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NASA NTRS2006

Fault Management Techniques in Human Spaceflight Operations

This paper discusses human spaceflight fault management operations. Fault detection and response capabilities available in current US human spaceflight programs Space Shuttle and International Space Station are described while emphasizing system design impacts on operational techniques and constraints. Preflight and inflight processes along with products used to anticipate, mitigate and respond to failures are introduced. Examples of operational products used to support failure responses are presented. Possible improvements in the state of the art, as well as prioritization and success criteria for their implementation are proposed. This paper describes how the architecture of a command and control system impacts operations in areas such as the required fault response times, automated vs. manual fault responses, use of workarounds, etc. The architecture includes the use of redundancy at the system and software function level, software capabilities, use of intelligent or autonomous systems, number and severity of software defects, etc. This in turn drives which Caution and Warning (C&W) events should be annunciated, C&W event classification, operator display designs, crew training, flight control team training, and procedure development. Other factors impacting operations are the complexity of a system, skills needed to understand and operate a system, and the use of commonality vs. optimized solutions for software and responses. Fault detection, annunciation, safing responses, and recovery capabilities are explored using real examples to uncover underlying philosophies and constraints. These factors directly impact operations in that the crew and flight control team need to understand what happened, why it happened, what the system is doing, and what, if any, corrective actions they need to perform. If a fault results in multiple C&W events, or if several faults occur simultaneously, the root cause(s) of the fault(s), as well as their vehicle-wide impacts, must be determined in order to maintain situational awareness. This allows both automated and manual recovery operations to focus on the real cause of the fault(s). An appropriate balance must be struck between correcting the root cause failure and addressing the impacts of that fault on other vehicle components. Lastly, this paper presents a strategy for using lessons learned to improve the software, displays, and procedures in addition to determining what is a candidate for automation. Enabling technologies and techniques are identified to promote system evolution from one that requires manual fault responses to one that uses automation and autonomy where they are most effective. These considerations include the value in correcting software defects in a timely manner, automation of repetitive tasks, making time critical responses autonomous, etc. The paper recommends the appropriate use of intelligent systems to determine the root causes of faults and correctly identify separate unrelated faults.

O'Hagan, Brian↗

NASA NTRS2014

Identifiability of Additive Actuator and Sensor Faults by State Augmentation

A class of fault detection and identification (FDI) methods for bias-type actuator and sensor faults is explored in detail from the point of view of fault identifiability. The methods use state augmentation along with banks of Kalman-Bucy filters for fault detection, fault pattern determination, and fault value estimation. A complete characterization of conditions for identifiability of bias-type actuator faults, sensor faults, and simultaneous actuator and sensor faults is presented. It is shown that FDI of simultaneous actuator and sensor faults is not possible using these methods when all sensors have unknown biases. The fault identifiability conditions are demonstrated via numerical examples. The analytical and numerical results indicate that caution must be exercised to ensure fault identifiability for different fault patterns when using such methods.

Joshi, Suresh↗

NASA NTRS2014

Identifiability of Additive, Time-Varying Actuator and Sensor Faults by State Augmentation

Recent work has provided a set of necessary and sucient conditions for identifiability of additive step faults (e.g., lock-in-place actuator faults, constant bias in the sensors) using state augmentation. This paper extends these results to an important class of faults which may affect linear, time-invariant systems. In particular, the faults under consideration are those which vary with time and affect the system dynamics additively. Such faults may manifest themselves in aircraft as, for example, control surface oscillations, control surface runaway, and sensor drift. The set of necessary and sucient conditions presented in this paper are general, and apply when a class of time-varying faults affects arbitrary combinations of actuators and sensors. The results in the main theorems are illustrated by two case studies, which provide some insight into how the conditions may be used to check the theoretical identifiability of fault configurations of interest for a given system. It is shown that while state augmentation can be used to identify certain fault configurations, other fault configurations are theoretically impossible to identify using state augmentation, giving practitioners valuable insight into such situations. That is, the limitations of state augmentation for a given system and configuration of faults are made explicit. Another limitation of model-based methods is that there can be large numbers of fault configurations, thus making identification of all possible configurations impractical. However, the theoretical identifiability of known, credible fault configurations can be tested using the theorems presented in this paper, which can then assist the efforts of fault identification practitioners.

Upchurch, Jason M.↗

DOE OSTI2025

Coupled geomechanical investigation of depletion-induced fault reactivation

Fault reactivation during subsurface fluid production pose significant challenges to safe and sustainable resource extraction. Here, this study presents a three-dimensional coupled geomechanical framework to investigate the processes driving fault reactivation, capturing the interactions between reservoir dynamics and geomechanical responses. Verification against theoretical estimations based on linear poroelasticity confirms the model's capacity in representing reservoir background stress responses. However, the study reveals that relying solely on background stress states can underestimate or overestimate fault reactivation potential, emphasizing the importance of including localized stress perturbations such as differential compaction and stress redistribution. Applied to a fault (M1) inspired by the geological characteristics of the Groningen field, the model shows slip initiation at 2965 m depth with 16.0 MPa depletion, aligning with field observations where seismicity occurred at approximately 3 km depth after 15.8 MPa depletion. Parametric studies reveal: (1) inelastic reservoir compaction delays fault reactivation and mitigates fault slip by reducing stress concentration, (2) higher intermediate in-situ stress magnitudes decrease the Coulomb Failure Stress (CFS) increase rate and reduce fault slip, (3) larger fault offsets amplify shear stress near the offset zone, promoting earlier reactivation and longer rupture propagation, and (4) fault permeability significantly influences pressure diffusion, with low-permeability faults leading to sharper stress changes and earlier fault destabilization. These insights highlight the critical role of geological and mechanical parameters in fault reactivation and provide a predictive framework for mitigating induced seismicity risks.

Coupled geomechanical modeling↗

DOE OSTI2023

Sensitivity analysis of an automated fault detection algorithm for residential air-conditioning systems

The state of the art of fault detection and diagnosis (FDD) for residential air-conditioning systems is expensive and not yet amenable to widespread implementation. FDD for homes can significantly reduce utility costs, and increase the lifespan of the equipment. The cost barriers currently, however, make FDD for homes economically unviable for large scale implementation. In prior work, we offered a solution to reduce FDD costs by proposing an automated fault detection algorithm to serve as a screening step before more expensive FDD tests can be conducted. The algorithm uses only the home thermostat and local weather information to identify thermodynamic parameters and detect high-impact air-conditioning faults, including those that occur during equipment installation. We had tested the algorithm on a single EnergyPlus™ model of a home in Orlando, Florida. The thermodynamic parameter identification process is highly nonconvex involving several local optimal solutions. In this paper we propose a novel method to select the best model for fault detection from among the list of local optimal solutions to make the algorithm more robust to homes of different construction, without which the fault detection process would be infeasible. Another unique contribution of the paper is implementing the solution on real-world data. We also bring the algorithm closer to market by testing it on real-world data. We implement the algorithm on data obtained from experiments conducted by the Florida Solar Energy Center (FSEC) on a laboratory home equipped with a heat pump where faults were intentionally added for a period of seven months. The algorithm successfully detected an undercharge fault with 70.6% accuracy, concurrent duct leakage and undercharge faults with 85.2% accuracy, and duct leakage faults with 69.1% accuracy. A sensitivity analysis is also performed on EnergyPlus models of nine types of homes that vary in construction to demonstrate the robustness of the algorithm. Finally, the algorithm achieves an average accuracy of 71% for no-fault condition, 77% for 40% undercharge fault, and 76% for duct-leak fault.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Complex Fault Geometry of the 2020 M ww 6.5 Monte Cristo Range, Nevada, Earthquake Sequence

On 15 May 2020 an M ww 6.5 earthquake occurred beneath the Monte Cristo Range in the Mina Deflection region of western Nevada. Rapid deployment of eight temporary seismic stations enabled detailed analysis of its productive and slowly decaying aftershock sequence (p=0.8), which included ~18,000 autodetected events in 3.5 months. Double-difference, waveform-based relative relocation of 16,714 earthquakes reveals a complex network of faults, many of which cross the inferred 35-km-long east–northeast-striking, left-lateral mainshock rupture. Seismicity aligns with left-lateral, right-lateral, and normal mechanism moment tensors of 128 of the largest earthquakes. The mainshock occurred near the middle of the aftershock zone at the intersection of two distinct zones of seismicity. In the western section, numerous subparallel, shallow, north-northeast-striking faults form a broad flower-structure-like fault mesh that coalesces at depth into a near-vertical, left-lateral fault. We infer the near-vertical fault to be a region of significant slip in the mainshock and an eastward extension of the left-lateral Candelaria fault. Near the mainshock hypocenter, seismicity occurs on a northeast-striking, west-dipping structure that extends north from the eastern Columbus Salt Marsh normal fault. Together, these two intersecting structures bound the Columbus Salt Marsh tectonic basin. East of this intersection and the mainshock hypocenter, seismicity occurs in a narrow, near-vertical, east-northeast-striking fault zone through to its eastern terminus. At the eastern end, the aftershock zone broadens and extends northwest toward the southern extension of the northwest-striking, right-lateral Petrified Springs fault system. The eastern section hosts significantly fewer aftershocks than the western section, but has more moment release. We infer that shallow aftershocks throughout the system highlight fault-fracture meshes that connect mapped fault systems at depth. Here, comparing earthquake data with surface ruptures and a simple geodetic fault model sheds light on the complexity of this recent M 6.5 Walker Lane earthquake.

58 GEOSCIENCES↗

DOE OSTI2022

Real-Time Cavity Fault Prediction in CEBAF Using Deep Learning

Data-driven prediction of future faults is a major research area for many industrial applications. In this work, we present a new procedure of real-time fault prediction for superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF) using deep learning. CEBAF has been afflicted by frequent downtime caused by SRF cavity faults. We perform fault prediction using pre-fault RF signals from C100-type cryomodules. Using the pre-fault signal information, the new algorithm predicts the type of cavity fault before the actual onset. The early prediction may enable potential mitigation strategies to prevent the fault. In our work, we apply a two-stage fault prediction pipeline. In the first stage, a model distinguishes between faulty and normal signals using a U-Net deep learning architecture. In the second stage of the network, signals flagged as faulty by the first model are classified into one of seven fault types based on learned signatures in the data. Initial results show that our model can successfully predict most fault types 200 ms before onset. We will discuss reasons for poor model performance on specific fault types.

Rahman, M.↗

NASA NTRS1975

Faults on Skylab imagery of the Salton Trough area, Southern California

The author has identified the following significant results. Large segments of the major high angle faults in the Salton Trough area are readily identifiable in Skylab images. Along active faults, distinctive topographic features such as scarps and offset drainage, and vegetation differences due to ground water blockage in alluvium are visible. Other fault-controlled features along inactive as well as active faults visible in Skylab photography include straight mountain fronts, linear valleys, and lithologic differences producing contrasting tone, color or texture. A northwestern extension of a fault in the San Andreas set, is postulated by the regional alignment of possible fault-controlled features. The suspected fault is covered by Holocene deposits, principally windblown sand. A northwest trending tonal change in cultivated fields across Mexicali Valley is visible on Skylab photos. Surface evidence for faulting was not observed; however, the linear may be caused by differences in soil conditions along an extension of a segment of the San Jacinto fault zone. No evidence of faulting could be found along linears which appear as possible extensions of the Substation and Victory Pass faults, demonstrating that the interpretation of linears as faults in small scale photography must be corroborated by field investigations.

Merifield, P. M.↗

NASA NTRS1986

Measurement and application of fault latency

The time interval between the occurrence of a fault and the detection of the error caused by the fault is divided by the generation of that error into two parts: fault latency and error latency. Since the moment of error generation is not directly observable, all related works in the literature have dealt with only the sum of fault and error latencies, thereby making the analysis of their separate effects impossible. To remedy this deficiency, (1) a new methodology for indirectly measuring fault latency is presented; the distribution of fault latency is derived from the methodology; and (3) the knowledge of fault latency is applied to the analysis of two important examples. The proposed methodology has been implemented for measuring fault latency in the Fault-Tolerant Multiprocessor (FTMP) at the NASA Airlab. The experimental results show wide variations in the mean fault latencies of different function circuits within FTMP. Also, the measured distributions of fault latency are shown to have monotone hazard rates. Consequently, Gamma and Weibull distributions are selected for the least-squares fit as the distribution of fault latency.

Shin, K. G.↗

NASA NTRS1990

Fault diagnosis

The objective of the research in this area of fault management is to develop and implement a decision aiding concept for diagnosing faults, especially faults which are difficult for pilots to identify, and to develop methods for presenting the diagnosis information to the flight crew in a timely and comprehensible manner. The requirements for the diagnosis concept were identified by interviewing pilots, analyzing actual incident and accident cases, and examining psychology literature on how humans perform diagnosis. The diagnosis decision aiding concept developed based on those requirements takes abnormal sensor readings as input, as identified by a fault monitor. Based on these abnormal sensor readings, the diagnosis concept identifies the cause or source of the fault and all components affected by the fault. This concept was implemented for diagnosis of aircraft propulsion and hydraulic subsystems in a computer program called Draphys (Diagnostic Reasoning About Physical Systems). Draphys is unique in two important ways. First, it uses models of both functional and physical relationships in the subsystems. Using both models enables the diagnostic reasoning to identify the fault propagation as the faulted system continues to operate, and to diagnose physical damage. Draphys also reasons about behavior of the faulted system over time, to eliminate possibilities as more information becomes available, and to update the system status as more components are affected by the fault. The crew interface research is examining display issues associated with presenting diagnosis information to the flight crew. One study examined issues for presenting system status information. One lesson learned from that study was that pilots found fault situations to be more complex if they involved multiple subsystems. Another was pilots could identify the faulted systems more quickly if the system status was presented in pictorial or text format. Another study is currently under way to examine pilot mental models of the aircraft subsystems and their use in diagnosis tasks. Future research plans include piloted simulation evaluation of the diagnosis decision aiding concepts and crew interface issues. Information is given in viewgraph form.

Abbott, Kathy↗

NASA NTRS1992

Advanced information processing system: Fault injection study and results

The objective of the AIPS program is to achieve a validated fault tolerant distributed computer system. The goals of the AIPS fault injection study were: (1) to present the fault injection study components addressing the AIPS validation objective; (2) to obtain feedback for fault removal from the design implementation; (3) to obtain statistical data regarding fault detection, isolation, and reconfiguration responses; and (4) to obtain data regarding the effects of faults on system performance. The parameters are described that must be varied to create a comprehensive set of fault injection tests, the subset of test cases selected, the test case measurements, and the test case execution. Both pin level hardware faults using a hardware fault injector and software injected memory mutations were used to test the system. An overview is provided of the hardware fault injector and the associated software used to carry out the experiments. Detailed specifications are given of fault and test results for the I/O Network and the AIPS Fault Tolerant Processor, respectively. The results are summarized and conclusions are given.

Burkhardt, Laura F.↗

NASA NTRS1994

The Teton fault, Wyoming: Topographic signature, neotectonics, and mechanisms of deformation

We integrated geophysical and geological methods to evalute the structural evolution of the active Teton normal fault, Wyoming, and its role in the development of the dramatic topography of Teton Range and Jackson Hole. Comparison of variations in surface offsets with the topographic expression of the Teton range crest and drainage divide, and the overall structure of the range, suggests that the effects ofpostglacial faulting cannot be discriminated from the influence of pre-extensional structures and differential; erosion on the footwall topography. In contrast, the effects of multiple scarp-forming normal faulting earthquakes are expressed by the anomalous drainage pattern and westward tilt of the hanging wall, Jackson Hole, toward the Teton fault. Kinematic boundary element fault models suggest that the westward tilt of the valley floor is the product of 110-125 m of displacement on a 45 deg-75 deg E dipping Teton fault in the past 25,000-75,000 years. Comparisons with historic normal faulting earthquake displacements imply that this range of displacement corresponds to 10-50, M greater than 7 scarp-forming earthquakes. A total throw of 2.5 to 3.5 km across the Teton fault is suggested by inverse ray-tracing and forward gravity models. These models also suggest that Laramide age structures have been offset across the Teton fault and obscure its geophysical signature but also continue to influence the structural and topographic expression of the footwall and hanging wall blocks. Paleomagnetic analyses of the approximately 2.0 Ma Huckelberry Ridge Tuff suggest that the overall westward tilt of the Teton Range is a result ofabout 10 deg of west side down tilt across the Teton fault since tuff emplacement. This suggests that much if not all of the throw across the Teton fault has accumulated in the past 2 m.y. Complex demagnetization and rock magnetic behavior and local emplacement of the Huckleberry Ridge Tuff on preexisting topogrpahy preclude determination of the amount or variations in throw along strike of the Teton fault from the paleomagnetic data.

Byrd, John O. D.↗

NASA NTRS2005

Geometry of Thrust Faults Beneath Amenthes Rupes, Mars

Amenthes Rupes is a 380 km-long lobate fault scarp located in the eastern hemisphere of Mars near the dichotomy boundary. The scarp is marked by about 1 km of vertical separation across a northeast dipping thrust fault (top to the SW) and offsets heavily-cratered terrain of Late Noachian age, the visible portion of which was in place by 3.92 Ga and the buried portion in place between 4.08 and 4.27 Ga. The timing of scarp formation is difficult to closely constrain. Previous geologic mapping shows that near the northern end of Amenthes Rupes, Hesperian age basalts terminate at the scarp, suggesting that fault slip predated the emplacement of these flows at 3.69 to 3.9 Ga. Maxwell and McGill also suggest the faulting ceased before the final emplacement of the Late Hesperian lavas on Isidis Planitia. The trend of the faults at Amenthes, like many thrust faults at the dichotomy boundary, parallels the boundary itself. Schultz and Watters used a dislocation modeling program to match surface topography and vertical offset of the scarp at Amenthes Rupes, varying the dip and depth of faulting, assuming a slip of 1.5 km on the fault. They modeled faulting below Amenthes Rupes as having a dip of between 25 and 30 degrees and a depth of 25 to 35 km, based on the best match to topography. Assuming a 25 degree dip and surface measurements of vertical offset of between 0.3 and 1.2 km, Watters later estimated the maximum displacement on the Amenthes Rupes fault to be 2.90 km. However, these studies did not determine the geometry of the thrust using quantitative constraints that included shortening estimates. Amenthes Rupes deforms large preexisting impact craters. We use these craters to constrain shortening across the scarp and combine this with vertical separation to infer fault geometry. Fault dip was also estimated using measurements of scarp morphology. Measurements were based on 460 m (1/128 per pixel) digital elevation data from the Mars Orbiter Laser Altimeter (MOLA), an instrument on the Mars Global Surveyor (MGS) satellite.

Vidal, A.↗

NASA NTRS2011

Onboard Nonlinear Engine Sensor and Component Fault Diagnosis and Isolation Scheme

A method detects and isolates in-flight sensor, actuator, and component faults for advanced propulsion systems. In sharp contrast to many conventional methods, which deal with either sensor fault or component fault, but not both, this method considers sensor fault, actuator fault, and component fault under one systemic and unified framework. The proposed solution consists of two main components: a bank of real-time, nonlinear adaptive fault diagnostic estimators for residual generation, and a residual evaluation module that includes adaptive thresholds and a Transferable Belief Model (TBM)-based residual evaluation scheme. By employing a nonlinear adaptive learning architecture, the developed approach is capable of directly dealing with nonlinear engine models and nonlinear faults without the need of linearization. Software modules have been developed and evaluated with the NASA C-MAPSS engine model. Several typical engine-fault modes, including a subset of sensor/actuator/components faults, were tested with a mild transient operation scenario. The simulation results demonstrated that the algorithm was able to successfully detect and isolate all simulated faults as long as the fault magnitudes were larger than the minimum detectable/isolable sizes, and no misdiagnosis occurred

Tang, Liang↗

DOE OSTI2020

An Overview of Spread Spectrum Time Domain Reflectometry Responses to Photovoltaic Faults

Spread spectrum time domain reflectometry (SSTDR) is a broadband electrical reflectometry technique that has been used to detect and locate faults on live electrical systems, including photovoltaic systems. In this article, we evaluate the detectability and localizability from both existing literature and our own measurements using SSTDR of open-circuit faults, connection faults, short-circuit faults, ground faults, arc faults, shading faults, bypass diode faults, and accelerated degradation faults in PV cells and mini-modules. Additionally, reflection magnitudes for these faults are compared. Herein, preliminary data on buried and grounded PV cable along with arc fault detection are presented.

14 SOLAR ENERGY↗

DOE OSTI2023

1-D Convolutional Graph Convolutional Networks for Fault Detection in Distributed Energy Systems

This paper presents a 1-D convolutional and graph convolutional networks for fault detection in microgrids. The combination of 1-D convolutional neural networks (1D-CNN) and graph convolutional networks (GCN) helps extract both spatial-temporal correlations from the voltage measurements in microgrids. The fault detection scheme includes fault event detection, fault type and phase classification, and fault location. There are five neural network model training to handle these tasks. Transfer learning and fine-tuning are applied to reduce training efforts. The combined 1-D convolutional and graph convolutional networks (1D-CGCN) is compared with the traditional ANN structure on the Potsdam 13-bus microgrid dataset. The accuracy of 99.5%, 98.4%, 99.2%, and 95.5% are achieved in fault event detection, fault type classification, fault phase identification, and fault location respectively. The detailed confusion matrices of fault type and fault phase classification are provided for validation.

deep neural network↗

NASA NTRS2012

Improving Multiple Fault Diagnosability using Possible Conflicts

Multiple fault diagnosis is a difficult problem for dynamic systems. Due to fault masking, compensation, and relative time of fault occurrence, multiple faults can manifest in many different ways as observable fault signature sequences. This decreases diagnosability of multiple faults, and therefore leads to a loss in effectiveness of the fault isolation step. We develop a qualitative, event-based, multiple fault isolation framework, and derive several notions of multiple fault diagnosability. We show that using Possible Conflicts, a model decomposition technique that decouples faults from residuals, we can significantly improve the diagnosability of multiple faults compared to an approach using a single global model. We demonstrate these concepts and provide results using a multi-tank system as a case study.

multiple faults↗

DOE OSTI2020

Automated fault detection of residential air-conditioning systems using thermostat drive cycles

Residential air conditioning equipment comprises a significant portion of the total energy consumption of a home. Unfortunately, air-conditioning systems can be susceptible to faulty operation either from installation errors or faults that accrue over the equipment’s lifetime. This paper presents a novel automated fault detection algorithm for residential air-conditioning systems that can alert the homeowner of the presence of these faults. The proposed algorithm utilizes only the home’s thermostat and outside air temperature to perform automated fault detection over the course of the equipment’s lifetime, including immediately after installation. The algorithm uses an extended Kalman filter approach to identify a three-resistor, two-capacitor (3R2C) electrical equivalent thermodynamic model. The identified 3R2C model is used to predict cooling times during a testing period comprising of a series of thermostat drive-cycle experiments. We tested the algorithm on an EnergyPlus™ model of a typical residential building in Orlando, Florida. Duct faults, indoor airflow faults, and refrigerant undercharge faults were introduced into the building model one at a time. The algorithm was able to accurately determine duct-leak faults, 40% airflow faults, 40% undercharge faults, and no-fault cases with an accuracy of 70%, 77%, 82%, and 87%, respectively.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗