Search NASA⌕ Search

SEARCH · Search NASA

Results for “sensor fault”

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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
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↗
NASA NTRS2006

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

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

Kobayashi, Takahisa↗
NASA NTRS2010

A Unified Nonlinear Adaptive Approach for Detection and Isolation of Engine Faults

A challenging problem in aircraft engine health management (EHM) system development is to detect and isolate faults in system components (i.e., compressor, turbine), actuators, and sensors. Existing nonlinear EHM methods often deal with component faults, actuator faults, and sensor faults separately, which may potentially lead to incorrect diagnostic decisions and unnecessary maintenance. Therefore, it would be ideal to address sensor faults, actuator faults, and component faults under one unified framework. This paper presents a systematic and unified nonlinear adaptive framework for detecting and isolating sensor faults, actuator faults, and component faults for aircraft engines. The fault detection and isolation (FDI) architecture consists of a parallel bank of nonlinear adaptive estimators. Adaptive thresholds are appropriately designed such that, in the presence of a particular fault, all components of the residual generated by the adaptive estimator corresponding to the actual fault type remain below their thresholds. If the faults are sufficiently different, then at least one component of the residual generated by each remaining adaptive estimator should exceed its threshold. Therefore, based on the specific response of the residuals, sensor faults, actuator faults, and component faults can be isolated. The effectiveness of the approach was evaluated using the NASA C-MAPSS turbofan engine model, and simulation results are presented.

Tang, Liang↗
DOE OSTI2021

Sensor impact evaluation and verification for fault detection and diagnostics in building energy systems: A review

Sensors are the key information source for fault detection and diagnostics (FDD) in buildings. However, sensors are often not properly designed, installed, calibrated, located, and maintained, which negatively impacts FDD performance. Several sensor-related FDD topics have been widely studied, covering a wide range of fault types and applications. However, it is difficult to get a clear picture of the technical development of sensor-related topics in FDD. A systematic review of sensor topics is needed to summarize the existing research in a logical way, draw conclusions on the current development, and predict the future development of sensors in building FDD. To address this gap, we conducted a comprehensive literature review of more than 100 FDD-sensor-related papers. In this article, we subdivide the FDD tasks into building-level, system-level, and component-level FDD, and review sensor-related topics in each category. Our major conclusions are: (a) current data-driven FDD research focuses more on FDD algorithms than sensors, (b) sensor “hardware” research topics are less studied than sensor “software” topics, (c) very few papers focus on sensor engineering as an integral aspect of FDD development, and (d) some important sensor topics, such as sensor cost-effectiveness and sensor schema/layout/location, are not well studied. Finally, we discuss the need for a systematic framework of FDD sensors and models to integrate sensor design/selection, sensor data analysis/mining, feature selection, physics-based or data-driven algorithm development, sensor fault detection, sensor calibration, and sensor maintenance. Finally, expert interviews are conducted to validate the above findings and conclusions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2008

Benchmarking Gas Path Diagnostic Methods: A Public Approach

Recent technology reviews have identified the need for objective assessments of engine health management (EHM) technology. The need is two-fold: technology developers require relevant data and problems to design and validate new algorithms and techniques while engine system integrators and operators need practical tools to direct development and then evaluate the effectiveness of proposed solutions. This paper presents a publicly available gas path diagnostic benchmark problem that has been developed by the Propulsion and Power Systems Panel of The Technical Cooperation Program (TTCP) to help address these needs. The problem is coded in MATLAB (The MathWorks, Inc.) and coupled with a non-linear turbofan engine simulation to produce "snap-shot" measurements, with relevant noise levels, as if collected from a fleet of engines over their lifetime of use. Each engine within the fleet will experience unique operating and deterioration profiles, and may encounter randomly occurring relevant gas path faults including sensor, actuator and component faults. The challenge to the EHM community is to develop gas path diagnostic algorithms to reliably perform fault detection and isolation. An example solution to the benchmark problem is provided along with associated evaluation metrics. A plan is presented to disseminate this benchmark problem to the engine health management technical community and invite technology solutions.

Simon, Donald L.↗
NASA NTRS2012

Model-Based Method for Sensor Validation

Fault detection, diagnosis, and prognosis are essential tasks in the operation of autonomous spacecraft, instruments, and in situ platforms. One of NASA s key mission requirements is robust state estimation. Sensing, using a wide range of sensors and sensor fusion approaches, plays a central role in robust state estimation, and there is a need to diagnose sensor failure as well as component failure. Sensor validation can be considered to be part of the larger effort of improving reliability and safety. The standard methods for solving the sensor validation problem are based on probabilistic analysis of the system, from which the method based on Bayesian networks is most popular. Therefore, these methods can only predict the most probable faulty sensors, which are subject to the initial probabilities defined for the failures. The method developed in this work is based on a model-based approach and provides the faulty sensors (if any), which can be logically inferred from the model of the system and the sensor readings (observations). The method is also more suitable for the systems when it is hard, or even impossible, to find the probability functions of the system. The method starts by a new mathematical description of the problem and develops a very efficient and systematic algorithm for its solution. The method builds on the concepts of analytical redundant relations (ARRs).

Vatan, Farrokh↗
NASA NTRS2016

R2U2 in Space: System and Software Health Management for Small Satellites

In order for small but complex systems like rovers, SmallSats, or Unmanned Aircraft (UAS) to operate autonomously, they must have a real-time solution for assessing their own system health. System and Software Health Management (SHM) enables better detection of faulty sensors and software problems, and enables better fault management including mitigation of unpredicted fault scenarios in the absence of a human on-board. In recent work, we have developed a Responsive, Realizable, Unobtrusive Unit (R2U2) for on-board SHM of autonomous UAS and demonstrated its ability to detect faults during flight time. These faults, from sensor failures, to software problems, to malicious security attacks, can present as transient temporal faults that even humans are challenged to find. An R2U2 congfiuration is a modular combination of multiple types of temporal logic runtime observers with fault-specic Bayesian Nets and sensor filters. R2U2 reasons about both on-board hardware and software components; R2U2 itself can be instantiated as an independent FPGA (Field-Programmable Gate Array)-based conguration or as a software component running independently from other software on-board. Small satellites, such as CubeSats, also require on-board SHM and failure mitigation, as limited telemetry bandwidth does not allow the transmission of the entire system state for ground-based health management. However, the autonomous operation of satellites brings a set of challenges different from UAS, including the effects of radiation on non-rad-hard, low-cost components, and the harsher environment of space. We surmise that a new extension of R2U2 could be adapted to help better detect, for example, radiation errors in cheaper COTS (Commercial Off the Shelf) (not rad-hard) components often used in small space systems. Since small satellites often operate in coordination, we will also examine new ways of distributed monitoring of their communication and cooperation and real-time detection of off-nominal situations utilizing multiple satellites. This talk will discuss preliminary work and ideas for building on terrestrial success of system and software health management for the harsher, and differently challenging, environment of space.

Runtime Verification & Validation↗
NASA NTRS2015

Evaluation of MEMS-Based Wireless Accelerometer Sensors in Detecting Gear Tooth Faults in Helicopter Transmissions

The diagnostics capability of micro-electro-mechanical systems (MEMS) based rotating accelerometer sensors in detecting gear tooth crack failures in helicopter main-rotor transmissions was evaluated. MEMS sensors were installed on a pre-notched OH-58C spiral-bevel pinion gear. Endurance tests were performed and the gear was run to tooth fracture failure. Results from the MEMS sensor were compared to conventional accelerometers mounted on the transmission housing. Most of the four stationary accelerometers mounted on the gear box housing and most of the CI's used gave indications of failure at the end of the test. The MEMS system performed well and lasted the entire test. All MEMS accelerometers gave an indication of failure at the end of the test. The MEMS systems performed as well, if not better, than the stationary accelerometers mounted on the gear box housing with regards to gear tooth fault detection. For both the MEMS sensors and stationary sensors, the fault detection time was not much sooner than the actual tooth fracture time. The MEMS sensor spectrum data showed large first order shaft frequency sidebands due to the measurement rotating frame of reference. The method of constructing a pseudo tach signal from periodic characteristics of the vibration data was successful in deriving a TSA signal without an actual tach and proved as an effective way to improve fault detection for the MEMS.

gears↗
NASA NTRS1982

A fault tolerant approach to state estimation and failure detection in nonlinear systems

The design problem considered in the present investigation involves a nonlinear discrete time stochastic system where replicated sensors provide redundant observations of inputs and outputs of the system, compensate for sensor 'normal operating' bias levels, and generate reliable estimates for the plant states in the presence of possible sensor failures. The resulting fault tolerant design should utilize inherent analytical redundancy and be capable of detecting many different types and levels of sensor failures. In addition, it should have minimal complexity. In connection with these goals, a sensor fault tolerant system design methodology is developed. The performance of the considered approach to an application is discussed, taking into account the design of a sensor fault tolerant system using analytic redundancy for the Terminal Configured Vehicle research aircraft in a microwave landing system environment.

Lancraft, R. E.↗
NASA NTRS2008

Aircraft Engine On-Line Diagnostics Through Dual-Channel Sensor Measurements: Development of a Baseline System

In this paper, a baseline system which utilizes dual-channel sensor measurements for aircraft engine on-line diagnostics is developed. This system is composed of a linear on-board engine model (LOBEM) and fault detection and isolation (FDI) logic. The LOBEM provides the analytical third channel against which the dual-channel measurements are compared. When the discrepancy among the triplex channels exceeds a tolerance level, the FDI logic determines the cause of the discrepancy. Through this approach, the baseline system achieves the following objectives: (1) anomaly detection, (2) component fault detection, and (3) sensor fault detection and isolation. The performance of the baseline system is evaluated in a simulation environment using faults in sensors and components.

Kobayashi, Takahisa↗
NASA NTRS2008

Aircraft Engine On-Line Diagnostics Through Dual-Channel Sensor Measurements: Development of an Enhanced System

In this paper, an enhanced on-line diagnostic system which utilizes dual-channel sensor measurements is developed for the aircraft engine application. The enhanced system is composed of a nonlinear on-board engine model (NOBEM), the hybrid Kalman filter (HKF) algorithm, and fault detection and isolation (FDI) logic. The NOBEM provides the analytical third channel against which the dual-channel measurements are compared. The NOBEM is further utilized as part of the HKF algorithm which estimates measured engine parameters. Engine parameters obtained from the dual-channel measurements, the NOBEM, and the HKF are compared against each other. When the discrepancy among the signals exceeds a tolerance level, the FDI logic determines the cause of discrepancy. Through this approach, the enhanced system achieves the following objectives: 1) anomaly detection, 2) component fault detection, and 3) sensor fault detection and isolation. The performance of the enhanced system is evaluated in a simulation environment using faults in sensors and components, and it is compared to an existing baseline system.

Kobayashi, Takahisa↗
NASA NTRS1974

Technical support for guidance, navigation and control space shuttle program

A review of the guidance, navigation and control projects in support of the space shuttle program was conducted. The subjects considered include the following: (1) functional and performance requirements, (2) mission requirements, (3) operating systems software definition, (4) orbit navigation using various sensors, (5) fault detection, isolation and recovery, and (6) passive rendezvous sensors requirements definition.

Source record↗
NASA NTRS2013

Discrete Data Qualification System and Method Comprising Noise Series Fault Detection

A Sensor Data Qualification (SDQ) function has been developed that allows the onboard flight computers on NASA s launch vehicles to determine the validity of sensor data to ensure that critical safety and operational decisions are not based on faulty sensor data. This SDQ function includes a novel noise series fault detection algorithm for qualification of the output data from LO2 and LH2 low-level liquid sensors. These sensors are positioned in a launch vehicle s propellant tanks in order to detect propellant depletion during a rocket engine s boost operating phase. This detection capability can prevent the catastrophic situation where the engine operates without propellant. The output from each LO2 and LH2 low-level liquid sensor is a discrete valued signal that is expected to be in either of two states, depending on whether the sensor is immersed (wet) or exposed (dry). Conventional methods for sensor data qualification, such as threshold limit checking, are not effective for this type of signal due to its discrete binary-state nature. To address this data qualification challenge, a noise computation and evaluation method, also known as a noise fault detector, was developed to detect unreasonable statistical characteristics in the discrete data stream. The method operates on a time series of discrete data observations over a moving window of data points and performs a continuous examination of the resulting observation stream to identify the presence of anomalous characteristics. If the method determines the existence of anomalous results, the data from the sensor is disqualified for use by other monitoring or control functions.

Fulton, Christopher↗
NASA NTRS1990

Learning and diagnosing faults using neural networks

Neural networks have been employed for learning fault behavior from rocket engine simulator parameters and for diagnosing faults on the basis of the learned behavior. Two problems in applying neural networks to learning and diagnosing faults are (1) the complexity of the sensor data to fault mapping to be modeled by the neural network, which implies difficult and lengthy training procedures; and (2) the lack of sufficient training data to adequately represent the very large number of different types of faults which might occur. Methods are derived and tested in an architecture which addresses these two problems. First, the sensor data to fault mapping is decomposed into three simpler mappings which perform sensor data compression, hypothesis generation, and sensor fusion. Efficient training is performed for each mapping separately. Secondly, the neural network which performs sensor fusion is structured to detect new unknown faults for which training examples were not presented during training. These methods were tested on a task of fault diagnosis by employing rocket engine simulator data. Results indicate that the decomposed neural network architecture can be trained efficiently, can identify faults for which it has been trained, and can detect the occurrence of faults for which it has not been trained.

Whitehead, Bruce A.↗
NASA NTRS2003

Application of a Bank of Kalman Filters for Aircraft Engine Fault Diagnostics

In this paper, a bank of Kalman filters is applied to aircraft gas turbine engine sensor and actuator fault detection and isolation (FDI) in conjunction with the detection of component faults. This approach uses multiple Kalman filters, each of which is designed for detecting a specific sensor or actuator fault. In the event that a fault does occur, all filters except the one using the correct hypothesis will produce large estimation errors, thereby isolating the specific fault. In the meantime, a set of parameters that indicate engine component performance is estimated for the detection of abrupt degradation. The proposed FDI approach is applied to a nonlinear engine simulation at nominal and aged conditions, and the evaluation results for various engine faults at cruise operating conditions are given. The ability of the proposed approach to reliably detect and isolate sensor and actuator faults is demonstrated.

Kobayashi, Takahisa↗
NASA NTRS1990

Rocket engine diagnostics using neural networks

Two problems in applying neural networks to fault detection and identification are (1) the complexity of the sensor data to fault mapping and (2) the lack of sufficient training data. Here, methods are derived and tested in an architecture which addresses these two problems. First, the sensor data to fault mapping is decomposed into three simpler mappings which perform sensor data compression, hypothesis generation, and sensor fusion. Efficient training is performed for each mapping separately. Second, the neural network which performs sensor fusion is structured to detect new unknown faults for which training examples were not presented. These methods were tested on a task of fault detection and identification in the Space Shuttle Main Engine (SSME). Results indicate that the decomposed neural network architecture can be trained efficiently, can identify faults for which it has been trained, and can detect the occurrence of faults for which it has not been trained.

Whitehead, Bruce A.↗