTRS2004

Experimental Test-Bed for Intelligent Passive Array Research

This document describes the test-bed designed for the investigation of passive direction finding, recognition, and classification of speech and sound sources using sensor arrays. The test-bed forms the experimental basis of the Intelligent Small-Scale Spatial Direction Finder (ISS-SDF) project, aimed at furthering digital signal processing and intelligent sensor capabilities of sensor array technology in applications such as rocket engine diagnostics, sensor health prognostics, and structural anomaly detection. This form of intelligent sensor technology has potential for significant impact on NASA exploration, earth science and propulsion test capabilities. The test-bed consists of microphone arrays, power and signal distribution modules, web-based data acquisition, wireless Ethernet, modeling, simulation and visualization software tools. The Acoustic Sensor Array Modeler I (ASAM I) is used for studying steering capabilities of acoustic arrays and testing DSP techniques. Spatial sound distribution visualization is modeled using the Acoustic Sphere Analysis and Visualization (ASAV-I) tool.

Solano, Wanda M.↗

NASA NTRS2007

Automated Detection of Events of Scientific Interest

A report presents a slightly different perspective of the subject matter of Fusing Symbolic and Numerical Diagnostic Computations (NPO-42512), which appears elsewhere in this issue of NASA Tech Briefs. Briefly, the subject matter is the X-2000 Anomaly Detection Language, which is a developmental computing language for fusing two diagnostic computer programs one implementing a numerical analysis method, the other implementing a symbolic analysis method into a unified event-based decision analysis software system for real-time detection of events. In the case of the cited companion NASA Tech Briefs article, the contemplated events that one seeks to detect would be primarily failures or other changes that could adversely affect the safety or success of a spacecraft mission. In the case of the instant report, the events to be detected could also include natural phenomena that could be of scientific interest. Hence, the use of X- 2000 Anomaly Detection Language could contribute to a capability for automated, coordinated use of multiple sensors and sensor-output-data-processing hardware and software to effect opportunistic collection and analysis of scientific data.

James, Mark↗

NASA NTRS1981

Space Shuttle main engine progress through the first flight

A system overview is presented for the Space Shuttle Main Engine (SSME), and extensive performance comparisons are made with the Saturn V booster powerplant showing (1) staged-combustion turbopump pressures of 7,500 vs 1,100 psi for the Saturn V first stage; (2) a specific impulse of 455 vs 300; (3) power density of 100 vs 20 hp/lb; and (4) chamber pressures of 3200 vs 1000 psia. An account is then given of the SSME testing program, engine certification process and flight aboard the Space Shuttle Columbia, with attention to anomalies detected during flight in turbopump operation and mixture ratio sensor function.

Johnson, J. R.↗

NASA NTRS2024

Pressure Deficit in Gale Crater and a Larger Northern Polar Cap After the MY34 Global Dust Storm

We describe the model-independent analysis technique of Mars Science Laboratory (MSL) pressure and Mars Climate Sounder (MCS) data in de la Torre Juárez et al. (2019, https://doi.org/10.22541/essoar.169945479.90436599/v1) that compared multiple years of surface pressures on Gale before, during, and after the Global Dust Storm of Mars Year 34. The analysis found (a) representative pressure scale heights over Gale; (b) that the storm was followed by a pressure deficit at Gale; (c) the following C storms did not eliminate the deficit; (d) changes in the duration of the polar caps condensation seasons, with an early start of the North Polar (NP) ice cap growing season the year before the Great Dust Storm (GDS) and a late signature of the end of the expansion season thereafter, changes consistent with a larger growth phase of the NP cap; (e) MCS observed a larger than usual NP cap; and (f) cold temperature anomalies over the NP and warm over the Southern Pole after the storm. We also show that the analysis of observed MSL pressure data alone filters out effects on the pressure signal that are attributable to dynamical and orographic processes in a recent model analysis that makes similar interpretations as our 2019 study. One additional Mars year of observations is included to eliminate early concerns about sensor drifts. Noting that a similar NP anomaly was observed with MCS data after the last early GDS in MY25, and not the later GDS of MY27, the results suggest a possible unique effect of early GDSs.

Manuel de la Torre Juárez↗

NASA NTRSDate not supplied

Integrated System for Autonomous and Adaptive Caretaking (ISAAC): Phase 1 Low-Fidelity Demo

This presentation describes the ISAAC phase 1 low-fidelity demonstration results. The demonstration satisfied the milestone from the Gateway-ISAAC Memorandum of Understanding to "Demonstrate spatial and logical data registration between robotics and spacecraft". It integrated many new ISAAC components, including a spatially linked model, Astrobee multi-sensor mapping, and an integrated data interface. It advanced ISAAC key performance parameters related to mapping, in a lab setting. Areas for phase 1 forward work include: improve maturity toward the high-fidelity demo on the ISS; demonstrate mapping with more sensor modalities; expand initial anomaly detection implementation into a flexible framework with multiple detection algorithms for different tasks; begin open source software release process for releasable ISAAC components.

robotics↗

NASA NTRS1995

Automated Propulsion Data Screening demonstration system

A fully-instrumented firing of a propulsion system typically generates a very large quantity of data. In the case of the Space Shuttle Main Engine (SSME), data analysis from ground tests and flights is currently a labor-intensive process. Human experts spend a great deal of time examining the large volume of sensor data generated by each engine firing. These experts look for any anomalies in the data which might indicate engine conditions warranting further investigation. The contract effort was to develop a 'first-cut' screening system for application to SSME engine firings that would identify the relatively small volume of data which is unusual or anomalous in some way. With such a system, limited and expensive human resources could focus on this small volume of unusual data for thorough analysis. The overall project objective was to develop a fully operational Automated Propulsion Data Screening (APDS) system with the capability of detecting significant trends and anomalies in transient and steady-state data. However, the effort limited screening of transient data to ground test data for throttle-down cases typical of the 3-g acceleration, and for engine throttling required to reach the maximum dynamic pressure limits imposed on the Space Shuttle. This APDS is based on neural networks designed to detect anomalies in propulsion system data that are not part of the data used for neural network training. The delivered system allows engineers to build their own screening sets for application to completed or planned firings of the SSME. ERC developers also built some generic screening sets that NASA engineers could apply immediately to their data analysis efforts.

Hoyt, W. Andes↗

NASA NTRS2007

Space Technology 5 Post-Launch Ground Attitude Estimation Experience

The Space Technology (ST)-5 satellites were launched March 22, 2006 on a Pegasus XL launch vehicle into a Sun-synchronous orbit. The three micro-satellites which constituted the ST-5 mission were kept in a formation which allowed three successive measurements taken of the Earth s magnetic field in order to study short term fluctuations of the field. The attitude of each satellite was computed on the ground using data from the science grade magnetometer as well as the miniature spinning Sun sensor (MSSS) which was the primary attitude sensor. Attitude and orbit maneuvers were performed using a single axial cold gas thruster. This paper describes the ground attitude estimation process and performance as well as anomaly resolutions.

Harman, Richard R.↗

NASA NTRSDate not supplied

Towards a program of record of inland water quality: Exploiting present and heritage multispectral sensors for maximum information extraction

Degradation of Earth’s inland water resources due to anthropogenic perturbations and climate anomalies at both local and global scales continues to place human health at substantial risk. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products, and to promote operational and sustainable resource policy management. This research exploits recent advancements in bio-optical modeling, cloud computing, and machine learning to enhance our capacity to leverage present and heritage satellite data. Recent research suggests that sensors with low spectral resolution, such as Sentinel 2 and Landsat missions, contain enough hidden spectral variation which can be exploited using data-driven approaches. The availability of three decades of archival imagery will open doors to discover global trends of eutrophication and increased cyanobacteria dominance and provide valuable insight to the development of predictive methodologies. Preliminary efforts in synthetic emulation of global natural inland waters will be discussed and contextualized against satellite radiometric measurement uncertainty, satellite data product uncertainties and causal signal ambiguity over the visible wavelength range, supported by high quality field and image data for selected inland aquatic sites. Insights on water quality estimation via data-driven machine learning models versus matrix inversions will be discussed, and how we can exploit spectral-spatial relationships in high spatial resolution data. A cross-sensor synergistic approach with detailed uncertainty analysis based on optical water types, will allow for unprecedented global snapshots of fine scale ecological dynamics of inland waters.

Inland↗

NASA NTRS1994

Attention focusing and anomaly detection in systems monitoring

Any attempt to introduce automation into the monitoring of complex physical systems must start from a robust anomaly detection capability. This task is far from straightforward, for a single definition of what constitutes an anomaly is difficult to come by. In addition, to make the monitoring process efficient, and to avoid the potential for information overload on human operators, attention focusing must also be addressed. When an anomaly occurs, more often than not several sensors are affected, and the partially redundant information they provide can be confusing, particularly in a crisis situation where a response is needed quickly. The focus of this paper is a new technique for attention focusing. The technique involves reasoning about the distance between two frequency distributions, and is used to detect both anomalous system parameters and 'broken' causal dependencies. These two forms of information together isolate the locus of anomalous behavior in the system being monitored.

Doyle, Richard J.↗

NASA NTRS2000

Adaptive Management of Computing and Network Resources for Spacecraft Systems

It is likely that NASA's future spacecraft systems will consist of distributed processes which will handle dynamically varying workloads in response to perceived scientific events, the spacecraft environment, spacecraft anomalies and user commands. Since all situations and possible uses of sensors cannot be anticipated during pre-deployment phases, an approach for dynamically adapting the allocation of distributed computational and communication resources is needed. To address this, we are evolving the DeSiDeRaTa adaptive resource management approach to enable reconfigurable ground and space information systems. The DeSiDeRaTa approach embodies a set of middleware mechanisms for adapting resource allocations, and a framework for reasoning about the real-time performance of distributed application systems. The framework and middleware will be extended to accommodate (1) the dynamic aspects of intra-constellation network topologies, and (2) the complete real-time path from the instrument to the user. We are developing a ground-based testbed that will enable NASA to perform early evaluation of adaptive resource management techniques without the expense of first deploying them in space. The benefits of the proposed effort are numerous, including the ability to use sensors in new ways not anticipated at design time; the production of information technology that ties the sensor web together; the accommodation of greater numbers of missions with fewer resources; and the opportunity to leverage the DeSiDeRaTa project's expertise, infrastructure and models for adaptive resource management for distributed real-time systems.

Pfarr, Barbara↗