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A recently invented method of nonintrusively detecting faults in piezoelectric devices involves measurement of the resonance frequencies of inductor capacitor (LC) resonant circuits. The method is intended especially to enable diagnosis of piezoelectric sensors, actuators, and sensor/actuators that are embedded in structures and/or are components of multilayer composite material structures.
Topics covered include: Laser Truss Sensor for Segmented Telescope Phasing; Qualifications of Bonding Process of Temperature Sensors to Deep-Space Missions; Optical Sensors for Monitoring Gamma and Neutron Radiation; Compliant Tactile Sensors; Cytometer on a Chip; Measuring Input Thresholds on an Existing Board; Scanning and Defocusing Properties of Microstrip Reflectarray Antennas; Cable Tester Box; Programmable Oscillator; Fault-Tolerant, Radiation-Hard DSP; Sub-Shot Noise Power Source for Microelectronics; Asynchronous Message Service Reference Implementation; Zero-Copy Objects System; Delay and Disruption Tolerant Networking MACHETE Model; Contact Graph Routing; Parallel Eclipse Project Checkout; Technique for Configuring an Actively Cooled Thermal Shield in a Flight System; Use of Additives to Improve Performance of Methyl Butyrate-Based Lithium-Ion Electrolytes; Li-Ion Cells Employing Electrolytes with Methyl Propionate and Ethyl Butyrate Co-Solvents; Improved Devices for Collecting Sweat for Chemical Analysis; Tissue Photolithography; Method for Impeding Degradation of Porous Silicon Structures; External Cooling Coupled to Reduced Extremity Pressure Device; A Zero-Gravity Cup for Drinking Beverages in Microgravity; Co-Flow Hollow Cathode Technology; Programmable Aperture with MEMS Microshutter Arrays; Polished Panel Optical Receiver for Simultaneous RF/Optical Telemetry with Large DSN Antennas; Adaptive System Modeling for Spacecraft Simulation; Lidar-Based Navigation Algorithm for Safe Lunar Landing; Tracking Object Existence From an Autonomous Patrol Vehicle; Rad-Hard, Miniaturized, Scalable, High-Voltage Switching Module for Power Applications; and Architecture for a 1-GHz Digital RADAR.
Tectonic faults fail through a spectrum of slip modes, ranging from slow aseismic creep to rapid slip during earthquakes. Understanding the seismic radiation emitted during these slip modes is key for advancing earthquake science and earthquake hazard assessment. In this work, we use laboratory friction experiments instrumented with ultrasonic sensors to document the seismic radiation properties of slow and fast laboratory earthquakes. Stick-slip experiments were conducted at a constant loading rate of 8 μm/s and the normal stress was systematically increased from 7 to 15 MPa. We produced a full spectrum of slip modes by modulating the loading stiffness in tandem with the fault zone normal stress. Acoustic emission data were recorded continuously at 5 MHz. We demonstrate that the full continuum of slip modes radiate measurable high-frequency energy between 100 and 500 kHz, including the slowest events that have peak fault slip rates <100 μm/s. The peak amplitude of the high-frequency time-domain signals scales systematically with fault slip velocity. Stable sliding experiments further support the connection between fault slip rate and high-frequency radiation. Experiments demonstrate that the origin of the high-frequency energy is fundamentally linked to changes in fault slip rate, shear strain, and breaking of contact junctions within the fault gouge. Our results suggest that having measurements close to the fault zone may be key for documenting seismic radiation properties and fully understanding the connection between different slip modes.
A total of 14 extraction kickers., with one as the hot spare., are in service to extract protons out of the storage ring at the SNS. The jitter issue and the short lifetime of the switches were resolved after the thyratrons were replaced with solid state switches in 2018. This paper discusses the recent improvements. Two thyristor switches suffered overheating damage in separate incidents. One was due to the oil pump failure and the other was the result of a disconnected oil hose. An ultrasonic flow meter and a fiber optic temperature monitoring system have been installed for each extraction kicker power supply. The flow meter continuously monitors the entire tank oil flow. The temperature monitor detects the thyristor switch real-time temperatures in three locations. Fault thresholds are selected so that the thyristor switches are protected from overheating damage. Alarms are configured to alert staff to take actions before faults occur. In addition., the cause of an intermittent misfire issue was identified, and the solution was implemented. Lastly., a future oscilloscope upgrade and an oil level sensor are discussed.
Initial results obtained from an investigation using pattern recognition techniques for identifying fault modes in the Deep Space Network (DSN) 70 m antenna control loops are described. The overall background to the problem is described, the motivation and potential benefits of this approach are outlined. In particular, an experiment is described in which fault modes were introduced into a state-space simulation of the antenna control loops. By training a multilayer feed-forward neural network on the simulated sensor output, classification rates of over 95 percent were achieved with a false alarm rate of zero on unseen tests data. It concludes that although the neural classifier has certain practical limitations at present, it also has considerable potential for problems of this nature.
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.
The LEADER expert system has been developed for automatic learning tasks encompassing real-time detection, identification, verification, and correction of anomalous propulsion system operations, using a set of sensors to monitor engine component performance to ascertain anomalies in engine dynamics and behavior. Two diagnostic approaches are embodied in LEADER's architecture: (1) learning and identifying engine behavior patterns to generate novel hypotheses about possible abnormalities, and (2) the direction of engine sensor data processing to perform resoning based on engine design and functional knowledge, as well as the principles of the relevant mechanics and physics.
In nuclear power plants (NPPs), timely identification of sensor and human errors is critical to ensure safe and efficient plant operations. Anomaly detection models can be employed for this task. However, traditional anomaly detection approaches may have high dependency on labeled datasets and struggle with adaptability in complex, dynamic environments. Reinforcement learning (RL) has demonstrated significant potential in fault diagnosis and anomaly detection; however, its application to anomaly detection in NPPs remains a relatively underexplored research direction. Hence, to address this gap, in this study, we present a novel physics-informed reinforcement learning model, PIRL-AD: Physics-Informed Reinforcement Learning for Anomaly Detection, that integrates domain knowledge from calorimetric equations into the RL framework for enhanced sensor and human error anomaly detection. We evaluate the performance of PIRL-AD against a non-physics informed RL benchmark and a support vector machine (SVM) on data collected from a forced flow loop testbed. Experimental results suggest that PIRL-AD outperforms other baselines on a range of anomalous datasets that include both sensor and human-induced anomalies across key performance metrics, statistically outperforming the RL and SVM benchmarks with respect to geometric mean (respectively, 92.96% vs. 91.06% vs. 83.01%) and F1-score (respectively, 89.23% vs. 86.98% vs. 77.01%). Furthermore, the findings suggest the potential of physics-integrated reinforcement learning models for enhanced anomaly detection performance in NPPs.
Traditional methods to protect composite aircraft from lightning strike damage rely on a conductive layer embedded on or within the surface of the aircraft composite skin. This method is effective at preventing major direct effect damage and minimizes indirect effects to aircraft systems from lightning strike attachment, but provides no additional benefit for the added parasitic weight from the conductive layer. When a known lightning strike occurs, the points of attachment and detachment on the aircraft surface are visually inspected and checked for damage by maintenance personnel to ensure continued safe flight operations. A new multi-functional lightning strike protection (LSP) method has been developed to provide aircraft lightning strike protection, damage detection and diagnosis for composite aircraft surfaces. The method incorporates a SansEC sensor array on the aircraft exterior surfaces forming a "Smart skin" surface for aircraft lightning zones certified to withstand strikes up to 100 kiloamperes peak current. SansEC sensors are open-circuit devices comprised of conductive trace spiral patterns sans (without) electrical connections. The SansEC sensor is an electromagnetic resonator having specific resonant parameters (frequency, amplitude, bandwidth & phase) which when electromagnetically coupled with a composite substrate will indicate the electrical impedance of the composite through a change in its resonant response. Any measureable shift in the resonant characteristics can be an indication of damage to the composite caused by a lightning strike or from other means. The SansEC sensor method is intended to diagnose damage for both in-situ health monitoring or ground inspections. In this paper, the theoretical mathematical framework is established for the use of open circuit sensors to perform damage detection and diagnosis on carbon fiber composites. Both computational and experimental analyses were conducted to validate this new method and system for aircraft composite damage detection and diagnosis. Experimental test results on seeded fault damage coupons and computational modeling simulation results are presented. This paper also presents the shielding effectiveness along with the lightning direct effect test results from several different SansEC LSP and baseline protected and unprotected carbon fiber reinforced polymer (CFRP) test panels struck at 40 and 100 kiloamperes following a universal common practice test procedure to enable damage comparisons between SansEC LSP configurations and common practice copper mesh LSP approaches. The SansEC test panels were mounted in a LSP test bed during the lightning test. Electrical, mechanical and thermal parameters were measured during lightning attachment and are presented with post test nondestructive inspection comparisons. The paper provides correlational results between the SansEC sensors computed electric field distribution and the location of the lightning attachment on the sensor trace and visual observations showing the SansEC sensor's affinity for dispersing the lightning attachment.
SUMMARY We image the internal structure of the San Jacinto fault zone (SJFZ) near Anza, California, with seismic data recorded by two dense arrays (RA and RR) from ∼42 000 local and ∼180 teleseismic events occurring between 2012 and 2017. The RA linear array has short aperture (∼470 m long with 12 strong motion sensors) and recorded for the entire analysed time window, whereas the RR is a large three-component nodal array (97 geophones across a ∼2.4 km × 1.4 km area) that operated for about a month in September–October 2016. The SJFZ at the site contains three near-parallel surface traces F1, F2 and F3 from SW to NE that have accommodated several Mw > 6 earthquakes in the past 15 000 yr. Waveform changes in the fault normal direction indicate structural discontinuities that are consistent with the three fault surface traces. Relative slowness from local events and delay time analysis of teleseismic arrivals in the fault normal direction suggest a slower SW side than the NE with a core damage zone between F1 and F2. This core damage zone causes ∼0.05 s delay at stations RR26–31 in the teleseismic P arrivals compared with the SW-most station, and generates both P- and S-type fault zone trapped waves. Inversion of S trapped waves indicates the core damaged structure is ∼100 m wide, ∼4 km deep with a Q value of ∼20 and 40 per cent S-wave velocity reduction compared with bounding rocks. Fault zone head waves observed at stations SW of F3 indicate a local bimaterial interface that separates the locally faster NE block from the broad damage zone in the SW at shallow depth and merges with a deep interface that separates the regionally faster NE block from rocks to the SW with slower velocities at greater depth. The multiscale structural components observed at the site are related to the geological and earthquake rupture history at the site, and provide important information on the preferred NW propagation of earthquake ruptures on the San Jacinto fault.
The purpose of the NASA IVHM Technology Experiment for X-vehicles (NITEX) is to advance the development of selected IVHM technologies in a flight environment and to demonstrate the potential for reusable launch vehicle ground processing savings. The technologies to be developed and demonstrated include system-level and detailed diagnostics for real-time fault detection and isolation, prognostics for fault prediction, automated maintenance planning based on diagnostic and prognostic results, and a microelectronics hardware platform. Complete flight The Evolution of Flexible Insulation as IVHM consists of advanced sensors, distributed data acquisition, data processing that includes model-based diagnostics, prognostics and vehicle autonomy for control or suggested action, and advanced data storage. Complete ground IVHM consists of evolved control room architectures, advanced applications including automated maintenance planning and automated ground support equipment. This experiment will advance the development of a subset of complete IVHM.
An approach is presented supporting analysis, modeling, and test validation of operational flight instrumentation (OFI) that facilitates critical functions for the Space Launch System (SLS) main propulsion system (MPS). Certain types of OFI sensors were shown to exhibit highly nonlinear and non-gaussian noise characteristics during acceptance testing, motivating the development of advanced modeling and simulation (M&S) capability to support algorithm verification and flight certification. Hardware model and algorithm simulation fidelity was informed by a risk scoring metric; redesign of high-risk algorithms using test-validated sensor models significantly improved their expected performance as evaluated using Monte Carlo acceptance sampling methods. Autonomous functions include closed-loop ullage pressure regulation, pressurant leak detection, and fault isolation for automated safing and crew caution and warning (C&W).
A new nuclear fuel cycle test bed is being built at Idaho National Laboratory to support the purification of special nuclear material recovered from used fuel. The test bed provides an opportunity to research process flow and the application of computational tools in solvent extraction processes. A deeper understanding of process and equipment behavior coupled with real time data collection can indicate whether a process failure is accidental or purposeful. The goal of this project is to develop a system that utilizes non-traditional measurement sources such as vibration, acoustics, current, light, flow, and temperature in conjunction with data-based, machine learning techniques that will allow for signal discovery. This multi-sensor data can support the development of safeguards by design and security by design measures for such a facility. Additionally, it can aid in early detection and identification of removed materials indicating diversion, which is essential for initiating material recovery and actor identification. This overview encompasses the current research and testing of sensors to develop a spectrum of process signatures. To be followed by planned experiments aimed to characterize said signatures and study potential feature extraction techniques to identify a fault in the system (i.e. flow diversion).
SUMMARY The injection of CO2 at the Decatur carbon capture and storage site has generated significant microseismic activity, which occurs in distinct spatial clusters up to approximately 2.2 km from the primary injection well. Accurate and precise event locations are vital for the characterization of the microseismicity to help understand the reservoir response to the CO2 injection, whilst enabling the identification of minor faults and fractures below the resolution of conventional active seismic imaging. However, microseismic monitoring of fluid injection sites, such as Decatur, is often performed using a network of borehole sensors often from a single well. While these downhole sensors have excellent detection capabilities, their poor azimuthal coverage limits the ability to precisely determine event locations. We have developed a modified double-difference relocation algorithm suitable for both 1-D and 3-D velocity models, and which incorporates differential back azimuth observations to allow the benefits of the original double-difference algorithm to be applicable to a downhole microseismic monitoring setting. Applying the modified double-difference algorithm to the microseismicity at Decatur, we have successfully relocated 4293 events. The relocation included over 59 million observations for 757 285 event pairs, split across seven geographic regions. Despite the majority of observations being recorded in only two boreholes, with an almost identical azimuthal coverage, the results have shown to be reliable with significantly reduced residuals and low uncertainties associated with the final locations. We have analysed the residuals in terms of their association with each geographic region, data type, station and individual events, to fully appreciate their influence in the inversion and the fit of the data to the final set of event locations. For each region, the relocated seismicity has become less diffuse with improved clustering, and with newly visible linear features often orientated in a NE–SW direction. These results show the potential improvements that can be made to microseismic event locations recorded by a borehole network with a limited and variable azimuthal distribution.
The Wide-Field Infrared Explorer (WIRE), currently scheduled for launch in September 1998, is the fifth of five spacecraft in the NASA/Goddard Small Explorer (SMEX) series. This paper presents the design of WIRE's Attitude Control System flight software (ACS FSW). WIRE is a momentum-biased, three-axis stabilized stellar pointer which provides high-accuracy pointing and autonomous acquisition for eight to ten stellar targets per orbit. WIRE's short mission life and limited cryogen supply motivate requirements for Sun and Earth avoidance constraints which are designed to prevent catastrophic instrument damage and to minimize the heat load on the cryostat. The FSW implements autonomous fault detection and handling (FDH) to enforce these instrument constraints and to perform several other checks which insure the safety of the spacecraft. The ACS FSW implements modules for sensor data processing, attitude determination, attitude control, guide star acquisition, actuator command generation, command/telemetry processing, and FDH. These software components are integrated with a hierarchical control mode managing module that dictates which software components are currently active. The lowest mode in the hierarchy is the 'safest' one, in the sense that it utilizes a minimal complement of sensors and actuators to keep the spacecraft in a stable configuration (power and pointing constraints are maintained). As higher modes in the hierarchy are achieved, the various software functions are activated by the mode manager, and an increasing level of attitude control accuracy is provided. If FDH detects a constraint violation or other anomaly, it triggers a safing transition to a lower control mode. The WIRE ACS FSW satisfies all target acquisition and pointing accuracy requirements, enforces all pointing constraints, provides the ground with a simple means for reconfiguring the system via table load, and meets all the demands of its real-time embedded environment (16 MHz Intel 80386 processor with 80387 coprocessor running under the VRTX operating system). The mode manager organizes and controls all the software modules used to accomplish these goals, and in particular, the FDH module is tightly coupled with the mode manager.
Large composite material structures such as aircraft and Reusable Launch Vehicles (RLVS) operate in severe environments comprised of vehicle dynamic loads, aerodynamic loads, engine vibration, foreign object impact, lightning strikes, corrosion, and moisture absorption. These structures are susceptible to damage such as delamination, fiber breaking/pullout, matrix cracking, and hygrothermal strain. To ensure human safety and load-bearing integrity, these structures must be inspected to detect and locate often invisible damage and faults before becoming catastrophic. Moreover, nearly all future structures will need some type of in-service inspection technique to increase their useful life and reduce maintenance and overall costs. Possible techniques for monitoring the health and indicating damage on composite structures include: c-scan, thermography, acoustic emissions using piezoceramic actuators or fiber-optic wires with gratings, laser ultrasound, shearography, holography, x-ray, and others. These techniques have limitations in detecting damage that is beneath the surface of the structure, far away from a sensor location, or during operation of the vehicle. The objective of this project is to develop a more global method for damage detection that is based on structural dynamics principles, and can inspect for damage when the structure is subjected to vibratory loads to expose faults that may not be evident by static inspection. A Transmittance Function Monitoring (TFM) method is being developed in this project for ground-based inspection and operational health monitoring of large composite structures as a RLV. A comparison of the features of existing health monitoring approaches and the proposed TFM method is given.
Advanced high-temperature fluid reactors (ARs), such as sodium fast reactors (SFRs) and molten salt cooled reactors (MSCRs) utilize high-temperature fluids at ambient pressure. To melt the fluid during reactor startup and prevent fluid freezing during cooldown, the thermal–hydraulic systems of such ARs include heater zones consisting of specific heaters with controllers, temperature sensors, and thermal insulation. The failure of heater zones due to insulation material degradation or improper installation, resulting in parasitic heat losses, can lead to fluid freezing. The detection of faults using a heat-transfer model is difficult because of a lack of knowledge of the experimental details. Data-driven machine learning of heater zone temperature time series offers a viable alternative. In this study, we benchmarked the performance of recurrent neural networks (RNNs) in an analysis of heat-up transient temperature time series of heater zones installed on a liquid sodium vessel. The RNN models include long short-term memory (LSTM) and gated recurrent unit (GRU) networks, as well as their bi-directional variants, BiLSTM and BiGRU. Anomalous temperature points were designated using a percentile-based threshold applied to residual fluctuations in the detrended temperature time series. Additionally, the impact of the exponentially weighted moving average (EWMA) method on detection accuracy was examined. The RNN models’ performance was assessed using precision, recall, and F 1 score metrics. Results demonstrated that RNN models effectively detect anomalies in temperature time series with the best models for each heater zone achieving F 1 scores of over 93%. To explain the variations in RNN model performance across different heater zones, we used Kullback–Leibler (KL) divergence to quantify the relative entropy between training and testing data, and the Detrended Fluctuation Analysis (DFA) to assess long-range temporal correlations. For datasets with strong long-range correlations and minimal relative entropy between training and testing data, GRU is the best-performing model. When the data exhibits weaker long-term correlations and a significant relative entropy between training and testing distributions, BiGRU shows the best performance. For the data sets with intermediate values of both KL divergence and DFA, the best performance is obtained with LSTM and BiLSTM, respectively.
The digital control systems for the Under-the-Wing (UTW) and Over-the-Wing (OTW) engines developed on the NASA/General Electric Quiet Clean Shorthaul Experimental Engine (QCSEE) program are described. The system to control engine variables includes three major functional parts: system sensors, digital control and system actuators. One of the primary control system functions is to prevent the engine from exceeding speed or temperature limits. The UTW control system also provides fault detection and condition monitoring. The control system requirements for the OTW engine are essentially the same as the UTW engine, however the inlet Mach number control requirement is eliminated, and failure indication and corrective action and full authority digital control are added. The digital controls scheduled the engine variables and maintained engine operation within all physical limits throughout the test program of approximately 200 hours of operation and provided stable and accurate control of both engines.