Integrated control and health management. Orbit transfer rocket engine technology program

To insure controllability of the baseline design for a 7500 pound thrust, 10:1 throttleable, dual expanded cycle, Hydrogen-Oxygen, orbit transfer rocket engine, an Integrated Controls and Health Monitoring concept was developed. This included: (1) Dynamic engine simulations using a TUTSIM derived computer code; (2) analysis of various control methods; (3) Failure Modes Analysis to identify critical sensors; (4) Survey of applicable sensors technology; and, (5) Study of Health Monitoring philosophies. The engine design was found to be controllable over the full throttling range by using 13 valves, including an oxygen turbine bypass valve to control mixture ratio, and a hydrogen turbine bypass valve, used in conjunction with the oxygen bypass to control thrust. Classic feedback control methods are proposed along with specific requirements for valves, sensors, and the controller. Expanding on the control system, a Health Monitoring system is proposed including suggested computing methods and the following recommended sensors: (1) Fiber optic and silicon bearing deflectometers; (2) Capacitive shaft displacement sensors; and (3) Hot spot thermocouple arrays. Further work is needed to refine and verify the dynamic simulations and control algorithms, to advance sensor capabilities, and to develop the Health Monitoring computational methods

Particle-filter-based fault diagnosis for the startup process of an open-cycle liquid-propellant rocket engine

This study introduces a fault diagnosis algorithm based on particle filtering for open-cycle liquid-propellant rocket engines (LPREs). The algorithm serves as a model-based method for the startup process, accounting for more than 30% of engine failures. Similar to the previous fault detection and diagnosis (FDD) algorithm for the startup process, the algorithm in this study is composed of a nonlinear filter to generate residuals, a residual analysis, and a multiple-model (MM) approach to detect and diagnose faults from the residuals. In contrast to the previous study, this study makes use of the modified cumulative sum (CUSUM) algorithm, widely used in change-detection monitoring, and a particle filter (PF), which is theoretically the most accurate nonlinear filter. The algorithm is confirmed numerically using the CUSUM and MM methods. Subsequently, the FDD algorithm is compared with an algorithm from a previous study using a Monte Carlo simulation. Through a comparative analysis of algorithmic performance, this study demonstrates that the current PF-based FDD algorithm outperforms the algorithm based on other nonlinear filters.This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. RS-2022-00164702)

System diagnosis using a bayesian method

Today’s engineering systems have become increasingly more complex. This makes fault diagnosis a more challenging task in industry and therefore a significant amount of research has been undertaken on developing fault diagnostic methodologies. So far there already exist a variety of diagnostic methods, from qualitative to quantitative. However, no methods have considered multi-component degradation when diagnosing faults at the system level. For example, from the point a new aircraft takes off for the first time all of its components start to degrade, and yet in previous studies it is presumed that apart from the faulty component, other components in the system are operating in a healthy state. This thesis makes a contribution through the development of an experimental fuel rig to produce high quality data of multi-component degradation and a probabilistic framework based on the Bayesian method to diagnose faults in a system with considering multi-component degradation. The proposed method is implemented on the fuel rig data which illustrates the applicability of the proposed method and the diagnostic results are compared with the neural network method in order to show the capabilities and imperfections of the proposed method

Exploring Prognostic and Diagnostic Techniques for Jet Engine Health Monitoring: A Review of Degradation Mechanisms and Advanced Prediction Strategies

Maintenance is crucial for aircraft engines because of the demanding conditions to which they are exposed during operation. A proper maintenance plan is essential for ensuring safe flights and prolonging the life of the engines. It also plays a major role in managing costs for aeronautical companies. Various forms of degradation can affect different engine components. To optimize cost management, modern maintenance plans utilize diagnostic and prognostic techniques, such as Engine Health Monitoring (EHM), which assesses the health of the engine based on monitored parameters. In recent years, various EHM systems have been developed utilizing computational techniques. These algorithms are often enhanced by utilizing data reduction and noise filtering tools, which help to minimize computational time and efforts, and to improve performance by reducing noise from sensor data. This paper discusses the various mechanisms that lead to the degradation of aircraft engine components and the impact on engine performance. Additionally, it provides an overview of the most commonly used data reduction and diagnostic and prognostic techniques

Technology for space shuttle main engine control checkout and diagnosis /GP 70-232/

Electronic control for space shuttle main engin

Index to 1981 NASA Tech Briefs, volume 6, numbers 1-4

Short announcements of new technology derived from the R&D activities of NASA are presented. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This index for 1981 Tech Briefs contains abstracts and four indexes: subject, personal author, originating center, and Tech Brief Number. The following areas are covered: electronic components and circuits, electronic systems, physical sciences, materials, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences

A novel framework for enhancing marine dual fuel engines environmental and safety performance via digital twins

The Internet of Things (IoT) advent and digitalisation has enabled the effective application of the digital twins (DT) in various industries, including shipping, with expected benefits on the systems safety, efficiency and environmental footprint. The present research study establishes a novel framework that aims to optimise the marine DF engines performance-emissions trade-offs and enhance their safety, whilst delineating the involved interactions and their effect on the performance and safety. The framework employs a DT, which integrates a thermodynamic engine model along with control function and safety systems modelling. The DT was developed in GT-ISE© environment. Both the gas and diesel operating modes are investigated under steady state and transient conditions. The engine layout is modified to include Exhaust Gas Recirculation (EGR) and Air Bypass (ABP) systems for ensuring compliance with ‘Tier III’ emissions requirements. The optimal DF engine settings as well as the EGR/ABP systems settings for optimal engine efficiency and reduced emissions are identified in both gas and diesel modes, by employing a combination of optimisation techniques including multi-objective genetic algorithms (MOGA) and Design of Experiments (DoE) parametric runs. This study addresses safety by developing an intelligent engine monitoring and advanced faults/failure diagnostics systems, which evaluates the sensors measurements uncertainty. A Failure Mode Effects and Analysis (FMEA) is employed to identify the engine safety critical components, which are used to specify operating scenarios for detailed investigation with the developed DT. The integrated DT is further expanded, by establishing a Faulty Operation Simulator (FOS) to simulate the FMEA scenarios and assess the engine safety implications. Furthermore, an Engine Diagnostics System (EDS) is developed, which offers intelligent engine monitoring, advanced diagnostics and profound corrective actions. This is accomplished by developing and employing a Data-Driven (DD) model based on Neural Networks (NN), along with logic controls, all incorporated in the EDS. Lastly, the manufacturer’s and proposed engine control systems are combined to form an innovative Unified Digital System (UDS), which is also included in the DT. The analysis of marine (DF) engines with the use of an innovative DT, as presented herein, is paving the way towards smart shipping.The Internet of Things (IoT) advent and digitalisation has enabled the effective application of the digital twins (DT) in various industries, including shipping, with expected benefits on the systems safety, efficiency and environmental footprint. The present research study establishes a novel framework that aims to optimise the marine DF engines performance-emissions trade-offs and enhance their safety, whilst delineating the involved interactions and their effect on the performance and safety. The framework employs a DT, which integrates a thermodynamic engine model along with control function and safety systems modelling. The DT was developed in GT-ISE© environment. Both the gas and diesel operating modes are investigated under steady state and transient conditions. The engine layout is modified to include Exhaust Gas Recirculation (EGR) and Air Bypass (ABP) systems for ensuring compliance with ‘Tier III’ emissions requirements. The optimal DF engine settings as well as the EGR/ABP systems settings for optimal engine efficiency and reduced emissions are identified in both gas and diesel modes, by employing a combination of optimisation techniques including multi-objective genetic algorithms (MOGA) and Design of Experiments (DoE) parametric runs. This study addresses safety by developing an intelligent engine monitoring and advanced faults/failure diagnostics systems, which evaluates the sensors measurements uncertainty. A Failure Mode Effects and Analysis (FMEA) is employed to identify the engine safety critical components, which are used to specify operating scenarios for detailed investigation with the developed DT. The integrated DT is further expanded, by establishing a Faulty Operation Simulator (FOS) to simulate the FMEA scenarios and assess the engine safety implications. Furthermore, an Engine Diagnostics System (EDS) is developed, which offers intelligent engine monitoring, advanced diagnostics and profound corrective actions. This is accomplished by developing and employing a Data-Driven (DD) model based on Neural Networks (NN), along with logic controls, all incorporated in the EDS. Lastly, the manufacturer’s and proposed engine control systems are combined to form an innovative Unified Digital System (UDS), which is also included in the DT. The analysis of marine (DF) engines with the use of an innovative DT, as presented herein, is paving the way towards smart shipping

Fault diagnostics for advanced cycle marine gas turbine using genetic algorithm

The major challenges faced by the gas turbine industry, for both the users and the manufacturers, is the reduction in life cycle costs , as well as the safe and efficient running of gas turbines. In view of the above, it would be advantageous to have a diagnostics system capable of reliably detecting component faults (even though limited to gas path components) in a quantitative marmer. V This thesis presents the development an integrated fault diagnostics model for identifying shifts in component performance and sensor faults using advanced concepts in genetic algorithm. The diagnostics model operates in three distinct stages. The rst stage uses response surfaces for computing objective functions to increase the exploration potential of the search space while easing the computational burden. The second stage uses the heuristics modification of genetics algorithm parameters through a master-slave type configuration. The third stage uses the elitist model concept in genetic algorithm to preserve the accuracy of the solution in the face of randomness. The above fault diagnostics model has been integrated with a nested neural network to form a hybrid diagnostics model. The nested neural network is employed as a pre- processor or lter to reduce the number of fault classes to be explored by the genetic algorithm based diagnostics model. The hybrid model improves the accuracy, reliability and consistency of the results obtained. In addition signicant improvements in the total run time have also been observed. The advanced cycle Intercooled Recuperated WR2l engine has been used as the test engine for implementing the diagnostics model.SOE Prize winne

Extreme Learning Machine-Based Diagnostics for Component Degradation in a Microturbine

Micro turbojets are used for propelling radio-controlled aircraft, aerial targets, and personal air vehicles. When compared to full-scale engines, they are characterized by relatively low efficiency and durability. In this context, the degraded performance of gas path components could lead to an unacceptable reduction in the overall engine performance. In this work, a data-driven model based on a conventional artificial neural network (ANN) and an extreme learning machine (ELM) was used for estimating the performance degradation of the micro turbojet. The training datasets containing the performance data of the engine with degraded components were generated using the validated GSP model and the Monte Carlo approach. In particular, compressor and turbine performance degradation were simulated for three different flight regimes. It was confirmed that component degradation had a similar impact in flight than at sea level. Finally, the datasets were used in the training and testing process of the ELM algorithm with four different input vectors. Two vectors had an extensive number of virtual sensors, and the other two were reduced to just fuel flow and exhaust gas temperature. Even with the small number of sensors, the high prediction accuracy of ELM was maintained for takeoff and cruise but was slightly worse for variable flight conditions

Real-time fault identification for developmental turbine engine testing

Hundreds of individual sensors produce an enormous amount of data during developmental turbine engine testing. The challenge is to ensure the validity of the data and to identify data and engine anomalies in a timely manner. An automated data validation, engine condition monitoring, and fault identification process that emulates typical engineering techniques has been developed for developmental engine testing.An automated data validation and fault identification approach employing enginecycle-matching principles is described. Engine cycle-matching is automated by using an adaptive nonlinear component-level computer model capable of simulating both steady state and transient engine operation. Automated steady-state, transient, and real-time model calibration processes are also described. The model enables automation of traditional data validation, engine condition monitoring, and fault identification procedures. A distributed parallel computing approach enables the entire process to operate in real-time.The result is a capability to detect data and engine anomalies in real-time during developmental engine testing. The approach is shown to be successful in detecting and identifying sensor anomalies as they occur and distinguishing these anomalies from variations in component and overall engine aerothermodynamic performance. The component-level model-based engine performance and fault identification technique of the present research is capable of: identifying measurement errors on the order of 0.5 percent (e.g., sensor bias, drift,level shift, noise, or poor response) in facility fuel flow, airflow, and thrust measurements; identifying measurement errors in engine aerothermodynamic measurements (rotorspeeds, gas path pressures and temperatures); identifying measurement errors in engine control sensors (e.g., leaking/biased pressure sensor, slowly responding pressure measurement) and variable geometry rigging (e.g., misset guide vanes or nozzle area) that would invalidate a test or series of tests; identifying abrupt faults (e.g., faults due to domestic object damage, foreign object damage, and control anomalies); identifying slow faults (e.g., component or overall engine degradation, and sensor drift). Specifically, the technique is capable of identifying small changes in compressor (or fan) performance on the order of 0.5 percent; and being easily extended to diagnose secondary failure modes and to verify any modeling assumptions that may arise for developmental engine tests (e.g., increase in turbine flow capacity, inaccurate measurement of facility bleed flows, horsepower extraction, etc.).The component-level model-based engine performance and fault identification method developed in the present work brings together features which individually and collectively advance the state-of-the-art. These features are separated into three categories: advancements to effectively quantify off-nominal behavior, advancements to provide a fault detection capability that is practical from the viewpoint of the analysis,implementation, tuning, and design, and advancements to provide a real-time fault detection capability that is reliable and efficient