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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2010

Fault Management Technology Maturation for NASA's Constellation Program

This slide presentation reviews the maturation of fault management technology in preparation for the Constellation Program. There is a review of the Space Shuttle Main Engine (SSME) and a discussion of a couple of incidents with the shuttle main engine and tanking that indicated the necessity for predictive maintenance. Included is a review of the planned Ares I-X Ground Diagnostic Prototype (GDP) and further information about detection and isolation of faults using Testability Engineering and Maintenance System (TEAMS). Another system that being readied for use that detects anomalies, the Inductive Monitoring System (IMS). The IMS automatically learns how the system behaves and alerts operations it the current behavior is anomalous. The comparison of STS-83 and STS-107 (i.e., the Columbia accident) is shown as an example of the anomaly detection capabilities.

Waterman, Robert D.↗

NASA NTRS2017

Data Collection and Analysis Software Development for Rotor Dynamics Testing in Spin Laboratory

Gas turbine engine components undergo high rotational loading another complex environmental conditions. Such operating environment leads these components to experience damages and cracks that can cause catastrophic failure during flights. There are traditional crack detections and health monitoring methodologies currently being used which rely on periodic routine maintenances, nondestructive inspections that often times involve engine and components dis-assemblies. These methods do not also offer adequate information about the faults, especially, if these faults at subsurface or not clearly evident. At NASA Glenn research center, the rotor dynamics laboratory is presently involved in developing newer techniques that are highly dependent on sensor technology to enable health monitoring and prediction of damage and cracks in rotor disks. These approaches are noninvasive and relatively economical. Spin tests are performed using a subscale test article mimicking turbine rotor disk undergoing rotational load. Non-contact instruments such as capacitive and microwave sensors are used to measure the blade tip gap displacement and blade vibrations characteristics in an attempt develop a physics based model to assess/predict the faults in the rotor disk. Data collection is a major component in this experimental-analytical procedure and as a result, an upgrade to an older version of the data acquisition software which is based on LabVIEW program has been implemented to support efficiently running tests and analyze the results. Outcomes obtained from the tests data and related experimental and analytical rotor dynamics modeling including key features of the updated software are presented and discussed.

Rotor Dynamics↗

DOE OSTI2024

Advanced Sensor Deployment for Distribution System State Estimation and Fault Identification

Distribution systems are currently facing steep operational challenges as a result of the rapidly increasing integration of renewables and other distributed energy resources (DERs) at both the primary and secondary circuit levels. Distribution utilities and system operators have traditionally had some visibility of their primary circuits using low-frequency supervisory control and data acquisition systems, and they have had very poor if not zero visibility of the secondary circuits where the presence of DERs is constantly increasing. Therefore, this paper presents simulation studies to demonstrate the benefits of an advanced, high-fidelity sensor technology, called as the Meta-Alert System (MAS), developed by Electrical Grid Monitoring, Ltd. (EGM), on the distribution grid. First, a reliable model of the EGM sensors is developed, and then two use cases, distribution system state estimation (DSSE) and fault identification are simulated to evaluate the performance of the MAS technology. Simulation results on the Electric Power Research Institute J1 feeder demonstrate that the MAS can effectively participate in system-level DSSE programs and can detect and locate faults faster than traditional distribution protection schemes.

distribution system↗

NASA NTRS1983

A portable x-ray analyzer for wearmetal particles in lubricants

An extensive program exists for the systematic sampling and analysis of lubricants in U.S. Air Force aircraft engines. The sudden development of excessive engine wear can usually be detected on the basis of monitoring the concentration of several key metallic elements for each aircraft engine. In many cases even the specific fault within an engine can be recognized. The two types of equipment which are currently being employed for the conduction of oil analyses make use of atomic emission or atomic absorption spectroscopy. In both cases, heavy, large, and relatively fragile pieces of equipment are involved. Difficulties concerning the transportation of these devices have led to the development of a small, sturdy, and easily portable wearmetal analyzer. The analyzer makes use of an X-ray fluorescence technique.

Clark, B. C.↗

NASA NTRS2004

Advanced Diagnostic System on Earth Observing One

In this infusion experiment, the Livingstone 2 (L2) model-based diagnosis engine, developed by the Computational Sciences division at NASA Ames Research Center, has been uploaded to the Earth Observing One (EO-1) satellite. L2 is integrated with the Autonomous Sciencecraft Experiment (ASE) which provides an on-board planning capability and a software bridge to the spacecraft's 1773 data bus. Using a model of the spacecraft subsystems, L2 predicts nominal state transitions initiated by control commands, monitors the spacecraft sensors, and, in the case of failure, isolates the fault based on the discrepant observations. Fault detection and isolation is done by determining a set of component modes, including most likely failures, which satisfy the current observations. All mode transitions and diagnoses are telemetered to the ground for analysis. The initial L2 model is scoped to EO-1's imaging instruments and solid state recorder. Diagnostic scenarios for EO-1's nominal imaging timeline are demonstrated by injecting simulated faults on-board the spacecraft. The solid state recorder stores the science images and also hosts: the experiment software. The main objective of the experiment is to mature the L2 technology to Technology Readiness Level (TRL) 7. Experiment results are presented, as well as a discussion of the challenging technical issues encountered. Future extensions may explore coordination with the planner, and model-based ground operations.

Hayden, Sandra C.↗

DOE OSTI2022

Feature extraction for subtle anomaly detection using semi-supervised learning

The demand for automated and effective monitoring techniques has soared with the increased digitization of industrial monitoring systems. State-of-the-art machine learning methods are effectively detecting abrupt changes in system states. However, these methods lack comparable maturity in detecting subtle changes that may be signs of incipient faults. This manuscript argues that the current anomaly detection methods can be enhanced by exploring weak patterns to enable subtle variation detection. Specifically, the concept of semi-supervised learning is employed, with labels representing knowledge about some anomalous conditions of a system. The basic idea is to extract a candidate set of weak patterns discarded by state-of-the-art baselining algorithms. With few labeled anomalous data, the algorithm selects the weak patterns and allows for their possible fusion using the highest sensitivity to the labeled anomalies. Here, the method’s applicability is demonstrated using a representative pressurized water reactor (PWR) model simulated by Dymola.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS1992

Fault detection using a two-model test for changes in the parameters of an autoregressive time series

This article describes an investigation of a statistical hypothesis testing method for detecting changes in the characteristics of an observed time series. The work is motivated by the need for practical automated methods for on-line monitoring of Deep Space Network (DSN) equipment to detect failures and changes in behavior. In particular, on-line monitoring of the motor current in a DSN 34-m beam waveguide (BWG) antenna is used as an example. The algorithm is based on a measure of the information theoretic distance between two autoregressive models: one estimated with data from a dynamic reference window and one estimated with data from a sliding reference window. The Hinkley cumulative sum stopping rule is utilized to detect a change in the mean of this distance measure, corresponding to the detection of a change in the underlying process. The basic theory behind this two-model test is presented, and the problem of practical implementation is addressed, examining windowing methods, model estimation, and detection parameter assignment. Results from the five fault-transition simulations are presented to show the possible limitations of the detection method, and suggestions for future implementation are given.

Scholtz, P.↗

NASA NTRS1993

Implementing a real time reasoning system for robust diagnosis

The objective of the Thermal Control System Automation Project (TCSAP) is to develop an advanced fault detection, isolation, and recovery (FDIR) capability for use on the Space Station Freedom (SSF) External Active Thermal Control System (EATCS). Real-time monitoring, control, and diagnosis of the EATCS will be performed with a knowledge based system (KBS). Implementation issues for the current version of the KBS are discussed.

Hill, Tim↗

NASA NTRS2009

General Purpose Data-Driven System Monitoring for Space Operations

Modern space propulsion and exploration system designs are becoming increasingly sophisticated and complex. Determining the health state of these systems using traditional methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or for analysis of archived events. Ongoing and developing IMS space operations applications include International Space Station flight control, satellite vehicle system health management, launch vehicle ground operations, and fleet supportability. As a common thread of discussion this paper will employ the evolution of the IMS data-driven technique as related to several Integrated Systems Health Management (ISHM) elements. Thematically, the projects listed will be used as case studies. The maturation of IMS via projects where it has been deployed, or is currently being integrated to aid in fault detection will be described. The paper will also explain how IMS can be used to complement a suite of other ISHM tools, providing initial fault detection support for diagnosis and recovery.

Satellites↗

NASA NTRS2006

Developing Signal-Pattern-Recognition Programs

Pattern Interpretation and Recognition Application Toolkit Environment (PIRATE) is a block-oriented software system that aids the development of application programs that analyze signals in real time in order to recognize signal patterns that are indicative of conditions or events of interest. PIRATE was originally intended for use in writing application programs to recognize patterns in space-shuttle telemetry signals received at Johnson Space Center's Mission Control Center: application programs were sought to (1) monitor electric currents on shuttle ac power busses to recognize activations of specific power-consuming devices, (2) monitor various pressures and infer the states of affected systems by applying a Kalman filter to the pressure signals, (3) determine fuel-leak rates from sensor data, (4) detect faults in gyroscopes through analysis of system measurements in the frequency domain, and (5) determine drift rates in inertial measurement units by regressing measurements against time. PIRATE can also be used to develop signal-pattern-recognition software for different purposes -- for example, to monitor and control manufacturing processes.

Shelton, Robert O.↗

NASA NTRS2006

Real-Time Diagnosis of Faults Using a Bank of Kalman Filters

A new robust method of automated real-time diagnosis of faults in an aircraft engine or a similar complex system involves the use of a bank of Kalman filters. In order to be highly reliable, a diagnostic system must be designed to account for the numerous failure conditions that an aircraft engine may encounter in operation. The method achieves this objective though the utilization of multiple Kalman filters, each of which is uniquely designed based on a specific failure hypothesis. A fault-detection-and-isolation (FDI) system, developed based on this method, is able to isolate faults in sensors and actuators while detecting component faults (abrupt degradation in engine component performance). By affording a capability for real-time identification of minor faults before they grow into major ones, the method promises to enhance safety and reduce operating costs. The robustness of this method is further enhanced by incorporating information regarding the aging condition of an engine. In general, real-time fault diagnostic methods use the nominal performance of a "healthy" new engine as a reference condition in the diagnostic process. Such an approach does not account for gradual changes in performance associated with aging of an otherwise healthy engine. By incorporating information on gradual, aging-related changes, the new method makes it possible to retain at least some of the sensitivity and accuracy needed to detect incipient faults while preventing false alarms that could result from erroneous interpretation of symptoms of aging as symptoms of failures. The figure schematically depicts an FDI system according to the new method. The FDI system is integrated with an engine, from which it accepts two sets of input signals: sensor readings and actuator commands. Two main parts of the FDI system are a bank of Kalman filters and a subsystem that implements FDI decision rules. Each Kalman filter is designed to detect a specific sensor or actuator fault. When a sensor or actuator fault occurs, large estimation errors are generated by all filters except the one using the correct hypothesis. By monitoring the residual output of each filter, the specific fault that has occurred can be detected and isolated on the basis of the decision rules. A set of parameters that indicate the performance of the engine components is estimated by the "correct" Kalman filter for use in detecting component faults. To reduce the loss of diagnostic accuracy and sensitivity in the face of aging, the FDI system accepts information from a steady-state-condition-monitoring system. This information is used to update the Kalman filters and a data bank of trim values representative of the current aging condition.

Kobayashi, Takahisa↗

NASA NTRS2012

General Purpose Data-Driven System Monitoring for Space Operations

Modern space propulsion and exploration system designs are becoming increasingly sophisticated and complex. Determining the health state of these systems using traditional methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or for analysis of archived events. Ongoing and developing IMS space operations applications include International Space Station flight control, spacecraft vehicle system health management, launch vehicle ground operations, and fleet supportability. As a common thread of discussion this paper will employ the evolution of the IMS data-driven technique as related to several Integrated Systems Health Management (ISHM) elements. Thematically, the projects listed will be used as case studies. The maturation of IMS via projects where it has been deployed or is currently being integrated to aid in fault detection will be described. The paper will also explain how IMS can be used to complement a suite of other ISHM tools, providing initial fault detection support for diagnosis and recovery

Space Propulsion↗

DOE OSTI2026

Advancing Industry 4.0: Multimodal Sensor Fusion for AI-Based Fault Detection in 3D Printing

Additive manufacturing, particularly fused deposition modeling, is transforming modern production by enabling rapid prototyping and complex part fabrication. However, its layer-by-layer process remains vulnerable to faults such as nozzle clogging, filament runout, and layer misalignment, which compromise print quality and reliability. Traditional inspection methods are costly, time-intensive, and often limited to post-process analysis, making them unsuitable for real-time intervention. In this current study, the authors developed a novel, low-cost, and portable faultdetection system that leverages multimodal sensor fusion and artificial intelligence for real-time monitoring in FDM-based 3D printing. The system integrates acoustic, vibration, and thermal sensing into a non-intrusive architecture, capturing complementary data streams that reflect both mechanical and process-related anomalies. Acoustic and thermal sensors operate in a fully contactless manner, while the vibration sensor requires minimal attachment such that it will not interfere with printer hardware, thereby preserving portability and ease of deployment. The multimodal signals are processed into spectrograms and time-frequency features, which are classified using convolutional neural networks for intelligent fault detection. The proposed system advances Industry 4.0 objectives by offering an affordable, scalable, and practical monitoring solution that improves faultdetection accuracy, reduces waste, and supports sustainable, adaptive manufacturing.

42 ENGINEERING↗

NASA NTRS1989

Advanced power system protection and incipient fault detection and protection of spaceborne power systems

This research concentrated on the application of advanced signal processing, expert system, and digital technologies for the detection and control of low grade, incipient faults on spaceborne power systems. The researchers have considerable experience in the application of advanced digital technologies and the protection of terrestrial power systems. This experience was used in the current contracts to develop new approaches for protecting the electrical distribution system in spaceborne applications. The project was divided into three distinct areas: (1) investigate the applicability of fault detection algorithms developed for terrestrial power systems to the detection of faults in spaceborne systems; (2) investigate the digital hardware and architectures required to monitor and control spaceborne power systems with full capability to implement new detection and diagnostic algorithms; and (3) develop a real-time expert operating system for implementing diagnostic and protection algorithms. Significant progress has been made in each of the above areas. Several terrestrial fault detection algorithms were modified to better adapt to spaceborne power system environments. Several digital architectures were developed and evaluated in light of the fault detection algorithms.

Russell, B. Don↗

NASA NTRS1996

Feasibility Study of a Rotorcraft Health and Usage Monitoring System (HUMS): Results of Operator's Evaluation

The objective was to evaluate the feasibility of a state-of-the-art health and usage monitoring system (HUMS) to provide monitoring of critical mechanical systems on the helicopter, including motors, drive train, engines, and life-limited components. The implementation of HUMS and cost integration with current maintenance procedures was assessed from the operator's viewpoint in order to achieve expected benefits from these systems, such as enhanced safety, reduced maintenance cost, and increased availability. An operational HUMS that was installed and operated under an independent flight trial program was used as a basis for this study. The HUMS equipment and software were commercially available. Based on the results of the feasibility study, the HUMS used in the flight trial program generally demonstrated a high level of reliability in monitoring the rotor system, engines, drive train, and life-limited components. The system acted as a sentinel to warn of impending failures. A worn tail rotor pitch bearing was detected by HUMS, which had the capability for self testing to diagnose system and sensor faults. Examples of potential payback to the operator with HUMS were identified, including reduced insurance cost through enhanced safety, lower operating costs derived from maintenance credits, increased aircraft availability, and improved operating efficiency. The interfacing of HUMS with current operational procedures was assessed to require only minimal revisions to the operator's maintenance manuals. Finally the success in realizing the potential benefits from HUMS technology was found to depend on the operator, helicopter manufacturer, regulator (FAA), and HUMS supplier working together.

Romero, Raylund↗

NASA NTRS1995

Transmission diagnostic research at NASA Lewis Research Center

The NASA Lewis Research Center and the U.S. Army Research Laboratory are involved in a joint research program to advance the technology of aerospace transmissions. Within the last six years, a transmission diagnostics research team was formed to address current and future technology barriers in transmission diagnostics. The diagnostics team conducted a survey to determine critical needs of the diagnostics community. Survey results indicated that experimental verification of gear and bearing fault detection methods and damage magnitude assessment were considered the two most critical research areas of a highly reliable health and usage monitoring system. A plan was implemented by the diagnostics team to address these key research areas, by in-house research and university grants. A variety of transmission fault detection methods were applied to experimentally obtained fatigue data. Failure modes of the fatigue tests include a variety of gear pitting failures, tooth wear, tooth fracture, and bearing spalling failures. Accomplishments to date include verification of several specific gear diagnostic methods, verification of a new pattern recognition method to determine failure, and development of a new method to model gear tooth damage. This paper presents the results of these accomplishments in transmission diagnostics research at NASA Lewis Research Center.

Zakrajsek, J. J.↗

NASA NTRS1990

Automated Power-Distribution System

Automated power-distribution system monitors and controls electrical power to modules in network. Handles both 208-V, 20-kHz single-phase alternating current and 120- to 150-V direct current. Power distributed to load modules from power-distribution control units (PDCU's) via subsystem distributors. Ring busses carry power to PDCU's from power source. Needs minimal attention. Detects faults and also protects against them. Potential applications include autonomous land vehicles and automated industrial process systems.

Thomason, Cindy↗

NASA NTRS1988

A method of measuring fault latency in a digital flight control system

This paper describes the motivation, conduct, and analysis of some 2500 low-level hardware fault cases applied in automated testing at the NASA Ames Reconfigurable Digital Flight Control System Facility. Fault detection was correlated with hardware and software fault monitoring and, in limited cases, with sensitivity to flight program execution modes. The results are statistically assessed to ascertain system-level reliability implications based on a single-fault model. Extension to multiple-fault models is addressed. The overall methodology/facility itself is judged to be a promising enhancement to current practice.

Mcgough, John↗