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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Hybrid Approaches to Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Systems Health Management↗
NASA NTRS2016

Integrated System for Health Management and Autonomous Control (ISHM-AC): Development and Application

Autonomous control systems represent a technological barrier from manual and automated operated control systems in an industry wide application. An increase in energy-efficient storage, transfer and use of cryogens and cryogenic propellants on Earth and in space has been observed in space related industries between NASA, government and commercial programs. An increase in efficiency of cryogenic systems demands an increase in the capabilities of the control and monitoring system that manages them. As new technologies are developed for cryogenic systems, complexity and capability increases. The increase in complexities are a natural drive to develop better and more capable health monitoring and control system management. Current research and development efforts lead the Cryogenics Test Laboratory at the Kennedy Space Center to improve its automated control and health monitoring system from a Programmable Logic Control (PLC) based-only system to a fully Integrated System for Health Management (ISHM) - Autonomous Control (AC) capable of performing autonomous operations on a cryogenic propellant transfer system. This ISHM-AC system has been developed and tested by controlling a complete simulated propellant transfer operation. The capabilities and test results of this fully integrated autonomous system for cryogenics propellant transfer operation will be presented in this paper.

Toro, Jaime A.↗
NASA NTRS2016

Integrated System for Health Management and Autonomous Control (ISHM-AC) for Cryogenic Operations on the Simulated Propellant Loading System

Autonomous control systems represent a technological barrier from manual and automated operated control systems in an industry wide application. An increase in energy-efficient storage, transfer and use of cryogens and cryogenic propellants on Earth and in space has been observed in space related industries between NASA, government and commercial programs. An increase in efficiency of cryogenic systems demands an increase in the capabilities of the control and monitoring system that manages them. As new technologies are developed for cryogenic systems, complexity and capability increases. The increase in complexities are a natural drive to develop better and more capable health monitoring and control system management. Current research and development efforts lead the Cryogenics Test Laboratory at the Kennedy Space Center to improve its automated control and health monitoring system from a Programmable Logic Control (PLC) based-only system to a fully Integrated System for Health Management (ISHM) - Autonomous Control (AC) capable of performing autonomous operations on a cryogenic propellant transfer system. This ISHM-AC system has been developed and tested by controlling a complete simulated propellant transfer operation. The capabilities and test results of this fully integrated autonomous system for cryogenics propellant transfer operation will be presented in this paper.

Toro Medina, Jaime A.↗
NASA NTRS2020

Prognostics for Systems Health Management - Model and Hybrid Based Approaches. Where are We Heading?

To facilitate and solve the prediction problem, awareness of the current state and health of the system is key, since it is necessary to perform condition-based system health predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditional. In case of next generation electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. In order to tackle and solve the prediction problem, it is essential to have awareness of the current health state of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operational conditions and flight profiles for accurate estimation of end-of-discharge (EOD) for the batteries. Similar framework can be implemented to other complex systems and subsystems. Our research approach is to develop a system level health monitoring safety indicator which runs estimation and prediction algorithms to estimate remaining useful life predictions at system, subsystem swell as component levels. Given models of the current and future system behavior, a general approach of model-based prognostics is discussed as a solution to the prediction problem and further for decision making. Data driven prognostics approaches have been equally used with good results in the past, where respective approaches have their own challenges to tackle. This limits their applicability to complex real-world domains: (a) high complexity or incompleteness of physics-based models and (b) limited representativeness of the training dataset for data-driven models. With the advent of internet of things for data collection and increased use of ML algorithms, hybrid approaches are the next avenue to reduce the challenges and achieve better results. An hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, we use physics-based performance models to infer unobservable model parameters related to the system's components health solving a calibration problem.

Prognostics↗
NASA NTRSDate not supplied

Systems Health Management and Decision Making

In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to accurately predict the future state of any system, it is required to possess knowledge of its current and future operations. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prior stated prediction problem. In case of electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle. Discuss research approach to develop a system level health monitoring safety indicator which runs estimation and prediction algorithms to estimate remaining useful life predictions at system as well as subsystem levels. Given models of the current and future system behavior, a general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making. In addition a digital twin concept is being implemented in the framework to demonstrate verification and validation of developed algorithms. Note - This presentation is for the short workshop conducted at the same conference and contains all previously approved and published information.

Systems Health Managent↗
NASA NTRS2005

Intelligent Engine Systems Work Element 1.3: Sub System Health Management

The objectives of this program were to develop health monitoring systems and physics-based fault detection models for engine sub-systems including the start, lubrication, and fuel. These models will ultimately be used to provide more effective sub-system fault identification and isolation to reduce engine maintenance costs and engine down-time. Additionally, the bearing sub-system health is addressed in this program through identification of sensing requirements, a review of available technologies and a demonstration of a demonstration of a conceptual monitoring system for a differential roller bearing. This report is divided into four sections; one for each of the subtasks. The start system subtask is documented in section 2.0, the oil system is covered in section 3.0, bearing in section 4.0, and the fuel system is presented in section 5.0.

Ashby, Malcolm↗
NASA NTRS2020

Model Based Diagnostics and Prognostics Framework for Systems Health Management

In order to tackle and solve the system health prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to accurately predict the future state of any system, it is required to possess knowledge of its current and future operations. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prior stated prediction problem. In case of electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle. This presentation will cover a physics based-modeling approach implemented for case-studies in battery and composite structures for prognostics. Given models of the current and future system behavior, a general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making.

Banerjee, Portia↗
NASA NTRSDate not supplied

Systems Health Management and Prognostics Approaches for Electric Aircrafts

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in the prediction of remaining driving flying time/distance for the flying vehicles. This information is important, particularly in the case of auto vehicles, because such vehicles can become self-aware, autonomously compute its own capabilities, and identify how to best plan and successfully complete vehicular missions safely. In case of electric aircrafts, computing the remaining flying time is also safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. A systematic prediction framework is implemented to identify all possible sources of uncertainty, quantify each of them individually, and mathematically estimate their combined effect on the system-level quantity of interest, in this case, the remaining flying time/distance of the unmanned aircraft. Note - This presentation contains all previously published information.

Systems Health Managent↗
NASA NTRS2017

Unobtrusive Software and System Health Management with R2U2 on a Parallel MIMD Coprocessor

Dynamic monitoring of software and system health of a complex cyber-physical system requires observers that continuously monitor variables of the embedded software in order to detect anomalies and reason about root causes. There exists a variety of techniques for code instrumentation, but instrumentation might change runtime behavior and could require costly software re-certification. In this paper, we present R2U2E, a novel realization of our real-time, Realizable, Responsive, and Unobtrusive Unit (R2U2). The R2U2E observers are executed in parallel on a dedicated 16-core EPIPHANY co-processor, thereby avoiding additional computational overhead to the system under observation. A DMA-based shared memory access architecture allows R2U2E to operate without any code instrumentation or program interference.

Schumann, Johann↗
NASA NTRS2008

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.

Figueroa, Jorge F.↗
NASA NTRS2023

Flexible Data Fusion for Air Quality Estimation and Forecasting in Google Earth Engine to support Global Health Management Needs

The assessment and forecasting of air quality around the world at high spatial and temporal resolution can be enhanced by integrating data from multiple sources including models, satellites, regulatory monitors, and low-cost sensors. Such integration is subject to numerous technical challenges, however, including heterogeneous data resolution and formatting, different levels of data availability and reliability, and computational and capacity challenges to developing data fusion tools and platforms. This presentation will provide an overview of a NASA-funded effort to develop a data fusion system within the Google Earth Engine platform which integrates these air quality data sources to produce comprehensive assessments and forecasts of key air pollutants at sub-daily and sub-city scales. The system is being developed in collaboration with city- and regional-level air quality managers, and will provide them with information to the assess and anticipate the health impacts of poor air quality, track local changes in air quality due to ongoing transportation and land use changes, and identify potential gaps in their current air quality monitoring strategies. The presentation will report advances achieved through the project, including bringing local air quality monitoring data into Google Earth Engine, quantifying uncertainties in air quality estimates and forecasts, and tailored communications tools providing integration into end-user processes to meet their needs.

Carl Malings↗
NASA NTRSDate not supplied

Flexible Data Fusion for Air Quality Estimation and Forecasting in Google Earth Engine to support Global Health Management Needs

The assessment and forecasting of air quality around the world at high spatial and temporal resolution can be enhanced by integrating data from multiple sources including models, satellites, regulatory monitors, and low-cost sensors. Such integration is subject to numerous technical challenges, however, including heterogeneous data resolution and formatting, different levels of data availability and reliability, and computational and capacity challenges to developing data fusion tools and platforms. This presentation will provide an overview of a NASA-funded effort to develop a data fusion system within the Google Earth Engine platform which integrates these air quality data sources to produce comprehensive assessments and forecasts of key air pollutants at sub-daily and sub-city scales. The system is being developed in collaboration with city- and regional-level air quality managers and will provide them with information to the assess and anticipate the health impacts of poor air quality, track local changes in air quality due to ongoing transportation and land use changes, and identify potential gaps in their current air quality monitoring strategies. The presentation will report advances achieved through the project, including bringing local air quality monitoring data into Google Earth Engine, quantifying uncertainties in air quality estimates and forecasts, and tailored communications tools providing integration into end-user processes to meet their needs.

Nathan R. Pavlovic↗
NASA NTRS2005

NASA Integrated Vehicle Health Management (NIVHM) A New Simulation Architecture: An Investigation - Part I

The overall objective of this research is to explore the development of a new architecture for simulating a vehicle health monitoring system in support of NASA s on-going Integrated Vehicle Health Monitoring (IVHM) initiative. As discussed in NASA MSFC s IVHM workshop on June 29-July 1, 2004, a large number of sensors will be required for a robust IVHM system. The current simulation architecture is incapable of simulating the large number of sensors required for IVHM. Processing the data from the sensors into a format that a human operator can understand and assimilate in a timely manner will require a paradigm shift. Data from a single sensor is, at best, suspect and in order to overcome this deficiency, redundancy will be required for tomorrow s sensors. The sensor technology of tomorrow will allow for the placement of thousands of sensors per square inch. The major obstacle to overcome will then be how we can mitigate the torrent of data from raw sensor data to useful information to computer assisted decisionmaking.

Sheppard, Gene↗
NASA NTRS2022

Hybrid Model Based Approaches for Systems Health Management and Prognostics

This is a previously approved and published presentation. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Present achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. In principle, data-driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data-driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety-critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Prognostics↗
NASA NTRS2003

Support of Integrated Health Management (IHM) through Automated Analyses of Flowfield-Derived Spectrographic Data

Flow-field analysis techniques under continuing development at NASA's Marshall Space Flight Center are the foundation for a new type of health monitoring instrumentation for propulsion systems and a vast range of other applications. Physics, spectroscopy, mechanics, optics, and cutting-edge computer sciences merge to make recent developments in such instrumentation possible. Issues encountered in adaptation of such a system to future space vehicles, or retrofit in existing hardware, are central to the work. This paper is an overview of the collaborative efforts results, current efforts, and future plans.

Patrick, Marshall C.↗