Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “ELECTRICAL FAULTS”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Autonomous Inspection of Electrical Transmission Structures with Airborne UV Sensors - NASA Report on Dominion Virginia Power Flights of November 2016

The report details test and measurement flights to demonstrate autonomous UAV inspection of high voltage electrical transmission structures. A UAV built with commercial, off-the-shelf hardware and software, supplemented with custom sensor logging software, measured ultraviolet emissions from a test generator placed on a low-altitude substation and a medium-altitude switching tower. Since corona discharge precedes catastrophic electrical faults on high-voltage structures, detection and geolocation of ultraviolet emissions is needed to develop a UAV-based self-diagnosing power grid. Signal readings from an onboard ultraviolet sensor were validated during flight with a commercial corona camera. Geolocation was accomplished with onboard GPS; the UAV position was logged to a local ground station and transmitted in real time to a NASA server for tracking in the national airspace.

Moore, Andrew J.↗
NASA NTRS2018

Autonomous Inspection of Electrical Transmission Structures with Airborne UV Sensors and Automated Air Traffic Management

This report details test and measurement flights to demonstrate autonomous UAV (Unmanned Aerial Vehicle) inspection of high-voltage electrical transmission structures. A UAV built with commercial, off-the-shelf hardware and software, supplemented with custom sensors and logging software, measured ultraviolet (UV) emissions from a test generator placed on a low-altitude substation and a medium-altitude switching tower. Since corona discharge precedes catastrophic electrical faults on high-voltage structures, detection and geolocation of ultraviolet emissions is needed to develop a UAV-based self-diagnosing power grid. Signal readings from an onboard ultraviolet sensor were validated during flight with a commercial corona camera. Geolocation was accomplished with onboard GPS; the UAV position was logged to a local ground station and transmitted in real time to a NASA server for tracking in the national airspace. The method has practicality and relevance but not adequacy; either improved UAV position determination technology or increased sensor range is needed to enable broad deployment of this method.

Moore, Andrew J.↗
NASA NTRS2009

Estimation of Faults in DC Electrical Power System

This paper demonstrates a novel optimization-based approach to estimating fault states in a DC power system. Potential faults changing the circuit topology are included along with faulty measurements. Our approach can be considered as a relaxation of the mixed estimation problem. We develop a linear model of the circuit and pose a convex problem for estimating the faults and other hidden states. A sparse fault vector solution is computed by using 11 regularization. The solution is computed reliably and efficiently, and gives accurate diagnostics on the faults. We demonstrate a real-time implementation of the approach for an instrumented electrical power system testbed, the ADAPT testbed at NASA ARC. The estimates are computed in milliseconds on a PC. The approach performs well despite unmodeled transients and other modeling uncertainties present in the system.

Gorinevsky, Dimitry↗
NASA NTRS2009

Methods for Probabilistic Fault Diagnosis: An Electrical Power System Case Study

Health management systems that more accurately and quickly diagnose faults that may occur in different technical systems on-board a vehicle will play a key role in the success of future NASA missions. We discuss in this paper the diagnosis of abrupt continuous (or parametric) faults within the context of probabilistic graphical models, more specifically Bayesian networks that are compiled to arithmetic circuits. This paper extends our previous research, within the same probabilistic setting, on diagnosis of abrupt discrete faults. Our approach and diagnostic algorithm ProDiagnose are domain-independent; however we use an electrical power system testbed called ADAPT as a case study. In one set of ADAPT experiments, performed as part of the 2009 Diagnostic Challenge, our system turned out to have the best performance among all competitors. In a second set of experiments, we show how we have recently further significantly improved the performance of the probabilistic model of ADAPT. While these experiments are obtained for an electrical power system testbed, we believe they can easily be transitioned to real-world systems, thus promising to increase the success of future NASA missions.

Ricks, Brian W.↗
NASA NTRSDate not supplied

Solid-State Exergy Optimized Electric Aircraft Thermal and Fault Management

Future air vehicles will increasingly incorporate electrical powertrains that require very tight system level integration of power, propulsion, thermal, fault protection, and airframe technologies. This paper provides an overview of a new category of thermal energy conversion technology that can be used to enable a fully solid-state integrated thermal and fault management electric aircraft protection system, while synergistically managing and recycling both the low-grade waste heat from electrical components and the high-grade waste heat from engine components. This is achieved with exergy amplification of the powertrain waste-heat, a new class of fast flight-weight breakers, new class of long variable conductance heat pipe with multiple switchable condensers, new class of turbofan integrated heat exchangers and a gradient-based powertrain system optimizer.

safety↗
NASA NTRSDate not supplied

Hybrid Modeling of Unmanned Aerial Vehicle Electric Powertrain for Fault Detection and Diagnostics

This paper shows the application of hybrid physics-informed machine learning to a representative electric powertrain for unmanned aerial vehicles. The model is composed of physics-derived principles and empirical equations, as well as fully connected networks that are strategically placed within the model to substitute equations that are subject to large uncertainty. Polynomial fit driven by heuristics or empirical observations can be substituted by more flexible networks that can minimize the error between model predictions and observations without being restricted to a predefined functional form. This modeling strategy allows training of networks deep inside the model and unknown parameters in a single learning stage. It has already been applied to Li-ion batteries in the past, and in this work, we extend the applications to other components of an electric powertrain, namely electronic speed controller with pulse-width modulation, and brushless DC motor with connected propeller. Training and testing of the model is carried out using experimental data from Li-ion battery discharge and powertrain testing in a laboratory environment.

Physics-Informed Machine Learning↗
NASA NTRS2009

The Diagnostic Challenge Competition: Probabilistic Techniques for Fault Diagnosis in Electrical Power Systems

Reliable systems health management is an important research area of NASA. A health management system that can accurately and quickly diagnose faults in various on-board systems of a vehicle will play a key role in the success of current and future NASA missions. We introduce in this paper the ProDiagnose algorithm, a diagnostic algorithm that uses a probabilistic approach, accomplished with Bayesian Network models compiled to Arithmetic Circuits, to diagnose these systems. We describe the ProDiagnose algorithm, how it works, and the probabilistic models involved. We show by experimentation on two Electrical Power Systems based on the ADAPT testbed, used in the Diagnostic Challenge Competition (DX 09), that ProDiagnose can produce results with over 96% accuracy and less than 1 second mean diagnostic time.

Ricks, Brian W.↗
NASA NTRS2009

Developing Large-Scale Bayesian Networks by Composition: Fault Diagnosis of Electrical Power Systems in Aircraft and Spacecraft

In this paper, we investigate the use of Bayesian networks to construct large-scale diagnostic systems. In particular, we consider the development of large-scale Bayesian networks by composition. This compositional approach reflects how (often redundant) subsystems are architected to form systems such as electrical power systems. We develop high-level specifications, Bayesian networks, clique trees, and arithmetic circuits representing 24 different electrical power systems. The largest among these 24 Bayesian networks contains over 1,000 random variables. Another BN represents the real-world electrical power system ADAPT, which is representative of electrical power systems deployed in aerospace vehicles. In addition to demonstrating the scalability of the compositional approach, we briefly report on experimental results from the diagnostic competition DXC, where the ProADAPT team, using techniques discussed here, obtained the highest scores in both Tier 1 (among 9 international competitors) and Tier 2 (among 6 international competitors) of the industrial track. While we consider diagnosis of power systems specifically, we believe this work is relevant to other system health management problems, in particular in dependable systems such as aircraft and spacecraft. (See CASI ID 20100021910 for supplemental data disk.)

Mengshoel, Ole Jakob↗
NASA NTRS2009

Developing Large-Scale Bayesian Networks by Composition: Fault Diagnosis of Electrical Power Systems in Aircraft and Spacecraft

This CD contains files that support the talk (see CASI ID 20100021404). There are 24 models that relate to the ADAPT system and 1 Excel worksheet. In the paper an investigation into the use of Bayesian networks to construct large-scale diagnostic systems is described. The high-level specifications, Bayesian networks, clique trees, and arithmetic circuits representing 24 different electrical power systems are described in the talk. The data in the CD are the models of the 24 different power systems.

Mengshoel, Ole Jakob↗
NASA NTRS1992

Large transient fault current test of an electrical roll ring

The space station uses precision rotary gimbals to provide for sun tracking of its photoelectric arrays. Electrical power, command signals and data are transferred across the gimbals by roll rings. Roll rings have been shown to be capable of highly efficient electrical transmission and long life, through tests conducted at the NASA Lewis Research Center and Honeywell's Satellite and Space Systems Division in Phoenix, AZ. Large potential fault currents inherent to the power system's DC distribution architecture, have brought about the need to evaluate the effects of large transient fault currents on roll rings. A test recently conducted at Lewis subjected a roll ring to a simulated worst case space station electrical fault. The system model used to obtain the fault profile is described, along with details of the reduced order circuit that was used to simulate the fault. Test results comparing roll ring performance before and after the fault are also presented.

Yenni, Edward J.↗
NASA NTRS1991

Large transient fault current test of an electrical roll ring

The Space Station Freedom uses precision rotary gimbals to provide for sun tracking of its photoelectric arrays. Electrical power, command signals, and data are transferred across the gimbals by roll rings. Roll rings have been shown to be capable of highly efficient electrical transmission and long life, through tests conducted at the NASA Lewis Research Center and Honeywell's Satellite and Space Systems Division in Phoenix, AZ. Large potential fault currents inherent to the power system's DC distribution architecture have brought about the need to evaluate the effects of large transient fault currents on roll rings. A test recently conducted at Lewis subjected a roll ring to a simulated worst case space station electrical fault. The system model used to obtain the fault profile is described, along with details of the reduced order circuit that was used to simulate the fault. Test results comparing roll ring performance before and after the fault are also presented.

Yenni, Edward J.↗
NASA NTRS1978

Shock Prevention

The electrician pictured is installing a General Electric Ground Fault Interrupter (GFI), a device which provides protection against electrical shock in the home or in industrial facilities. Shocks due to defective wiring in home appliances or other electrical equipment can cause severe burns, even death. As a result, the National Electrical Code now requires GFIs in all new homes constructed. This particular type of GFI employs a sensing element which derives from technology acquired in space projects by SCI Systems, Inc., Huntsville, Alabama, producer of sensors for GE and other manufacturers of GFI equipment. The sensor is based on the company's experience in developing miniaturized circuitry for space telemetry and other spacecraft electrical systems; this experience enabled SCI to package interruptor circuitry in the extremely limited space available and to produce sensory devices at practicable cost. The tiny sensor measures the strength of the electrical current and detects current differentials that indicate a fault in the functioning of an electrical system. The sensing element then triggers a signal to a disconnect mechanism in the GFI, which cuts off the current in the faulty circuit.

Source record↗
NASA NTRS2014

Demonstration of Prognostics-Enabled Decision Making Algorithms on a Hardware Mobile Robot Test Platform

Prognostics-enabled Decision Making (PDM) is an emerging research area that aims to integrate prognostic health information and knowledge about the future operating conditions into the process of selecting subsequent actions for the system. Previous work developing and testing PDM algorithms has been done in simulation; this paper describes the effort leading to a successful demonstration of PDM algorithms on a hardware mobile robot platform. The hardware platform, based on the K11 planetary rover prototype, was modified to allow injection of selected fault modes related to the rover’s electrical power subsystem. The PDM algorithms were adapted to the hardware platform, including development of a software module framework, a new route planner, and modifications to increase the algorithms’ robustness to sensor noise and system timing issues. A set of test scenarios was chosen to demonstrate the algorithms’ capabilities. The modifications to run with a hardware platform, the test scenarios, and the test results are described in detail. The results show a successful use of PDM algorithms on a hardware test platform to optimize mission planning in the presence of electrical system faults.

Prognosis↗
NASA NTRS2020

Development of a Supervisory Tool for Fault Detection and Diagnosis of DC Electric Power Systems with the Application of Deep Space Vehicles

This dissertation formulates the problem of fault detection and diagnosis of DC electric power systems for the application of autonomous spacecraft. The ability to accurately identify and isolate failures in the electrical power system is critical to ensure the reliability of a spacecraft. This problem becomes more pronounced during deep space missions that lack the ability to monitor from ground control. The current state of electrical power system fault supervision is insufficient to guarantee highly reliable and robust operation. To solve this issue, a combination of model-based and rules-based techniques are used in a hierarchical framework to improve the diagnostic performance of the spacecraft electrical power system. Noise, disturbances, and modeling errors are considered in the design of the method. Practical considerations related to the hardware and software are discussed for the flight application. A wide array of failure types are simulated in a series of experiments to assess the functionality of the design. The experiments showed that the methods used improved the diagnostic capability of the autonomous system while taking into account the limitations attributed to flight software requirements. The significance of this study is to provide a framework capable of advanced diagnostics of an electrical power system with little to no interaction from a human operator.

Fault Detection and Diagnosis↗
NASA NTRS2015

Architecture, Voltage, and Components for a Turboelectric Distributed Propulsion Electric Grid (AVC-TeDP)

The purpose of this effort was to advance the selection, characterization, and modeling of a propulsion electric grid for a Turboelectric Distributed Propulsion (TeDP) system for transport aircraft. The TeDP aircraft would constitute a miniature electric grid with 50 MW or more of total power, two or more generators, redundant transmission lines, and multiple electric motors driving propulsion fans. The study proposed power system architectures, investigated electromechanical and solid state circuit breakers, estimated the impact of the system voltage on system mass, and recommended DC bus voltage range. The study assumed an all cryogenic power system. Detailed assumptions within the study include hybrid circuit breakers, a two cryogen system, and supercritical cyrogens. A dynamic model was developed to investigate control and parameter selection.

power↗
NASA NTRS1990

Fault analysis of multichannel spacecraft power systems

The NASA Marshall Space Flight Center proposes to implement computer-controlled fault injection into an electrical power system breadboard to study the reactions of the various control elements of this breadboard. Elements under study include the remote power controllers, the algorithms in the control computers, and the artificially intelligent control programs resident in this breadboard. To this end, a study of electrical power system faults is being performed to yield a list of the most common power system faults. The results of this study will be applied to a multichannel high-voltage DC spacecraft power system called the large autonomous spacecraft electrical power system (LASEPS) breadboard. The results of the power system fault study and the planned implementation of these faults into the LASEPS breadboard are described.

Dugal-Whitehead, Norma R.↗
NASA NTRSDate not supplied

Enhancing Fault Isolation for Health Monitoring of Electric Aircraft Propulsion by Embedding Failure Mode and Effect Analysis into Bayesian Networks

This paper describes a fault isolation approach for electric powertrains of unmanned aerial vehicles. The approach leverages the combination of failure mode and effect analysis (FMEA) and Bayesian networks, thus introducing depend-ability structures into a diagnostic framework. Faults and failure events from the FMEA are mapped within a Bayesian network, where network edges replicate the links embedded within FMEAs. This framework helps the fault isolation process by identifying the probability of occurrence of specific faults or root causes given evidence observed through sensor signals. The framework is applied to an electric power-train system of a small, rotary-wing unmanned aerial vehicle, demonstrating how a Bayesian network enhanced by FMEA helps disambiguate between root causes of incipient failures, which would otherwise be considered as equally probable.

Fault Isolation↗
NASA NTRS2020

Electric Aircraft Cooling with Bio-Inspired Exergy Management

Future air vehicles will increasingly incorporate electrical powertrains that require very tight system-level integration of power, propulsion, thermal, fault protection, and airframe technologies. This chapter provides an overview of a new category of thermal energy conversion technology and describes the development of the resulting Thermal Recovery Exergy Efficient System, TREES. TREES can be used to enable a fully solid-state integrated thermal and fault management electric aircraft protection system, while synergistically managing and recycling both the low-grade waste heat from electrical components and the high-grade waste heat from engine components. This is achieved with exergy amplification of the powertrain waste heat, a new class of fast flight-weight breakers, a new class of long variable-conductance heat pipes with multiple switchable condensers, a new class of turbofan-integrated heat exchangers, and a gradient-based powertrain system optimizer. This approach is like the human body’s thermal management system in which the heart, arteries, and veins are analogous to the turbofan, acoustic tubes, and heat pipes proposed herein.

Rodger W Dyson↗