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Experimental and Analytical Development of a Health Management System for Electro-Mechanical Actuators
data.nasa.gov | Last Updated 2020-01-29T01:49:29.000ZExpanded deployment of Electro-Mechanical Actuators (EMAs) in critical applications has created much interest in EMA Prognostic Health Management (PHM), a key enabling technology of Condition Based Maintenance (CBM). As such, Impact Technologies, LLC is collaborating with the NASA Ames Research Center to perform a number of research efforts in support of NASA’s Integrated Vehicle Health Management (IVHM) initiatives. These efforts have combined experimental test stand development, laboratory seeded fault testing, and physical model-based health monitoring in a comprehensive PHM system development strategy. This paper discusses two closely related EMA research programs being conducted by Impact and NASA Ames. The first of these efforts resulted in the creation of an electro-mechanical actuator test stand for the Prognostics Center of Excellence at the NASA Ames Research Center. The second effort is ongoing and is utilizing physics-based modeling techniques to develop an algorithm and software package toolset for PHM of aircraft EMA systems using a hybrid (virtual sensor) approach.
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Rapid Electrochemical Detection and Identification of Microbiological and Chemical Contaminants for Manned Spaceflight Project
data.nasa.gov | Last Updated 2020-01-29T03:33:53.000Z<p>A great deal of effort has gone into the development of point-of-use methods to meet the challenge of rapid bacterial identification for both environmental monitoring and clinical applications.&nbsp; Unfortunately, most of the methods developed rely on Preliminary Chain Reaction (PCR) and face inherent limitations because of the requirement for enzymatic components and thermal control.&nbsp; Other methods based on surface plasmon resonance, quartz crystal microbalance, and fluorescence has been reported with good detection limits, but, these methods are immunological and cannot provide genetic-level information.&nbsp; Further, they require labeled markers, complicated fluid handling systems, and sensitive optics that drive up cost and complexity and preclude them from outside the laboratory.&nbsp; Recent work by a group at the University of Toronto has focused on developing an electrochemical platform that combines ultrasensitive detection, straightforward sample processing, and inexpensive components to create a cost-effective, user-friendly device for detection and identification of microorganisms.&nbsp; The platform combines an electrical cell lysis chamber, and electrochemical reporter system, and nanostructured microelectrodes (NMEs) to detect specific nucleic acid sequences.&nbsp; The nucleic acid sequences are unique to a given type of microorganism and can be used to identify the microorganisms present in a sample.</p><p>From the perspective of the anticipated prototype device &nbsp;(Lam, et al. 2012. <em>Polymerase Chain Reaction-Free, Sample-to-Answer Bacterial Detection in 30 Minutes with Integrated Cell Lysis</em>. Anal. Chem. <strong>84(1)</strong>: 21-5), detection of microbial contaminants will begin with a lysis chamber designed to release DNA and RNA from microorganisms present in the sample using ultrasonic or electrochemical technology.&nbsp; The DNA and RNA mixture is then passed into an analysis chamber containing an array of nanostructured microelectrodes (NMEs).&nbsp; The surface of the NMEs will be functionalized with probe molecules for DNA or RNA sequences specific to the bacteria being targeted.&nbsp; Binding of the DNA or RNA to the appropriate detection probe on the NME surface in the presence of an electrochemical reporter system will change the electrochemical properties of the NMEs.&nbsp; A potentiostat is used to measure the current at each individual electrode before and after addition of the DNA and RNA mixture.&nbsp; The difference in current before and after addition of the mixture to the NMEs is compared against a pre-determined threshold to check for the presence of target bacteria in the sample.&nbsp; The process for detection of chemical contaminants is very similar.&nbsp; The lysis chamber would be bypassed and the sample would flow directly into the analysis chamber.&nbsp; The NMEs will be functionalized with molecules to selectively bind the desired targets (analytes) and the change in the electrochemical response of each NME can again be used to detect and quantify the contaminants.&nbsp; Depending on the analyte of interest, it may be possible to directly measure analyte binding on the surface of the NMEs without the use of an electrochemical reporter system. The overall project will focus on optimization of the individual aspects of the detection platform in preparation for construction of a prototype for a flight experiment.&nbsp; The scope of the work in this proposal is limited to characterization and optimization of the lysis step/sample preparation, probe selection, and NME structure.&nbsp; Lysis conditions will be optimized by evaluating parameters associated with the oscillation frequency and lysis time for ultrasonic techniques and applied voltage for the electrochemical techniques.&nbsp; Cell viability, as determined by fluorescent detection of DNA or R
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Airborne Electro-Mechanical Actuator Test Stand for Development of Prognostic Health Management Systems
data.nasa.gov | Last Updated 2020-01-29T04:05:01.000ZWith the advent of the next generation of aerospace systems equipped with fly-by-wire controls, electro- mechanical actuators (EMA) are quickly becoming components critical to safety of aerospace vehicles. Being relatively new to the field, however, EMA lack the knowledge base compared to what is accumulated for the more traditional actuator types, especially when it comes to fault detection and prognosis. Scarcity of health monitoring data from fielded systems and prohibitive costs of carrying out real flight tests create the need to build high-fidelity system models and design affordable yet realistic experimental setups. The objective of this work is to build an EMA test stand that, unlike current laboratory stands typically weighing in excess of one metric ton, is portable enough to be easily placed aboard a wide variety of aircraft. This stand, named the FLEA (for Flyable Electro- mechanical Actuator test stand), allows testing EMA fault detection and prognosis technologies in flight environment, thus substantially increasing their technology readiness level – all without the expense of dedicated flights, as the stand is designed to function as a non-intrusive secondary payload. No aircraft modifications are required and data can be collected during any available flight opportunity: pilot currency flights, ferry flights, or flights dedicated to other experiments. The stand is currently equipped with a prototype version of NASA Ames developed prognostic health management system with models aimed at detecting and tracking several fault types. At this point the team has completed test flights of the stand on US Air Force C-17 aircraft and US Army UH-60 helicopters and more experiments, both laboratory and airborne, are planned for the coming months.
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A Model-based Avionic Prognostic Reasoner (MAPR)
data.nasa.gov | Last Updated 2020-01-29T04:02:06.000ZThe Model-based Avionic Prognostic Reasoner (MAPR) presented in this paper is an innovative solution for non-intrusively monitoring the state of health (SoH) and predicting the remaining useful life (RUL) of electronic and electromechanical assets by accessing and processing data obtained from a standard avionics data bus. To support Integrated Vehicle Health Monitoring (IVHM) initiatives, the solution being described here has been designed to be as non-intrusive as possible. An innovative, model-driven anomaly diagnostic and fault characterization system for electromechanical actuator (EMA) systems was developed to mitigate potentially catastrophic faults. EMA systems are used in a wide variety of aircraft applications to control critical components such as control surfaces, landing gear and thrust vector control. Failure in any one of these systems can compromise passenger safety, as well as mission success. A MIL-STD-1553 bus interface and monitor were designed to extract environmental (e.g., altitude, air speed, air density) and operational (i.e., response of system to a commanded change) data of a representative EMA system and to determine whether an anomaly is detected, and the corresponding severity. The MIL-STD-1553 bus was chosen as the test bed to develop this approach, due to its large installed base and availability of compatible development tools. Advanced and unique reasoning methodologies are applied to the extracted data sets to provide anomaly detection and fault classification on various fault modes and eventually yield SoH and RUL. In this paper we describe a data monitoring unit that will, in real time, identify, isolate, and characterize faults and establish their severity so that major performance problems can be alleviated. When built, this system will consist of a laptop with a Peripheral Component Interconnect (PCI) card slot that can accept multiple interfaces to the MAPR software package. The MAPR package will be designed to be adaptable for a large number of different platforms, for portability and for maximum input data type flexibility. This paper describes a ground-based prototype of the technology to show the efficacy of the method.
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A Systems Engineering Approach to Electro-Mechanical Actuator Diagnostic and Prognostic Development
data.nasa.gov | Last Updated 2020-01-29T03:31:10.000ZThe authors have formulated a Comprehensive Systems Engineering approach to Electro-Mechanical Actuator (EMA) Prognostics and Health Management (PHM) system development. The approach implements software tools to integrate simulation-based design principles and dynamic failure mode and effects analysis. It also provides automated failure mode insertion and propagation analysis, PHM algorithm design and verification, full dynamic simulations, code generation, and validation testing. This process aims to produce the appropriate fault detection and prediction algorithms needed for successful development of an EMA PHM system. As an initial use case, the developed approach was implemented to develop and validate a model-based, virtual sensor software package for landing gear EMA PHM. This effort included creation of a dynamic, component-level system model that can be used to virtually sense parameters, detect degradation, isolate probable root cause, and assess severity. This model is also used as a virtual test bed for performing fault insertion analysis to address algorithm development and experimental prioritization. The developed model was validated using data from a test stand, which was specifically constructed for EMA PHM development. The model-based predictor was then coupled with failure mode diagnostics, advanced knowledge fusion, and failure mode progression algorithms to form a complete prototype EMA PHM solution. Reproduced by kind permission of MFPT (www.mfpt.org).
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Experimental Validation of a Prognostic Health Management System for Electro-Mechanical Actuators
data.nasa.gov | Last Updated 2020-01-29T04:28:33.000ZThe work described herein is aimed to advance prognostic health management solutions for electro-mechanical actuators and, thus, increase their reliability and attractiveness to designers of the next generation aircraft and spacecraft. In pursuit of this goal the team adopted a systematic approach by starting with EMA FMECA reviews, consultations with EMA manufacturers, and extensive literature reviews of previous efforts. Based on the acquired knowledge, nominal/off-nominal physics models and prognostic health management algorithms were developed. In order to aid with development of the algorithms and validate them on realistic data, a testbed capable of supporting experiments in both laboratory and flight environment was developed. Test actuators with architectures similar to potential flight-certified units were obtained for the purposes of testing and realistic fault injection methods were designed. Several hundred fault scenarios were created, using permutations of position and load profiles, as well as fault severity levels. The diagnostic system was tested extensively on these scenarios, with the test results demonstrating high accuracy and low numbers of false positive and false negative diagnoses. The prognostic system was utilized to track fault progression in some of the fault scenarios, predicting the remaining useful life of the actuator. A series of run-to-failure experiments were conducted to validate its performance, with the resulting error in predicting time to failure generally lesser than 10% error. While a more robust validation procedure would require dozens more experiments executed under the same conditions (and, consequently, more test articles destroyed), the current results already demonstrate the potential for predicting fault progression in this type of devices. More prognostic experiments are planned for the next phase of this work, including investigation and comparison of other prognostic algorithms (such as various types of Particle Filter and GPR), addition of new fault types, and execution of prognostic experiments in flight environment.
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Prognostics Enhanced Reconfigurable Control of Electro-Mechanical Actuators
data.nasa.gov | Last Updated 2020-01-29T03:50:25.000ZActuator systems are employed widely in aerospace, transportation and industrial processes to provide power to critical loads, such as aircraft control surfaces. They must operate reliably and accurately in order for the vehicle / process to complete successfully its designated mission. Incipient actuator failure conditions may severely endanger the operational integrity of the vehicle / process and compromise its mission. The ability to maintain a stable and credible operation, even in the presence of incipient failures, is of paramount importance to accomplish “must achieve” mission objectives. This paper introduces a novel methodology for the fault-tolerant design of critical subsystems, such as an ElectroMechanical Actuator (EMA), that takes advantage of on-line, real-time estimates of the Remaining Useful Life (RUL) or Time-to-Failure (TTF) of a failing component and reconfigures the available control authority by trading off system performance with control activity. The primary goal is to complete critical mission objectives within a time window dictated by prognostic algorithms so that the fault mode is accommodated and an acceptable level of performance maintained for the duration of the mission. The proposed fault-tolerant control design is mathematically rigorous, generic and applicable to a variety of application domains. An EMA is used to illustrate the efficacy of the proposed approach.
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Global Pesticide Grids (PEST-CHEMGRIDS), Version 1.01
data.nasa.gov | Last Updated 2022-01-17T05:22:43.000ZThe Global Pesticide Grids (PEST-CHEMGRIDS), Version 1.01 data set contains 20 of the most-used pesticide active ingredients on 6 dominant crops and 4 aggregated crop classes at 5 arc-minute resolution (about 10 km at the equator), estimated in year 2015, and then projected to 2020 and 2025. To estimate the global application rates of specific active ingredients, spatial statistical methods were used to re-analyze the U.S. Geological Survey Pesticide National Synthesis Project (USGS/PNSP) and the Food and Agriculture Organization Corporate Statistical Database (FAOSTAT) pesticide databases, along with other public inventories including globally gridded data of soil physical properties, hydro-climatic variables, agricultural quantities, and socioeconomic indices. The application rate (APR) of each active ingredient on each crop is in kilogram per hectare per year (kg/ha-year), and the harvest area of each crop is in hectare (ha). The data set also includes 200 data quality index maps corresponding to each active ingredient on each crop, as well as maps of the 10 dominant crops and 4 aggregated crop classes. Version 1.01 includes data in GeoTIFF and netCDF formats.
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Data driven modeling of the simultaneous activities in ambient environments
data.nasa.gov | Last Updated 2020-01-29T04:05:26.000ZResident of a smart home, who may be an old person or an Alzheimer patient needing permanent assistance, actuates the world by realizing activities, which are observed through the embedded sensors of smart home. Typically, this person may sometimes forget completion of the activities; may realize the activities of daily living incorrectly, and may enter to dangerous states. In order to provide automatic assistance for the smart home resident through the embedded electronically controllable actuators and make the smart home resident able to live independently at home we propose to calculate a possibilistic logical space for correct realization of activities, which may be represented in form of a multivariable problem. Regardless from the physical entity (modality and location) of the intelligence source and the quantity of individuals who perform the activities; per each possible goal or activity, we consider a unique source of intelligence (for example a social mind) who directs the order of fuzzy events that occur in the ambient environment, then the plan behind world actuations is modeled applying extensions of the fuzzy logic. The main key point that we deal with is the analysis of the observations in order to make inferences about possible simultaneous activities that may be planned and realized by one or more individuals; so that we can reason in the cases the parallel activities are interrupted.
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Solving a prisoner's dilemma in distributed anomaly detection
data.nasa.gov | Last Updated 2020-01-29T01:46:30.000ZAnomaly detection has recently become an important problem in many industrial and financial applications. In several instances, the data to be analyzed for possible anomalies is located at multiple sites and cannot be merged due to practical constraints such as bandwidth limitations and proprietary concerns. At the same time, the size of data sets affects prediction quality in almost all data mining applications. In such circumstances, distributed data mining algorithms may be used to extract information from multiple data sites in order to make better predictions. In the absence of theoretical guarantees, however, the degree to which data decentralization affects the performance of these algorithms is not known, which reduces the data providing participants' incentive to cooperate.This creates a metaphorical 'prisoners' dilemma' in the context of data mining. In this work, we propose a novel general framework for distributed anomaly detection with theoretical performance guarantees. Our algorithmic approach combines existing anomaly detection procedures with a novel method for computing global statistics using local sufficient statistics. We show that the performance of such a distributed approach is indistinguishable from that of a centralized instantiation of the same anomaly detection algorithm, a condition that we call zero information loss. We further report experimental results on synthetic as well as real-world data to demonstrate the viability of our approach. The remaining content of this presentation is presented in Fig. 1.