Results for “faults” — Page 45

NASA NTRS2009

A Model-Based Architecture for a Small Flexible Fault Protection System

In this paper we will give a brief overview of how different missions implemented the fault protection application and the improvements along the way. We will then propose an architecture that supports the direct implementation of state-chart models into flight code. These state-chart models can be used to formally and naturally specify the behavior of all the major fault protection components - monitors, fault protection engine, and fault responses. The goal is a flexible, light-weight implementation of a traditional fault protection software system that is inexpensive to implement, reliable and understandable for both system and software developers.

Watney, Garth↗

NASA NTRS2015

Sensor Selection for Aircraft Engine Performance Estimation and Gas Path Fault Diagnostics

This paper presents analytical techniques for aiding system designers in making aircraft engine health management sensor selection decisions. The presented techniques, which are based on linear estimation and probability theory, are tailored for gas turbine engine performance estimation and gas path fault diagnostics applications. They enable quantification of the performance estimation and diagnostic accuracy offered by different candidate sensor suites. For performance estimation, sensor selection metrics are presented for two types of estimators including a Kalman filter and a maximum a posteriori estimator. For each type of performance estimator, sensor selection is based on minimizing the theoretical sum of squared estimation errors in health parameters representing performance deterioration in the major rotating modules of the engine. For gas path fault diagnostics, the sensor selection metric is set up to maximize correct classification rate for a diagnostic strategy that performs fault classification by identifying the fault type that most closely matches the observed measurement signature in a weighted least squares sense. Results from the application of the sensor selection metrics to a linear engine model are presented and discussed. Given a baseline sensor suite and a candidate list of optional sensors, an exhaustive search is performed to determine the optimal sensor suites for performance estimation and fault diagnostics. For any given sensor suite, Monte Carlo simulation results are found to exhibit good agreement with theoretical predictions of estimation and diagnostic accuracies.

Gas Turbine Engines↗

NASA NTRS2015

Sensor Selection for Aircraft Engine Performance Estimation and Gas Path Fault Diagnostics

This paper presents analytical techniques for aiding system designers in making aircraft engine health management sensor selection decisions. The presented techniques, which are based on linear estimation and probability theory, are tailored for gas turbine engine performance estimation and gas path fault diagnostics applications. They enable quantification of the performance estimation and diagnostic accuracy offered by different candidate sensor suites. For performance estimation, sensor selection metrics are presented for two types of estimators including a Kalman filter and a maximum a posteriori estimator. For each type of performance estimator, sensor selection is based on minimizing the theoretical sum of squared estimation errors in health parameters representing performance deterioration in the major rotating modules of the engine. For gas path fault diagnostics, the sensor selection metric is set up to maximize correct classification rate for a diagnostic strategy that performs fault classification by identifying the fault type that most closely matches the observed measurement signature in a weighted least squares sense. Results from the application of the sensor selection metrics to a linear engine model are presented and discussed. Given a baseline sensor suite and a candidate list of optional sensors, an exhaustive search is performed to determine the optimal sensor suites for performance estimation and fault diagnostics. For any given sensor suite, Monte Carlo simulation results are found to exhibit good agreement with theoretical predictions of estimation and diagnostic accuracies.

Gas Turbine Engines↗

NASA NTRS2016

Sensor Selection for Aircraft Engine Performance Estimation and Gas Path Fault Diagnostics

This paper presents analytical techniques for aiding system designers in making aircraft engine health management sensor selection decisions. The presented techniques, which are based on linear estimation and probability theory, are tailored for gas turbine engine performance estimation and gas path fault diagnostics applications. They enable quantification of the performance estimation and diagnostic accuracy offered by different candidate sensor suites. For performance estimation, sensor selection metrics are presented for two types of estimators including a Kalman filter and a maximum a posteriori estimator. For each type of performance estimator, sensor selection is based on minimizing the theoretical sum of squared estimation errors in health parameters representing performance deterioration in the major rotating modules of the engine. For gas path fault diagnostics, the sensor selection metric is set up to maximize correct classification rate for a diagnostic strategy that performs fault classification by identifying the fault type that most closely matches the observed measurement signature in a weighted least squares sense. Results from the application of the sensor selection metrics to a linear engine model are presented and discussed. Given a baseline sensor suite and a candidate list of optional sensors, an exhaustive search is performed to determine the optimal sensor suites for performance estimation and fault diagnostics. For any given sensor suite, Monte Carlo simulation results are found to exhibit good agreement with theoretical predictions of estimation and diagnostic accuracies.

Diagnosis↗

NASA NTRS2017

Achieving Agreement in Three Rounds with Bounded-Byzantine Faults

A three-round algorithm is presented that guarantees agreement in a system of K greater than or equal to 3F+1 nodes provided each faulty node induces no more than F faults and each good node experiences no more than F faults, where, F is the maximum number of simultaneous faults in the network. The algorithm is based on the Oral Message algorithm of Lamport, Shostak, and Pease and is scalable with respect to the number of nodes in the system and applies equally to traditional node-fault model as well as the link-fault model. We also present a mechanical verification of the algorithm focusing on verifying the correctness of a bounded model of the algorithm as well as confirming claims of determinism.

Malekpour, Mahyar, R.↗

NASA NTRS2018

ASTERIA Operations Demonstrates the Value of Combining the Mission Assurance and Fault Protection Roles on CubeSats

On November 20, 2017, ASTERIA (Arcsecond Space Telescope Enabling Research in Astrophysics), a 6U CubeSat performing a technology demonstration of astrophysical measurements, deployed from the ISS. The technology demonstration goals to achieve precision photometry via arcsecond-level line-of-sight pointing error and highly stable focal plane temperature control were met by February 2018. Extended mission operations are ongoing, with the primary focus on observing nearby stars for transiting exoplanets. Throughout development and operations, the roles of mission assurance and fault protection have proven critical to achieving the primary technical goals and to maintaining a healthy spacecraft through multiple extended missions. Given the budget and schedule constraints typical of a CubeSat, innovative tailoring of processes has been critical to success throughout both development and operations of ASTERIA. Mission assurance plays an important role in identifying and evaluating risk and developing cost-effective mitigations. Flexibility in the fault protection design offers a variety of options for implementing risk mitigations as risks have been uncovered both in pre-delivery testing and in mission operations. This paper will discuss the approach taken on ASTERIA to implement mission assurance and fault protection and the resulting benefits to operational efficiency and success. It will briefly address the advantages of this approach during development, in which the combination of the roles provided mission assurance significant insight to system risks, which feeds back into testing methodologies and directly into fault protection design. Operations will be discussed in detail. During this phase, the roles merge to identify in-flight fault protection updates to efficiently respond to anomalies and improve the likelihood of successful technology demonstrations. The paper will also detail the tools that are used to analyse data, identify anomalies, and develop the updates to uplink to the spacecraft. Finally, the general operational approach will be discussed to highlight the usefulness of the ASTERIA processes and their applicability to future CubeSat missions.

Knapp, Mary↗

NASA NTRSDate not supplied

Design Optimization Study of Fault Tolerant and Redundant Motor Drivetrains for Urban Air Mobility Vehicles

Fully electric and hybrid electric aircraft will require extremely lightweight and reliable electric motor drivetrains to meet performance and safety goals. It is likely that to meet reliability targets some form of fault tolerance and/or redundancy will need to be used in the electric motor drivetrain. The use of either redundancy or fault tolerance will result in a reduction in drivetrain performance. In this paper, an example design study is carried out comparing redundant and fault tolerant drivetrains based on an example fault tolerant motor topology for a tilt rotor UAM application. Results show a minimal weight penalty for the incorporation of fault tolerance into drivetrains with the example motor explored here.

Thomas Francis Tallerico↗

NASA NTRSDate not supplied

Design Optimization Study of Fault Tolerant and Redundant Motor Drivetrains for Urban Air Mobility Vehicles

Fully electric and hybrid electric aircraft will require extremely lightweight and reliable electric motor drivetrains to meet performance and safety goals. It is likely that to meet reliability targets some form of fault tolerance and/or redundancy will need to be used in the electric motor drivetrain. The use of either redundancy or fault tolerance will result in a reduction in drivetrain performance. In this paper, an example design study is carried out comparing redundant and fault tolerant drivetrains based on an example fault tolerant motor topology for a tilt rotor UAM application. Results show a minimal weight penalty for the incorporation of fault tolerance into drivetrains with the example motor explored here.

Thomas Tallerico↗

DOE OSTI2016

Marine and land active-source seismic imaging of mid-Miocene to Holocene-aged faulting near geothermal prospects at Pyramid Lake, Nevada

Amy Eisses, Annie Kell, Graham Kent, Neal Driscoll, Robert Karlin, Rob Baskin, John Louie, and Satish Pullammanappallil, 2011, Marine and land active-source seismic imaging of mid-Miocene to Holocene-aged faulting near geothermal prospects at Pyramid Lake, Nevada: Geothermal Resources Council Transactions, 35, 7 pp. Preprint at http://crack.seismo.unr.edu/geothermal/Eisses-GRCpaper-sm.pdf The Pyramid Lake fault zone lies within a vitally important area of the northern Walker Lane where not only can transtension can be studied through a complex arrangement of strike-slip and normal faults but also geothermal activity can be examined in the extensional regime for productivity. This study used advanced and economical seismic methods in attempt to develop the Paiute Tribe’s geothermal reservoir and to expand upon the tectonics and earthquake hazard knowledge of the area. 500 line-kilometers of marine CHIRP data were collected on Pyramid Lake combined with 27 kilometers of vibrator seismic on-land data from the northwest side of the basin were collected in 2010 that highlighted two distinct phases of faulting. Preliminary results suggest that the geothermal fluids in the area are controlled by the late Pleistoceneto Holocene-aged faults and not through the mid-Miocene-aged conduits as originally hypothesized.

15 GEOTHERMAL ENERGY↗

DOE OSTI2015

FITL: Extending LLVM for the Translation of Fault-Injection Directives

The frequency of hardware errors in HPC systems continues to grow as system designs evolve toward exascale. Tolerating these errors efficiently and effectively will require software-based resilience solutions. With this requirement in mind, recent research has increasingly employed LLVM-based tools to simulate transient hardware faults in order to study the resilience characteristics of specific applications. However, such tools require researchers to configure their experiments at the level of the LLVM intermediate representation (LLVM IR) rather than at the source level of the applications under study. In this paper, we present FITL (Fault-Injection Toolkit for LLVM), a set of LLVM extensions to which it is straightforward to translate source-level pragmas that specify fault injection. While we have designed FITL not to be tied to any particular compiler front end or high-level language, we also describe how we have extended our OpenARC compiler to translate a novel set of fault-injection pragmas for C to FITL. Finally, we present several resilience studies we have conducted using FITL, including a comparison with a source-level fault injector we have built as part of OpenARC.

Lee, Seyong↗

DOE OSTI2016

Fault detection and diagnosis for refrigerator from compressor sensor

A refrigerator, a sealed refrigerant system, and method are provided where the refrigerator includes at least a refrigerated compartment and a sealed refrigerant system including an evaporator, a compressor, a condenser, a controller, an evaporator fan, and a condenser fan. The method includes monitoring a frequency of the compressor, and identifying a fault condition in the at least one component of the refrigerant sealed system in response to the compressor frequency. The method may further comprise calculating a compressor frequency rate based upon the rate of change of the compressor frequency, wherein a fault in the condenser fan is identified if the compressor frequency rate is positive and exceeds a condenser fan fault threshold rate, and wherein a fault in the evaporator fan is identified if the compressor frequency rate is negative and exceeds an evaporator fan fault threshold rate.

42 ENGINEERING↗

DOE OSTI2019

Fault detection and diagnosis for refrigerator from compressor sensor

A refrigerator, a sealed refrigerant system, and method are provided where the refrigerator includes at least a refrigerated compartment and a sealed refrigerant system including an evaporator, a compressor, a condenser, a controller, an evaporator fan, and a condenser fan. The method includes monitoring a frequency of the compressor, and identifying a fault condition in the at least one component of the refrigerant sealed system in response to the compressor frequency. The method may further comprise calculating a compressor frequency rate based upon the rate of change of the compressor frequency, wherein a fault in the condenser fan is identified if the compressor frequency rate is positive and exceeds a condenser fan fault threshold rate, and wherein a fault in the evaporator fan is identified if the compressor frequency rate is negative and exceeds an evaporator fan fault threshold rate.

42 ENGINEERING↗

DOE OSTI2020

Fault-tolerant controller for modular multi-level converters

Various examples are provided related to fault-tolerant controller architectures for multi-level converters. In one example, a multi-level converter includes an array of power modules. The power modules can include a controller communicatively coupled to controllers of adjacent power modules in the array of power modules. Circuitry of the controllers can receive operational data from the adjacent power modules; identify a fault condition in an adjacent power module using the operational data; and initiate reconfiguration of the array of power modules in response to an indication of the fault condition, where the reconfiguration bypasses the adjacent power module. In another example, a method includes identifying a fault condition in an adjacent power module in an array of power modules based upon operational data from one or more adjacent power modules of the array; and initiating reconfiguration of the array of power modules in response to an indication of the fault condition.

42 ENGINEERING↗

DOE OSTI2017

Real-time fault detection and transient identification using extended Kalman filters - 254

In this paper, real-time fault detection and transient identification are studied with adaptive estimation methods, in particular parallel processing methods by implementing a bank of extended Kalman filters. Adaptive estimation is combined with probabilistic risk assessment to allow for real-time continuous monitoring of initiating events. The approach provides a probability of an initiating event happening. Real-time monitoring of initiating events can reduce labor demand. Each extended Kalman filter is configured to operate at a single configuration, either at normal operation or a faulted state. Fault detection is based on calculating a likelihood function for each extended Kalman filter to determine the posteriori conditional probability that the system configuration is the true configuration. The transient identification is determined by performing a multivariate statistical analysis on each extended Kalman filter to provide a posterior probability assessment that the reactor states and its dynamics are within the allowable limits. The probability distribution being outside the allowable limits indicate a transient has occurred. The combination of parallel processing methods with the multivariate statistical analysis for transient identification allows for the operator to perform transient identification with having a high level of certainty of the current system configuration. We demonstrate the real-time fault detection and transient identification on a simple reactor example to determine the reactor power, reactor temperature, and coolant temperature in the presence of various faults. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Evaluate the impact of sensor accuracy on model performance in data-driven building fault detection and diagnostics using Monte Carlo simulation

The performance of data-driven fault detection and diagnostics (FDD) is heavily dependent on sensors. However, sensor inaccuracy and sensor faults are pervasive in building operation: inaccurate and missing sensor readings deteriorate FDD performance; sensor inaccuracy will also affect the selection of sensor for data-driven FDD in the model training process, which is another key factor of data-driven FDD performance. Sensor accuracy and sensor selection individually are well-studied research topics in this field, but the impact of sensor accuracy on sensor selection and its further impact on FDD performance has not been evaluated and quantified. In this paper, we developed a novel analysis methodology that comprehensively evaluates sensor fault on sensor selection and FDD accuracy. Monte Carlo simulation is applied to deal with multiple stochastic sensor inaccuracy and provide probabilistic analysis results of the impact of sensor inaccuracy on sensor selection and FDD accuracy. This methodology focuses on the net impact of fault states across a full sensor set. The developed methodology can be used for the early-stage sensor design and operation-stage sensor maintenance. Furthermore, a case study is conducted to demonstrate the analysis methodology using a commercial building model crated to Flexible Research Platform located at Oak Ridge National Laboratory, USA.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

An Envelope Time Synchronous Averaging for Wind Turbine Gearbox Fault Diagnosis

Vibration-based condition monitoring techniques are widely used for diagnosing faults in rotating machines. These techniques are implemented in the time domain, the frequency domain, or both. However, the composite and noisy nature of the raw data collected requires a preprocessing stage such as filtering and decomposition using in-depth processing techniques. Moreover, these methods require good frequency resolution and involve examining a broad frequency range to discern both healthy and faulty cases. In this work, we introduce a simple and fast diagnostic scheme for wind turbine gear teeth wear based on time domain analysis. The proposed method is based on the local minima interpolation of a filtered version of the vibration signal following time synchronous averaging (TSA) technique. Given tachometer signal, the TSA of the vibration data is performed using MTALAB software. Then, local minima of the filtered signal are interpolated using the Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) function. The variance of the interpolated curve built a gear fault index. The derived fault index resulting of the proposed technique allows a substantial distinction between the healthy and faulty cases. Its efficiency is validated using 10 real-world datasets of vibration stemmed from a wind turbine planetary gearbox. The proposed method boasts a low computation time and ease of interpretation, specifically beneficial for gearbox fault diagnosis purposes.

fault diagnosis↗

DOE OSTI2018

Collaborative operational fault tolerant control for stochastic distribution control system

Based on a class of industry processes, a distributed fault diagnosis and a collaborative operational fault tolerant control algorithms are presented for an irreversible interconnected stochastic distribution control system with boundary conditions. Different from the existed collaborative fault tolerant controllers, the controller does not need to change after fault occurs, the system tracking performance can be maintained by adjusting the setpoint of the whole system. Simulations are also included to show the effectiveness of the proposed algorithm and encouraging results have been obtained.

Stochastic distribution control, Fault diagnosis, ↗

DOE OSTI2025

Machine learning for photovoltaic single axis tracker fault detection and classification

More than 81% of the annual capacity of utility-scale photovoltaic (PV) power plants in the U.S. use single-axis trackers (SATs) due to SATs delivering 4% in capacity factor on average over fixed-array systems. However, SATs are subject to faults, such as software misconfigurations and mechanical failures, resulting in suboptimal tracking. If left undetected, the overall power yield of the PV power plant is reduced significantly. Minimizing downtime and ensuring efficient operation of SATs requires robust detection and diagnosis mechanisms for SAT faults. We present a machine learning framework for implementing real-time SAT fault detection and classification. Our implementation of the proposed framework reliably identifies measurements taken from a test PV system undergoing emulated SAT faults relative to state-of-the-art algorithms and produces nearly zero false positives on our testing days. Code and data are available at https://pvpmc.sandia.gov/tools.

Fault classification↗