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Smart Sensor Systems for High Temperature Intelligent Engine Applications
No abstract available
STS-114 Engine Cut-off Sensor Anomaly Technical Consultation Report
The NESC consultation team participated in real-time troubleshooting of the Main Propulsion System (MPS) Engine Cutoff (ECO) sensor system failures during STS-114 launch countdown. The team assisted with External Tank (ET) thermal and ECO Point Sensor Box (PSB) circuit analyses, and made real-time inputs to the Space Shuttle Program (SSP) problem resolution teams. Several long-term recommendations resulted. One recommendation was to conduct cryogenic tests of the ECO sensors to validate, or disprove, the theory that variations in circuit impedance due to cryogenic effects on swaged connections within the sensor were the root cause of STS-114 failures.
High Temperature Wireless Communication And Electronics For Harsh Environment Applications
In order for future aerospace propulsion systems to meet the increasing requirements for decreased maintenance, improved capability, and increased safety, the inclusion of intelligence into the propulsion system design and operation becomes necessary. These propulsion systems will have to incorporate technology that will monitor propulsion component conditions, analyze the incoming data, and modify operating parameters to optimize propulsion system operations. This implies the development of sensors, actuators, and electronics, with associated packaging, that will be able to operate under the harsh environments present in an engine. However, given the harsh environments inherent in propulsion systems, the development of engine-compatible electronics and sensors is not straightforward. The ability of a sensor system to operate in a given environment often depends as much on the technologies supporting the sensor element as the element itself. If the supporting technology cannot handle the application, then no matter how good the sensor is itself, the sensor system will fail. An example is high temperature environments where supporting technologies are often not capable of operation in engine conditions. Further, for every sensor going into an engine environment, i.e., for every new piece of hardware that improves the in-situ intelligence of the components, communication wires almost always must follow. The communication wires may be within or between parts, or from the engine to the controller. As more hardware is added, more wires, weight, complexity, and potential for unreliability is also introduced. Thus, wireless communication combined with in-situ processing of data would significantly improve the ability to include sensors into high temperature systems and thus lead toward more intelligent engine systems. NASA Glenn Research Center (GRC) is presently leading the development of electronics, communication systems, and sensors capable of prolonged stable operation in harsh 500C environments. This has included world record operation of SiC-based transistor technology (including packaging) that has demonstrated continuous electrical operation at 500C for over 2000 hours. Based on SiC electronics, development of high temperature wireless communication has been on-going. This work has concentrated on maturing the SiC electronic devices for communication purposes as well as the passive components such as resistors and capacitors needed to enable a high temperature wireless system. The objective is to eliminate wires associated with high temperature sensors which add weight to a vehicle and can be a cause of sensor unreliability. This paper discusses the development of SiC based electronics and wireless communications technology for harsh environment applications such as propulsion health management systems and in Venus missions. A brief overview of the future directions in sensor technology is given including maturing of near-room temperature "Lick and Stick" leak sensor technology for possible implementation in the Crew Launch Vehicle program. Then an overview of high temperature electronics and the development of high temperature communication systems is presented. The maturity of related technologies such as sensor and packaging will also be discussed. It is concluded that a significant component of efforts to improve the intelligence of harsh environment operating systems is the development and implementation of high temperature wireless technology
Strategy Developed for Selecting Optimal Sensors for Monitoring Engine Health
Sensor indications during rocket engine operation are the primary means of assessing engine performance and health. Effective selection and location of sensors in the operating engine environment enables accurate real-time condition monitoring and rapid engine controller response to mitigate critical fault conditions. These capabilities are crucial to ensure crew safety and mission success. Effective sensor selection also facilitates postflight condition assessment, which contributes to efficient engine maintenance and reduced operating costs. Under the Next Generation Launch Technology program, the NASA Glenn Research Center, in partnership with Rocketdyne Propulsion and Power, has developed a model-based procedure for systematically selecting an optimal sensor suite for assessing rocket engine system health. This optimization process is termed the systematic sensor selection strategy. Engine health management (EHM) systems generally employ multiple diagnostic procedures including data validation, anomaly detection, fault-isolation, and information fusion. The effectiveness of each diagnostic component is affected by the quality, availability, and compatibility of sensor data. Therefore systematic sensor selection is an enabling technology for EHM. Information in three categories is required by the systematic sensor selection strategy. The first category consists of targeted engine fault information; including the description and estimated risk-reduction factor for each identified fault. Risk-reduction factors are used to define and rank the potential merit of timely fault diagnoses. The second category is composed of candidate sensor information; including type, location, and estimated variance in normal operation. The final category includes the definition of fault scenarios characteristic of each targeted engine fault. These scenarios are defined in terms of engine model hardware parameters. Values of these parameters define engine simulations that generate expected sensor values for targeted fault scenarios. Taken together, this information provides an efficient condensation of the engineering experience and engine flow physics needed for sensor selection. The systematic sensor selection strategy is composed of three primary algorithms. The core of the selection process is a genetic algorithm that iteratively improves a defined quality measure of selected sensor suites. A merit algorithm is employed to compute the quality measure for each test sensor suite presented by the selection process. The quality measure is based on the fidelity of fault detection and the level of fault source discrimination provided by the test sensor suite. An inverse engine model, whose function is to derive hardware performance parameters from sensor data, is an integral part of the merit algorithm. The final component is a statistical evaluation algorithm that characterizes the impact of interference effects, such as control-induced sensor variation and sensor noise, on the probability of fault detection and isolation for optimal and near-optimal sensor suites.
Smart Sensor Demonstration Payload
Sensors are a critical element to any monitoring, control, and evaluation processes such as those needed to support ground based testing for rocket engine test. Sensor applications involve tens to thousands of sensors; their reliable performance is critical to achieving overall system goals. Many figures of merit are used to describe and evaluate sensor characteristics; for example, sensitivity and linearity. In addition, sensor selection must satisfy many trade-offs among system engineering (SE) requirements to best integrate sensors into complex systems [1]. These SE trades include the familiar constraints of power, signal conditioning, cabling, reliability, and mass, and now include considerations such as spectrum allocation and interference for wireless sensors. Our group at NASA s John C. Stennis Space Center (SSC) works in the broad area of integrated systems health management (ISHM). Core ISHM technologies include smart and intelligent sensors, anomaly detection, root cause analysis, prognosis, and interfaces to operators and other system elements [2]. Sensor technologies are the base fabric that feed data and health information to higher layers. Cost-effective operation of the complement of test stands benefits from technologies and methodologies that contribute to reductions in labor costs, improvements in efficiency, reductions in turn-around times, improved reliability, and other measures. ISHM is an active area of development at SSC because it offers the potential to achieve many of those operational goals [3-5].
Study to develop improved methods to detect leakage in fluid systems, phase 2
An ultrasonic contact sensor engineering prototype leak detection system was developed and its capabilities under cryogenic operations demonstrated. The results from tests indicate that the transducer performed well on liquid hydrogen plumbing, that flow and valve actuation could be monitored, and that the phase change from gaseous to liquid hydrogen could be detected by the externally mounted transducers. Tests also demonstrate the ability of the system to detect internal leaks past valve seats and to function as a flow meter. Such a system demonstrates that it is not necessary to break into welded systems to locate internal leaks.
Enhanced Bank of Kalman Filters Developed and Demonstrated for In-Flight Aircraft Engine Sensor Fault Diagnostics
In-flight sensor fault detection and isolation (FDI) is critical to maintaining reliable engine operation during flight. The aircraft engine control system, which computes control commands on the basis of sensor measurements, operates the propulsion systems at the demanded conditions. Any undetected sensor faults, therefore, may cause the control system to drive the engine into an undesirable operating condition. It is critical to detect and isolate failed sensors as soon as possible so that such scenarios can be avoided. A challenging issue in developing reliable sensor FDI systems is to make them robust to changes in engine operating characteristics due to degradation with usage and other faults that can occur during flight. A sensor FDI system that cannot appropriately account for such scenarios may result in false alarms, missed detections, or misclassifications when such faults do occur. To address this issue, an enhanced bank of Kalman filters was developed, and its performance and robustness were demonstrated in a simulation environment. The bank of filters is composed of m + 1 Kalman filters, where m is the number of sensors being used by the control system and, thus, in need of monitoring. Each Kalman filter is designed on the basis of a unique fault hypothesis so that it will be able to maintain its performance if a particular fault scenario, hypothesized by that particular filter, takes place.
Fiber optic (flight quality) sensors for advanced aircraft propulsion
Development of flight prototype, fiber-optic sensing system components for measuring nine sensed parameters (three temperatures, two speeds, three positions, and one flame) on an F404-400 aircraft engine is described. Details of each sensor's design, functionality, and environmental testing, and the electro-optics architecture for sensor signal conditioning are presented. Eight different optical sensing techniques were utilized. Design, assembly, and environmental testing of an engine-mounted, electro-optics chassis unit (EOU), providing MIL-C-1553 data output, are related. Interconnection cables and connectors between the EOU and the sensors are identified. Results of sensor/cable/circuitry integrated testing, and installation and ground testing of the sensor system on an engine in October 1993 and April 1994 are given, including comparisons with the engine control system's electrical sensors. Lessons learned about the design, fabrication, testing, and integration of the sensor system components are included.
Application of the Systematic Sensor Selection Strategy for Turbofan Engine Diagnostics
The data acquired from available system sensors forms the foundation upon which any health management system is based, and the available sensor suite directly impacts the overall diagnostic performance that can be achieved. While additional sensors may provide improved fault diagnostic performance there are other factors that also need to be considered such as instrumentation cost, weight, and reliability. A systematic sensor selection approach is desired to perform sensor selection from a holistic system-level perspective as opposed to performing decisions in an ad hoc or heuristic fashion. The Systematic Sensor Selection Strategy is a methodology that optimally selects a sensor suite from a pool of sensors based on the system fault diagnostic approach, with the ability of taking cost, weight and reliability into consideration. This procedure was applied to a large commercial turbofan engine simulation. In this initial study, sensor suites tailored for improved diagnostic performance are constructed from a prescribed collection of candidate sensors. The diagnostic performance of the best performing sensor suites in terms of fault detection and identification are demonstrated, with a discussion of the results and implications for future research.
Application of the Systematic Sensor Selection Strategy for Turbofan Engine Diagnostics
The data acquired from available system sensors forms the foundation upon which any health management system is based, and the available sensor suite directly impacts the overall diagnostic performance that can be achieved. While additional sensors may provide improved fault diagnostic performance, there are other factors that also need to be considered such as instrumentation cost, weight, and reliability. A systematic sensor selection approach is desired to perform sensor selection from a holistic system-level perspective as opposed to performing decisions in an ad hoc or heuristic fashion. The Systematic Sensor Selection Strategy is a methodology that optimally selects a sensor suite from a pool of sensors based on the system fault diagnostic approach, with the ability of taking cost, weight, and reliability into consideration. This procedure was applied to a large commercial turbofan engine simulation. In this initial study, sensor suites tailored for improved diagnostic performance are constructed from a prescribed collection of candidate sensors. The diagnostic performance of the best performing sensor suites in terms of fault detection and identification are demonstrated, with a discussion of the results and implications for future research.
Communal Sensor Network for Adaptive Noise Reduction in Aircraft Engine Nacelles
Emergent behavior, a subject of much research in biology, sociology, and economics, is a foundational element of Complex Systems Science and is apropos in the design of sensor network systems. To demonstrate engineering for emergent behavior, a novel approach in the design of a sensor/actuator network is presented maintaining optimal noise attenuation as an adaptation to changing acoustic conditions. Rather than use the conventional approach where sensors are managed by a central controller, this new paradigm uses a biomimetic model where sensor/actuators cooperate as a community of autonomous organisms, sharing with neighbors to control impedance based on local information. From the combination of all individual actions, an optimal attenuation emerges for the global system.
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.
Sensor failure detection for jet engines
The use of analytical redundancy to improve gas turbine engine control system reliability through sensor failure detection, isolation, and accommodation is surveyed. Both the theoretical and application papers that form the technology base of turbine engine analytical redundancy research are discussed. Also, several important application efforts are reviewed. An assessment of the state-of-the-art in analytical redundancy technology is given.
Sensor failure detection for jet engines
The use of analytical redundancy to improve gas turbine engine control system reliability through sensor failure detection, isolation, and accommodation is surveyed. Both the theoretical and application papers that form the technology base of turbine engine analytical redundancy research are discussed. Also, several important application efforts are reviewed. An assessment of the state-of-the-art in analytical redundancy technology is given.
Microelectromechanical Systems (MEMS) Broadband Light Source Developed
A miniature, low-power broadband light source has been developed for aerospace applications, including calibrating spectrometers and powering miniature optical sensors. The initial motivation for this research was based on flight tests of a Fabry-Perot fiberoptic temperature sensor system used to detect aircraft engine exhaust gas temperature. Although the feasibility of the sensor system was proven, the commercial light source optically powering the device was identified as a critical component requiring improvement. Problems with the light source included a long stabilization time (approximately 1 hr), a large amount of heat generation, and a large input electrical power (6.5 W). Thus, we developed a new light source to enable the use of broadband optical sensors in aerospace applications. Semiconductor chip-based light sources, such as lasers and light-emitting diodes, have a relatively narrow range of emission wavelengths in comparison to incandescent sources. Incandescent light sources emit broadband radiation from visible to infrared wavelengths; the intensity at each wavelength is determined by the filament temperature and the materials chosen for the filament and the lamp window. However, present commercial incandescent light sources are large in size and inefficient, requiring several watts of electrical power to obtain the desired optical power, and they emit a large percentage of the input power as heat that must be dissipated. The miniature light source, developed jointly by the NASA Glenn Research Center, the Jet Propulsion Laboratory, and the Lighting Innovations Institute, requires one-fifth the electrical input power of some commercial light sources, while providing similar output light power that is easily coupled to an optical fiber. Furthermore, it is small, rugged, and lightweight. Microfabrication technology was used to reduce the size, weight, power consumption, and potential cost-parameters critical to future aerospace applications. This chip-based light source has the potential for monolithic fabrication with on-chip drive electronics. Other uses for these light sources are in systems for vehicle navigation, remote sensing applications such as monitoring bridges for stress, calibration sources for spectrometers, light sources for space sensors, display lighting, addressable arrays, and industrial plant monitoring. Two methods for filament fabrication are being developed: wet-chemical etching and laser ablation. Both yield a 25-mm-thick tungsten spiral filament. The proof-of-concept filament shown was fabricated with the wet etch method. Then it was tested by heating it in a vacuum chamber using about 1.25 W of electrical power; it generated bright, blackbody radiation at approximately 2650 K. The filament was packaged in Glenn's clean-room facilities. This design uses three chips vacuum-sealed with glass tape. The bottom chip consists of a reflective film deposited on silicon, the middle chip contains a tungsten filament bonded to silicon, and the top layer is a transparent window. Lifetime testing on the package will begin shortly. The emitted optical power is expected to be approximately 1.0 W with the spectral peak at 1.1 mm.
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.
Research sensors
The LeRC program in research sensors is directed at development of sensors and sensing techniques for research applications on turbine engines and propulsion systems. In general, the sensors are used either to measure to response of an engine component to the imposed environment. Locations of concern are generally within the gas path and, for the most part, are within the hot section of the engine. Since these sensors are used for research testing as opposed to operational use, a sensor lifetime of the order of 50 hr is considered sufficient. The following discussion presents a sample of this work, describing programs to develop a dynamic gas temperature measuring system, total heat flux sensors, a variety of thin-film sensors, and high-temperature strain measuring systems.