Analysis of Artificial Intelligence based diagnostic methods for satellites

The growing utilization of small satellites in various applications has emphasized the need for reliable diagnostic methods to ensure their optimal performance and longevity. This master thesis focuses on the analysis of artificial intelligence-based diagnostic methods for these particular space assets. This work firstly explores the main characteristics and applications of small satellites, highlighting the critical subsystems and components that play a vital role in their proper functioning. The key components of this study revolve around Diagnosis, Prognosis, and Health Monitoring (DPHM) systems and techniques for small satellites. The DPHM systems aim at monitoring the health status of the satellite, detecting anomalies and predicting future system behavior. The reason why advanced DPHM systems are of interest for the space operators is the fact that they mitigate the risk of satellites catastrophic failures that may lead to service interruptions or mission abort. To achieve these objectives, a hybrid architecture combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks is proposed. This architecture leverages the strengths of CNNs in feature extraction and LSTM networks in capturing temporal dependencies. The integration of these two neural network architectures enhances the diagnostic capabilities and enables accurate predictions for small satellite systems. Real data collected from an operational satellite is utilized to validate and test the proposed CNN-LSTM hybrid architecture. Based on the experimental results obtained, advantages and drawbacks of the exploitation of this architecture are discussed.The growing utilization of small satellites in various applications has emphasized the need for reliable diagnostic methods to ensure their optimal performance and longevity. This master thesis focuses on the analysis of artificial intelligence-based diagnostic methods for these particular space assets. This work firstly explores the main characteristics and applications of small satellites, highlighting the critical subsystems and components that play a vital role in their proper functioning. The key components of this study revolve around Diagnosis, Prognosis, and Health Monitoring (DPHM) systems and techniques for small satellites. The DPHM systems aim at monitoring the health status of the satellite, detecting anomalies and predicting future system behavior. The reason why advanced DPHM systems are of interest for the space operators is the fact that they mitigate the risk of satellites catastrophic failures that may lead to service interruptions or mission abort. To achieve these objectives, a hybrid architecture combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks is proposed. This architecture leverages the strengths of CNNs in feature extraction and LSTM networks in capturing temporal dependencies. The integration of these two neural network architectures enhances the diagnostic capabilities and enables accurate predictions for small satellite systems. Real data collected from an operational satellite is utilized to validate and test the proposed CNN-LSTM hybrid architecture. Based on the experimental results obtained, advantages and drawbacks of the exploitation of this architecture are discussed

A Data-driven Fault Isolation and Identification Scheme for Multiple In-Phase Faults in Satellite Control Moment Gyros

A satellite can only complete its mission successfully when all its subsystems, including the attitude control subsystem, are in healthy condition and work properly. Control moment gyroscope is a type of actuator used in the attitude control subsystems of satellites. Any fault in the control moment gyroscope can cause the satellite mission failure if it is not detected, isolated and resolved in-time. Fault isolation provides an opportunity to detect and isolate the occurring faults and, if accompanied by proactive remedial actions, can avoid failure and improve the satellite reliability. It is also necessary to know the fault severity for better maintenance planning and prioritize the corrective actions. This way, the more severe faults can be corrected first. In this work, an enhanced data-driven fault diagnosis scheme is introduced for fault isolation and identification of multiple in-phase faults of satellite control moment gyroscopes that is not addressed in the literature before with high accuracy. The proposed method is based on an optimized support vector machine and an optimized support vector regressor. The results yield fault predictions with up to 95.6% accuracy for isolation and 94.9% accuracy for identification, on average. In addition, a sensitivity analysis with regards to noise, missing values, and missing sensors is done where the results show that the proposed model is robust enough to be used in real applications

A Data-Driven Approach using Long-Short Term Memory for Fault Prognosis and Remaining Useful Life Estimation of Satellite Reaction Wheel

Artificial satellites are objects or a body that are stationed in the orbit of another object. The purpose of artificial satellites includes monitoring, information transfer, studying a different planet, space exploration, and fulfilling many other modern-day needs. For the increased demand, the number of artificial satellites revolving around the earth is also increasing. Due to cost efficiency, bulk manufacturing capability, and ease to launch in the orbits, small satellites are the topic of interest. Reaction wheels are widely used in the attitude control system of small satellites. Unfortunately, reaction wheels failure restricts the efficacy of a satellite, and it is one of the many reasons that lead to premature abandonment of the satellites. In larger satellites, there is room for mechanical redundancy to increase service reliability, so an onboard health monitoring system is in demand to ensure seamless performance by minimizing the risk factor of the sudden failure of a small satellite. This study observes the measurable system parameter of a faulty reaction wheel to estimate the remaining useful life of the reaction wheels. In this research, a data-driven approach is for the fault prognosis of the satellite reaction wheel. The measurable system parameters from the satellite reaction wheel are not directly related to the health of the system. So, the proposed method involves three stages to achieve the goal. In the first stage, the necessary observable system parameters are identified, and their future state is predicted based on historical data using a long short-term memory recurrent neural network. A health index parameter is defined and estimated using a multi-variate long short-term memory network in the second stage. In the third stage, the remaining useful life of the reaction wheel is estimated based on historical data of the health index parameter and a threshold. The approach is very efficient depending on the fault severity and can be used in on-field scenarios. The approach is robust up to a certain degree of noise, disturbance, and missing data

The achievement of spacecraft autonomy through the thematic application of multiple cooperating intelligent agents

A description is given of UNICORN, a prototype system developed for the purpose of investigating artificial intelligence (AI) concepts supporting spacecraft autonomy. UNICORN employs thematic reasoning, of the type first described by Rodger Schank of Northwestern University, to allow the context-sensitive control of multiple intelligent agents within a blackboard based environment. In its domain of application, UNICORN demonstrates the ability to reason teleologically with focused knowledge. Also presented are some of the lessons learned as a result of this effort. These lessons apply to any effort wherein system level autonomy is the objective

Fault detection, isolation, and identification for nonlinear systems using a hybrid approach

This thesis presents a novel integrated hybrid approach for fault diagnosis (FD) of nonlinear systems; taking advantage of both system's mathematical model and the adaptive nonlinear approximation capability of computational intelligence techniques. Unlike most FD techniques, the proposed solution simultaneously accomplishes fault detection, isolation, and identification (FDII) within a unified diagnostic module. At the core of this solution are a bank of adaptive neural parameter estimators (NPE) and a set of single-parameterized fault models. The NPEs continuously estimate unknown fault parameters (FP) that are indicators of faults in the system. In view of the availability of full-state measurements, two NPE structures, namely series-parallel and parallel, are developed with their exclusive set of desirable attributes. The parallel scheme is extremely robust to measurement noise and possesses a simpler, yet more solid, fault isolation logic. On the contrary, the series-parallel scheme displays short FD delays and is robust to closed-loop system transients due to changes in control commands. Simple neural network architecture and update laws make both schemes suitable for real-time implementations. A fault tolerant observer (FTO) is then designed to extend the FDII schemes to systems with partial-state measurement. The proposed FTO is a neural state estimator that can estimate unmeasured states even in presence of faults. The estimated and the measured states then comprise the inputs to the FDII schemes. Simulation results for FDII of reaction wheels of a 3-axis stabilized satellite in presence of disturbances and noise demonstrate the effectiveness of the proposed FDII solution under both full and partial-state measurements

Health Monitoring in Small Satellite Design

Presentation/lecture on systems health monitoring (diagnostics, prognostics, decision-making) with applications to the design phase of small satellite components and systems

Fault Diagnosis of Lubrication Decay in Reaction Wheels Using Temperature Estimation and Forecasting via Enhanced Adaptive Particle Filter

Reaction wheel (RW), the most common Attitude Control Systems (ACS) in satellites, are highly prone to failure. A satellite needs to be oriented in a particular direction to maneuver and accomplish its mission goals; losing the RW can lead to a complete or partial mission failure. Therefore, estimating the remaining useful life (RUL) in long and short spans can be extremely valuable. The short-period prediction allows the satellite\u27s operator to manage and prioritize mission tasks based on the RUL and increases the chances of a total mission failure becoming a partial one. Studies show that lack of proper bearing lubrication and uneven frictional torque distribution, which lead to variation in motor torque, are the leading causes of failure in RWs. Hence, this study aims to develop a three-step prognostic method for longterm RUL estimation of RWs based on the remaining lubricant for the bearing unit and potential fault in the supplementary lubrication system. In the first step of this method, the temperature of the lubricants is estimated as the non-measurable state of the system, using a proposed Adaptive particle filter (APF) with an-gular velocity and motor current of RW as the available measurements. In the second step, the estimated lubricant\u27s temperature and amount of injected lubrication in the bearing alongside the lubrication degradation model are fed to a two-step Particle Filter (PF) for online model parameter estimation. In the last step, the performance of the proposed prognostics method is evaluated by predicting the RW\u27s RUL under two fault scenarios, including excessive loss of lubrication and insufficient injection of lubrication. The results show promising performance for the proposed scheme with accuracy in estimation of degradation model\u27s parameters around 2–3% of root mean squared percentage error (RMSPE) and prediction of RUL around 0.1- 4% percentage error

Engineering Resilient Space Systems

Several distinct trends will influence space exploration missions in the next decade. Destinations are becoming more remote and mysterious, science questions more sophisticated, and, as mission experience accumulates, the most accessible targets are visited, advancing the knowledge frontier to more difficult, harsh, and inaccessible environments. This leads to new challenges including: hazardous conditions that limit mission lifetime, such as high radiation levels surrounding interesting destinations like Europa or toxic atmospheres of planetary bodies like Venus; unconstrained environments with navigation hazards, such as free-floating active small bodies; multielement missions required to answer more sophisticated questions, such as Mars Sample Return (MSR); and long-range missions, such as Kuiper belt exploration, that must survive equipment failures over the span of decades. These missions will need to be successful without a priori knowledge of the most efficient data collection techniques for optimum science return. Science objectives will have to be revised ‘on the fly’, with new data collection and navigation decisions on short timescales. Yet, even as science objectives are becoming more ambitious, several critical resources remain unchanged. Since physics imposes insurmountable light-time delays, anticipated improvements to the Deep Space Network (DSN) will only marginally improve the bandwidth and communications cadence to remote spacecraft. Fiscal resources are increasingly limited, resulting in fewer flagship missions, smaller spacecraft, and less subsystem redundancy. As missions visit more distant and formidable locations, the job of the operations team becomes more challenging, seemingly inconsistent with the trend of shrinking mission budgets for operations support. How can we continue to explore challenging new locations without increasing risk or system complexity? These challenges are present, to some degree, for the entire Decadal Survey mission portfolio, as documented in Vision and Voyages for Planetary Science in the Decade 2013–2022 (National Research Council, 2011), but are especially acute for the following mission examples, identified in our recently completed KISS Engineering Resilient Space Systems (ERSS) study: 1. A Venus lander, designed to sample the atmosphere and surface of Venus, would have to perform science operations as components and subsystems degrade and fail; 2. A Trojan asteroid tour spacecraft would spend significant time cruising to its ultimate destination (essentially hibernating to save on operations costs), then upon arrival, would have to act as its own surveyor, finding new objects and targets of opportunity as it approaches each asteroid, requiring response on short notice; and 3. A MSR campaign would not only be required to perform fast reconnaissance over long distances on the surface of Mars, interact with an unknown physical surface, and handle degradations and faults, but would also contain multiple components (launch vehicle, cruise stage, entry and landing vehicle, surface rover, ascent vehicle, orbiting cache, and Earth return vehicle) that dramatically increase the need for resilience to failure across the complex system. The concept of resilience and its relevance and application in various domains was a focus during the study, with several definitions of resilience proposed and discussed. While there was substantial variation in the specifics, there was a common conceptual core that emerged—adaptation in the presence of changing circumstances. These changes were couched in various ways—anomalies, disruptions, discoveries—but they all ultimately had to do with changes in underlying assumptions. Invalid assumptions, whether due to unexpected changes in the environment, or an inadequate understanding of interactions within the system, may cause unexpected or unintended system behavior. A system is resilient if it continues to perform the intended functions in the presence of invalid assumptions. Our study focused on areas of resilience that we felt needed additional exploration and integration, namely system and software architectures and capabilities, and autonomy technologies. (While also an important consideration, resilience in hardware is being addressed in multiple other venues, including 2 other KISS studies.) The study consisted of two workshops, separated by a seven-month focused study period. The first workshop (Workshop #1) explored the ‘problem space’ as an organizing theme, and the second workshop (Workshop #2) explored the ‘solution space’. In each workshop, focused discussions and exercises were interspersed with presentations from participants and invited speakers. The study period between the two workshops was organized as part of the synthesis activity during the first workshop. The study participants, after spending the initial days of the first workshop discussing the nature of resilience and its impact on future science missions, decided to split into three focus groups, each with a particular thrust, to explore specific ideas further and develop material needed for the second workshop. The three focus groups and areas of exploration were: 1. Reference missions: address/refine the resilience needs by exploring a set of reference missions 2. Capability survey: collect, document, and assess current efforts to develop capabilities and technology that could be used to address the documented needs, both inside and outside NASA 3. Architecture: analyze the impact of architecture on system resilience, and provide principles and guidance for architecting greater resilience in our future systems The key product of the second workshop was a set of capability roadmaps pertaining to the three reference missions selected for their representative coverage of the types of space missions envisioned for the future. From these three roadmaps, we have extracted several common capability patterns that would be appropriate targets for near-term technical development: one focused on graceful degradation of system functionality, a second focused on data understanding for science and engineering applications, and a third focused on hazard avoidance and environmental uncertainty. Continuing work is extending these roadmaps to identify candidate enablers of the capabilities from the following three categories: architecture solutions, technology solutions, and process solutions. The KISS study allowed a collection of diverse and engaged engineers, researchers, and scientists to think deeply about the theory, approaches, and technical issues involved in developing and applying resilience capabilities. The conclusions summarize the varied and disparate discussions that occurred during the study, and include new insights about the nature of the challenge and potential solutions: 1. There is a clear and definitive need for more resilient space systems. During our study period, the key scientists/engineers we engaged to understand potential future missions confirmed the scientific and risk reduction value of greater resilience in the systems used to perform these missions. 2. Resilience can be quantified in measurable terms—project cost, mission risk, and quality of science return. In order to consider resilience properly in the set of engineering trades performed during the design, integration, and operation of space systems, the benefits and costs of resilience need to be quantified. We believe, based on the work done during the study, that appropriate metrics to measure resilience must relate to risk, cost, and science quality/opportunity. Additional work is required to explicitly tie design decisions to these first-order concerns. 3. There are many existing basic technologies that can be applied to engineering resilient space systems. Through the discussions during the study, we found many varied approaches and research that address the various facets of resilience, some within NASA, and many more beyond. Examples from civil architecture, Department of Defense (DoD) / Defense Advanced Research Projects Agency (DARPA) initiatives, ‘smart’ power grid control, cyber-physical systems, software architecture, and application of formal verification methods for software were identified and discussed. The variety and scope of related efforts is encouraging and presents many opportunities for collaboration and development, and we expect many collaborative proposals and joint research as a result of the study. 4. Use of principled architectural approaches is key to managing complexity and integrating disparate technologies. The main challenge inherent in considering highly resilient space systems is that the increase in capability can result in an increase in complexity with all of the 3 risks and costs associated with more complex systems. What is needed is a better way of conceiving space systems that enables incorporation of capabilities without increasing complexity. We believe principled architecting approaches provide the needed means to convey a unified understanding of the system to primary stakeholders, thereby controlling complexity in the conception and development of resilient systems, and enabling the integration of disparate approaches and technologies. A representative architectural example is included in Appendix F. 5. Developing trusted resilience capabilities will require a diverse yet strategically directed research program. Despite the interest in, and benefits of, deploying resilience space systems, to date, there has been a notable lack of meaningful demonstrated progress in systems capable of working in hazardous uncertain situations. The roadmaps completed during the study, and documented in this report, provide the basis for a real funded plan that considers the required fundamental work and evolution of needed capabilities. Exploring space is a challenging and difficult endeavor. Future space missions will require more resilience in order to perform the desired science in new environments under constraints of development and operations cost, acceptable risk, and communications delays. Development of space systems with resilient capabilities has the potential to expand the limits of possibility, revolutionizing space science by enabling as yet unforeseen missions and breakthrough science observations. Our KISS study provided an essential venue for the consideration of these challenges and goals. Additional work and future steps are needed to realize the potential of resilient systems—this study provided the necessary catalyst to begin this process

A Framework for Diagnosis of Critical Faults in Unmanned Aerial Vehicles

Unmanned Aerial Vehicles (UAVs) need a large degree of tolerance towards faults. If not diagnosed and handled in time, many types of faults can have catastrophic consequences if they occur during flight. Prognosis of faults is also valuable and so is the ability to distinguish the severity of the different faults in terms of both consequences and the frequency with which they appear. In this paper flight data from a fleet of UAVs is analysed with respect to certain faults and their frequency of appearance. Data is taken from a group of UAV's of the same type but with small differences in weight and handling due to different types of payloads and engines used. Categories of critical faults, that could and have caused UAV crashes are analysed and requirements to diagnosis are formulated. Faults in air system sensors and in control surfaces are given special attention. In a stochastic framework, and based on a large number of data logged during flights, diagnostic methods are employed to diagnose faults and the performance of these fault detectors are evaluated against flight data. The paper demonstrates a significant potential for reducing the risk of unplanned loss of remotely piloted vehicles used by the Danish Navy for target practice.This is the authors' accepted and refereed manuscript to the article. Author's post-print must be released with a Creative Commons Attribution Non-Commercial No Derivatives License

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 184

This bibliography lists 139 reports, articles, and other documents introduced into the NASA scientific and technical information system in August 1978