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NASA NTRS2019

Prognostics and Health Monitoring: Application to Electric Vehicles

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in the prediction of remaining driving-time distance (for cars) or flying time-distance (for aircraft). This information is important, particularly in the case of unmanned 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 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. 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.

Electric Vehicles↗

NASA NTRS2010

Improving Bone-Health Monitoring in Astronauts: Recommended Use of Quantitative Computed Tomography [QCT] for Clinical and Operational Decisions by NASA

DXA measurement of areal bone mineral density [aBMD,g/cm2] is required by NASA for assessing skeletal integrity in astronauts. Due to the abundance of population-based data that correlate hip and spine BMDs to fragility fractures, BMD is widely applied as a predictor of fractures in the general aging population. In contrast, QCT is primarily a research technology that measures three-dimensional , volumetric BMD (vBMD,mg/cm3) of bone and is therefore capable of differentiating between cortical and trabecular components. Additionally, when combined with Finite Element Modeling [FEM], a computational tool, QCT data can be used to estimate the whole bone strength of the hip [FE strength] for a specific load vector. A recent report demonstrated that aBMD failed to correlate with incurred changes in FE strength (for fall and stance loading) by astronauts over typical 180-day ISS (International Space Station) missions. While there are no current guidelines for using QCT data in clinical practice, QCT increases the understanding of how bone structure and mineral content are affected by spaceflight and recovery on Earth. In order to understand/promote/consider the use of QCT, NASA convened a panel of clinicians specializing in osteoporosis. After reviewing the available, albeit limited, medical and research information from long-duration astronauts (e.g., data from DXA, QCT, FEM, biochemistry analyses, medical records and in-flight exercise performance) the panelists were charged with recommending how current and future research data and analyses could inform clinical and operational decisions. The Panel recommended that clinical bone tests on astronauts should include QCT (hip and lumbar spine) for occupational risk surveillance and for the estimation of whole hip bone strength as derived by FEM. FE strength will provide an improved index that NASA could use to select astronauts of optimal bone health for extended duration missions, for repeat missions or for specific mission operations.

Sibonga, J. D.↗

NASA NTRSDate not supplied

Systems Health Monitoring: Integrating FMEA into Bayesian Networks

The foreseeable high traffic density suggests that a large number of electric propulsion systems will enter the airspace, and that they will also operate at high frequency, e.g., large number of take offs and landings per unit time. The reliability of such critical systems is therefore key to ensure high safety standards in the low-altitude airspace. Diagnostic systems, which aim at identifying incipient faults, can mitigate unexpected failures or lower-than-expected reliability by performing early fault detection by monitoring the systems. A key element of fault diagnosis is fault detection and isolation (FDI), which complexity increases with the complexity of the system itself, namely the number of subsystems and components, interactions among sub-systems, and the number of sensors available. The proposed approach leverages combination of failure mode and effect analysis (FMEA) integrated with Bayesian networks, thus introducing dependability structures into a diagnostic framework to aid FDI. Faults and failure events from the FMEA are mapped within a Bayesian network, where network edges replicate the links embedded within FMEAs. The integrated framework enables the fault isolation process by identifying the probability of occurrence of specific faults or root causes given evidence observed through sensor signals. In this work, sub systems of Urban Air Mobility (UAM) type vehicle like avionics, structures, power-train etc. are taken into account to show the approach at the system level. This work integrates early design phase in the development of UAM type vehicles with diagnostic tools, which are often developed later in the product life-cycle, or retrofitted at a later time on systems. Failure mode and effect analysis (FMEA) derived for the system in the design phase is embedded within a Bayesian network (BN).

UAM↗

NASA NTRS1988

An expert system approach to turbopump health monitoring

A preliminary report is presented on the development of a system for monitoring the High Pressure Oxidizer Turbopump of the SSME. System development will proceed by defining the degradation mechanism of the turbopump, identifying those conventional and advanced sensor measurements that can monitor these mechanisms, and determining the digital processing requirements for these measurements. Once the failures and associated measurements have been defined, an expert system will be developed that can make both diagnostic and prognostic inferences about the condition and performance of the turbopump.

Perry, John G.↗

NASA NTRS1988

Plume spectrometry for liquid rocket engine health monitoring

An investigation of Space Shuttle Main Engine (SSME) testing failures identified optical events which appeared to be precursors of those failures. A program was therefore undertaken to detect plume trace phenomena characteristic of the engine and to design a monitoring system, responsive to excessive activity in the plume, capable of delivering a warning of an anomalous condition. By sensing the amount of extraneous material entrained in the plume and considering engine history, it may be possible to identify wearing of failing components in time for a safe shutdown and thus prevent a catastrophic event. To investigate the possibilities of safe shutdown and thus prevent a monitor to initiate the shutdown procedure, a large amount of plume data were taken from SSME firings using laboratory instrumentation. Those data were used to design a more specialized instrument dedicated to rocket plume diagnostics. The spectral wavelength range of the baseline data was about 220 nanometers (nm) to 15 micrometer with special attention given to visible and near UV. The data indicates that a satisfactory design will include a polychromator covering the range of 250 nM to 1000 nM, along with a continuous coverage spectrometer, each having a resolution of at least 5A degrees. The concurrent requirements for high resolution and broad coverage are normally at odds with one another in commercial instruments, therefore necessitating the development of special instrumentation. The design of a polychromator is reviewed herein, with a detailed discussion of the continuous coverage spectrometer delayed to a later forum. The program also requires the development of applications software providing detection, variable background discrimination, noise reduction, filtering, and decision making based on varying historical data.

Powers, William T.↗

NASA NTRS1989

Integrated control and health monitoring capacitive displacement sensor development task. Orbit transfer rocket engine technology program

The development of a miniature multifunction turbomachinery shaft displacement sensor using state-of-the-art non-contract capacitive sensing technology is described. Axial displacement, radial displacement, and speed are sensed using a single probe within the envelope normally required for a single function. A survey of displacement sensing technology is summarized including inductive, capacitive, optical and ultrasonic techniques. The design and operation of an experimental triple function sensor is described. Test results are included showing calibration tests and simultaneous dynamic testing of multiple functions. Recommendations for design changes are made to improve low temperature performance, reliability, and for design of a flight type signal conditioning unit.

Collamore, Frank N.↗

NASA NTRS1989

Model and simulation development for turbopump health monitoring by parameter estimation

The nonlinear equations describing the flow, pressure and temperature dynamics of the High Pressure Fuel Turbopump of the Space Shuttle Main Engine are summarized and implemented in a digital simulation. Some closed form approximations to the operating curves of the turbopump are made so that closed form derivatives exist. These equations and approximations are then used to develop an extended Kalman filter that estimates the dynamic states of the turbopump and a parameter that can reflect turbopump degradation. The filter is implemented as a digital computer program and applied to data generated by the digital simulation of the turbopump with substantial noise added to the output signals. The results indicate that the parameter estimate converges quickly and accurately. This suggests that the parameter estimate can be used to monitor for turbopump degradations affecting its value.

Walker, Bruce K.↗

NASA NTRS1989

Integrated controls and health monitoring fiberoptic shaft monitor

Recent work was performed on development optical technology to provide real time monitoring of shaft speed, shaft axial displacement, and shaft orbit of the OTVE hydrostatic bearing tester. Results show shaft axial displacement can be optically measured (at the same time as shaft orbital motion and speed) to within 0.3 mills by two fiber optic deflectometers. The final results of this condition monitoring development effort are presented.

Coleman, P.↗

NASA NTRS1990

Neural network approach to Space Shuttle Main Engine health monitoring

A neural network was trained to distinguish anomalies in Space Shuttle Main Engine sensor data from noisy normal steady-state sensor data. Power spectra of successive windows of individual sensor data were presented to a neural network using Kohonen's topological feature map training algorithm. The trained network for each sensor was then tested to determine if it would detect anomalies in the sensor data, and if so, the time at which the anomaly would be detected. Power spectra from a few hundred seconds of actual test data from NASA tests 901-364 and 904-044 were used to test the network. In both cases, the neural network detected the onset of anomalous engine behavior at approximately the same time within each test as the onset times reported by NASA and Rocketdyne experts in their post-test analyses.

Whitehead, Bruce A.↗

NASA NTRS1991

AI mass spectrometers for space shuttle health monitoring

The facility Hazardous Gas Detection System (HGDS) at Kennedy Space Center (KSC) is a mass spectrometer based gas analyzer. Two instruments make up the HGDS, which is installed in a prime/backup arrangement, with the option of using both analyzers on the same sample line, or on two different lines simultaneously. It is used for monitoring the Shuttle during fuel loading, countdown, and drainback, if necessary. The use of complex instruments, operated over many shifts, has caused problems in tracking the status of the ground support equipment (GSE) and the vehicle. A requirement for overall system reliability has been a major force in the development of Shuttle GSE, and is the ultimate driver in the choice to pursue artificial intelligence (AI) techniques for Shuttle and Advanced Launch System (ALS) mass spectrometer systems. Shuttle applications of AI are detailed.

Adams, F. W.↗

NASA NTRS1990

Approach to SSME health monitoring. II - Exhaust plume emission spectroscopy at the DTF

The Diagnostics Testbed Facility (DTF) located at the Stennis Space Center (SSC) is used for obtaining extensive sets of H2O2 exhaust plume emission spectral data for the SSME critical components related elements and materials. The SSME related elements and materials are simulated by mixing appropriate amounts of compounds of their respective constituent elements in an aqueous solution which is injected into the combustion chamber of the DTFT. Five of the most critical components of the SSME which have experienced very severe wear and tear problems in the past are analyzed. These are high pressure turbopump (HPTP) turbine blades, HPTP turbine disks, HPTP bearing, main injector LOX posts, and the main combustion chamber structural shell. The alloys used in the manufacturing of these components are MAR-M 246 + Hf, Waspaloy X, AISI 440C, Haynes 188, and Inconel 718, respectively. The experimental setup and procedures at the DTF are described; stratospheric data for the five alloys are presented; and strategies for the material identification in the SSME exhaust plume are discussed.

Tejwani, Gopal D.↗

NASA NTRS1991

On-orbit damage detection and health monitoring of large space trusses - Status and critical issues

The literature in the fields of structural identification, mode shape expansion and orthogonalization, and on-orbit damage location of large space trusses is reviewed. The use of a Dynamic Scale Model Technology (DSMT) hybrid scale model for damage location research and as a universal test bed for other damage location methods is proposed as a means of comparing and evaluating existing and newly developed methods in the named areas. Issues concerning on-orbit data acquisition, data accuracy, and data quality relevant to structural dynamic system identification which require extensive research are identified.

Kashangaki, Thomas A.-L.↗

NASA NTRS1992

Integrated health monitoring and controls for rocket engines

Current research in intelligent control systems at the Lewis Research Center is described in the context of a functional framework. The framework is applicable to a variety of reusable space propulsion systems for existing and future launch vehicles. It provides a 'road map' technology development to enable enhanced engine performance with increased reliability, durability, and maintainability. The framework hierarchy consists of a mission coordination level, a propulsion system coordination level, and an engine control level. Each level is described in the context of the Space Shuttle Main Engine. The concept of integrating diagnostics with control is discussed within the context of the functional framework. A distributed real time simulation testbed is used to realize and evaluate the functionalities in closed loop.

Merrill, W. C.↗

NASA NTRS1993

An artificial intelligence-based structural health monitoring system for aging aircraft

To reduce operating expenses, airlines are now using the existing fleets of commercial aircraft well beyond their originally anticipated service lives. The repair and maintenance of these 'aging aircraft' has therefore become a critical safety issue, both to the airlines and the Federal Aviation Administration. This paper presents the results of an innovative research program to develop a structural monitoring system that will be used to evaluate the integrity of in-service aerospace structural components. Currently in the final phase of its development, this monitoring system will indicate when repair or maintenance of a damaged structural component is necessary.

Grady, Joseph E.↗

NASA NTRS1992

A fuzzy case based reasoning tool for model based approach to rocket engine health monitoring

In this system we develop a fuzzy case based reasoner that can build a case representation for several past anomalies detected, and we develop case retrieval methods that can be used to index a relevant case when a new problem (case) is presented using fuzzy sets. The choice of fuzzy sets is justified by the uncertain data. The new problem can be solved using knowledge of the model along with the old cases. This system can then be used to generalize the knowledge from previous cases and use this generalization to refine the existing model definition. This in turn can help to detect failures using the model based algorithms.

Krovvidy, Srinivas↗

NASA NTRS1992

Integrated health monitoring and controls for rocket engines

Merrill, W. C.↗

NASA NTRS1993

Approach to SSME health monitoring. III - Exhaust plume emission spectroscopy: Recent results and detailed analysis

Spectral data for two recent A-1 test firings, 901-717 and 901-718, obtained from an Optical Multichannel Analyzer and an Optical Plume Anomaly Detector, are presented. The spectral data encompasses the database of SSME critical components and materials and the spectral database for the SSME related elements and materials. Relatively strong and continuous emissions from Cr and Fe atomic transitions were observed starting at engine start plus 494 s and persisting until the engine shut off at engine start plus 520 s. These emissions are considered to be emanated from the SSME material AISI 440C, which is traced to high pressure turbopump bearings.

Tejwani, Gopal D.↗

NASA NTRS1991

Genetic algorithm based input selection for a neural network function approximator with applications to SSME health monitoring

A genetic algorithm is used to select the inputs to a neural network function approximator. In the application considered, modeling critical parameters of the space shuttle main engine (SSME), the functional relationship between measured parameters is unknown and complex. Furthermore, the number of possible input parameters is quite large. Many approaches have been used for input selection, but they are either subjective or do not consider the complex multivariate relationships between parameters. Due to the optimization and space searching capabilities of genetic algorithms they were employed to systematize the input selection process. The results suggest that the genetic algorithm can generate parameter lists of high quality without the explicit use of problem domain knowledge. Suggestions for improving the performance of the input selection process are also provided.

Peck, Charles C.↗