Neural network interpolation of the magnetic field for the LISA Pathfinder Diagnostics Subsystem

LISA Pathfinder is a science and technology demonstrator of the European Space Agency within the framework of its LISA mission, which aims to be the first space-borne gravitational wave observatory. The payload of LISA Pathfinder is the so-called LISA Technology Package, which is designed to measure relative accelerations between two test masses in nominal free fall. Its disturbances are monitored and dealt by the diagnostics subsystem. This subsystem consists of several modules, and one of these is the magnetic diagnostics system, which includes a set of four tri-axial fluxgate magnetometers, intended to measure with high precision the magnetic field at the positions of the test masses. However, since the magnetometers are located far from the positions of the test masses, the magnetic field at their positions must be interpolated. It has been recently shown that because there are not enough magnetic channels, classical interpolation methods fail to derive reliable measurements at the positions of the test masses, while neural network interpolation can provide the required measurements at the desired accuracy. In this paper we expand these studies and we assess the reliability and robustness of the neural network interpolation scheme for variations of the locations and possible offsets of the magnetometers, as well as for changes in environmental conditions. We find that neural networks are robust enough to derive accurate measurements of the magnetic field at the positions of the test masses in most circumstances

Consciosusness in Cognitive Architectures. A Principled Analysis of RCS, Soar and ACT-R

This report analyses the aplicability of the principles of consciousness developed in the ASys project to three of the most relevant cognitive architectures. This is done in relation to their aplicability to build integrated control systems and studying their support for general mechanisms of real-time consciousness.\ud To analyse these architectures the ASys Framework is employed. This is a conceptual framework based on an extension for cognitive autonomous systems of the General Systems Theory (GST).\ud A general qualitative evaluation criteria for cognitive architectures is established based upon: a) requirements for a cognitive architecture, b) the theoretical framework based on the GST and c) core design principles for integrated cognitive conscious control systems

Bayesian Network Approach to Assessing System Reliability for Improving System Design and Optimizing System Maintenance

abstract: A quantitative analysis of a system that has a complex reliability structure always involves considerable challenges. This dissertation mainly addresses uncertainty in- herent in complicated reliability structures that may cause unexpected and undesired results. The reliability structure uncertainty cannot be handled by the traditional relia- bility analysis tools such as Fault Tree and Reliability Block Diagram due to their deterministic Boolean logic. Therefore, I employ Bayesian network that provides a flexible modeling method for building a multivariate distribution. By representing a system reliability structure as a joint distribution, the uncertainty and correlations existing between system’s elements can effectively be modeled in a probabilistic man- ner. This dissertation focuses on analyzing system reliability for the entire system life cycle, particularly, production stage and early design stages. In production stage, the research investigates a system that is continuously mon- itored by on-board sensors. With modeling the complex reliability structure by Bayesian network integrated with various stochastic processes, I propose several methodologies that evaluate system reliability on real-time basis and optimize main- tenance schedules. In early design stages, the research aims to predict system reliability based on the current system design and to improve the design if necessary. The three main challenges in this research are: 1) the lack of field failure data, 2) the complex reliability structure and 3) how to effectively improve the design. To tackle the difficulties, I present several modeling approaches using Bayesian inference and nonparametric Bayesian network where the system is explicitly analyzed through the sensitivity analysis. In addition, this modeling approach is enhanced by incorporating a temporal dimension. However, the nonparametric Bayesian network approach generally accompanies with high computational efforts, especially, when a complex and large system is modeled. To alleviate this computational burden, I also suggest to building a surrogate model with quantile regression. In summary, this dissertation studies and explores the use of Bayesian network in analyzing complex systems. All proposed methodologies are demonstrated by case studies.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201

New model framework and structure and the commonality evaluation model

The development of a framework and structure for shuttle era unmanned spacecraft projects and the development of a commonality evaluation model is documented. The methodology developed for model utilization in performing cost trades and comparative evaluations for commonality studies is discussed. The model framework consists of categories of activities associated with the spacecraft system's development process. The model structure describes the physical elements to be treated as separate identifiable entities. Cost estimating relationships for subsystem and program-level components were calculated

Distribution-free stochastic simulation methodology for model updating under hybrid uncertainties

In the real world, a significant challenge faced in the safe operation and maintenance of infrastructures is the lack of available information or data. This results in a large degree of uncertainty and the requirement for robust and efficient uncertainty quantification (UQ) tools in order to derive the most realistic estimates of the behavior of structures. While the probabilistic approach has long been utilized as an essential tool for the quantitative mathematical representation of uncertainty, a common criticism is that the approach often involves insubstantiated subjective assumptions because of the scarcity or imprecision of available information. To avoid the inclusion of subjectivity, the concepts of imprecise probabilities have been developed, and the distributional probability-box (p-box) has gained the most attention among various types of imprecise probability models since it can straightforwardly provide a clear separation between aleatory and epistemic uncertainty. This thesis concerns the realistic consideration and numerically efficient calibraiton and propagation of aleatory and epistemic uncertainties (hybrid uncertainties) based on the distributional p-box. The recent developments including the Bhattacharyya distance-based approximate Bayesian computation (ABC) and non-intrusive imprecise stochastic simulation (NISS) methods have strengthened the subjective assumption-free approach for uncertainty calibration and propagation. However, these methods based on the distributional p-box stand on the availability of the prior knowledge determining a specific distribution family for the p-box. The target of this thesis is hence to develop a distribution-free approach for the calibraiton and propagation of hybrid uncertainties, strengthening the subjective assumption-free UQ approach. To achieve the above target, this thesis presents five main developments to improve the Bhattacharyya distance-based ABC and NISS frameworks. The first development is on improving the scope of application and efficiency of the Bhattacharyya distance-based ABC. The dimension reduction procedure is proposed to evaluate the Bhattacharyya distance when the system under investigation is described by time-domain sequences. Moreover, the efficient Bayesian inference method within the Bayesian updating with structural reliability methods (BUS) framework is developed by combining BUS with the adaptive Kriging-based reliability method, namely AK-MCMC. The second development of the distribution-free stochastic model updating framework is based on the combined application of the staircase density functions and the Bhattacharyya distance. The staircase density functions can approximate a wide range of distributions arbitrarily close; hence the development achieved to perform the Bhattacharyya distance-based ABC without limiting hypotheses on the distribution families of the parameters having to be updated. The aforementioned two developments are then integrated in the third development to provide a solution to the latest edition (2019) of the NASA UQ challenge problem. The model updating tasks under very challenging condition, where prior information of aleatory parameters are extremely limited other than a common boundary, are successfully addressed based on the above distribution-free stochastic model updating framework. Moreover, the NISS approach that simplifies the high-dimensional optimization to a set of one-dimensional searching by a first-order high-dimensional model representation (HDMR) decomposition with respect to each design parameter is developed to efficiently solve the reliability-based design optimization tasks. This challenge, at the same time, elucidates the limitations of the current developments, hence the fourth development aims at addressing the limitation that the staircase density functions are designed for univariate random variables and cannot acount for the parameter dependencies. In order to calibrate the joint distribution of correlated parameters, the distribution-free stochastic model updating framework is extended by characterizing the aleatory parameters using the Gaussian copula functions having marginal distributions as the staircase density functions. This further strengthens the assumption-free approach for uncertainty calibration in which no prior information of the parameter dependencies is required. Finally, the fifth development of the distribution-free uncertainty propagation framework is based on another application of the staircase density functions to the NISS class of methods, and it is applied for efficiently solving the reliability analysis subproblem of the NASA UQ challenge 2019. The above five developments have successfully strengthened the assumption-free approach for both uncertainty calibration and propagation thanks to the nature of the staircase density functions approximating arbitrary distributions. The efficiency and effectiveness of those developments are sufficiently demonstrated upon the real-world applications including the NASA UQ challenge 2019

A RISK-INFORMED DECISION-MAKING METHODOLOGY TO IMPROVE LIQUID ROCKET ENGINE PROGRAM TRADEOFFS

This work provides a risk-informed decision-making methodology to improve liquid rocket engine program tradeoffs with the conflicting areas of concern affordability, reliability, and initial operational capability (IOC) by taking into account psychological and economic theories in combination with reliability engineering. Technical program risks are associated with the number of predicted failures of the test-analyze-and-fix (TAAF) cycle that is based on the maturity of the engine components. Financial and schedule program risks are associated with the epistemic uncertainty of the models that determine the measures of effectiveness in the three areas of concern. The affordability and IOC models' inputs reflect non-technical and technical factors such as team experience, design scope, technology readiness level, and manufacturing readiness level. The reliability model introduces the Reliability- As-an-Independent-Variable (RAIV) strategy that aggregates fictitious or actual hotfire tests of testing profiles that differ from the actual mission profile to estimate the system reliability. The main RAIV strategy inputs are the physical or functional architecture of the system, the principal test plan strategy, a stated reliability-bycredibility requirement, and the failure mechanisms that define the reliable life of the system components. The results of the RAIV strategy, which are the number of hardware sets and number of hot-fire tests, are used as inputs to the affordability and the IOC models. Satisficing within each tradeoff is attained by maximizing the weighted sum of the normalized areas of concern subject to constraints that are based on the decision-maker's targets and uncertainty about the affordability, reliability, and IOC using genetic algorithms. In the planning stage of an engine program, the decision variables of the genetic algorithm correspond to fictitious hot-fire tests that include TAAF cycle failures. In the program execution stage, the RAIV strategy is used as reliability growth planning, tracking, and projection model. The main contributions of this work are the development of a comprehensible and consistent risk-informed tradeoff framework, the RAIV strategy that links affordability and reliability, a strategy to define an industry or government standard or guideline for liquid rocket engine hot-fire test plans, and an alternative to the U.S. Crow/AMSAA reliability growth model applying the RAIV strategy

A study of low cost approaches to scientific experiment implementation for shuttle launched and serviced automated spacecraft

Cost reductions that can be obtained in experiment instrumentation by the use of standardized electronics and by the relaxation of instrument reliability requirements are studied. The feasibility of using standardized equipment for experiment instrumentation is assessed and a system design approach that most effectively incorporates standardized equipment is developed. The level and form of modularization that is appropriate for the standardized equipment is determined. Mission assurance aspects of instrument development are examined to determine the cost reductions that might be derived from the relaxation of reliability requirements and to formulate a systematic approach to the optimization of mission assurance cost reductions. The results of the analyses are applied to a representative model HEAO payload in order to provide a concrete example of the cost reductions that can be achieved by a standardized approach to the instrument electronics

Structural Identification Through Monitoring, Modeling And Predictive Analysis Under Uncertainty

Bridges are critical components of highway networks, which provide mobility and economical vitality to a nation. Ensuring the safety and regular operation as well as accurate structural assessment of bridges is essential. Structural Identification (St-Id) can be utilized for better assessment of structures by integrating experimental and analytical technologies in support of decision-making. St-Id is defined as creating parametric or nonparametric models to characterize structural behavior based on structural health monitoring (SHM) data. In a recent study by the ASCE St-Id Committee, St-Id framework is given in six steps, including modeling, experimentation and ultimately decision making for estimating the performance and vulnerability of structural systems reliably through the improved simulations using monitoring data. In some St-Id applications, there can be challenges and considerations related to this six-step framework. For instance not all of the steps can be employed; thereby a subset of the six steps can be adapted for some cases based on the various limitations. In addition, each step has its own characteristics, challenges, and uncertainties due to the considerations such as time varying nature of civil structures, modeling and measurements. It is often discussed that even a calibrated model has limitations in fully representing an existing structure; therefore, a family of models may be well suited to represent the structure’s response and performance in a probabilistic manner. The principle objective of this dissertation is to investigate nonparametric and parametric St-Id approaches by considering uncertainties coming from different sources to better assess the structural condition for decision making. In the first part of the dissertation, a nonparametric StId approach is employed without the use of an analytical model. The new methodology, which is iv successfully demonstrated on both lab and real-life structures, can identify and locate the damage by tracking correlation coefficients between strain time histories and can locate the damage from the generated correlation matrices of different strain time histories. This methodology is found to be load independent, computationally efficient, easy to use, especially for handling large amounts of monitoring data, and capable of identifying the effectiveness of the maintenance. In the second part, a parametric St-Id approach is introduced by developing a family of models using Monte Carlo simulations and finite element analyses to explore the uncertainty effects on performance predictions in terms of load rating and structural reliability. The family of models is developed from a parent model, which is calibrated using monitoring data. In this dissertation, the calibration is carried out using artificial neural networks (ANNs) and the approach and results are demonstrated on a laboratory structure and a real-life movable bridge, where predictive analyses are carried out for performance decrease due to deterioration, damage, and traffic increase over time. In addition, a long-span bridge is investigated using the same approach when the bridge is retrofitted. The family of models for these structures is employed to determine the component and system reliability, as well as the load rating, with a distribution that incorporates various uncertainties that were defined and characterized. It is observed that the uncertainties play a considerable role even when compared to calibrated model-based predictions for reliability and load rating, especially when the structure is complex, deteriorated and aged, and subjected to variable environmental and operational conditions. It is recommended that a family-of-models approach is suitable for structures that have less redundancy, high operational importance, are deteriorated, and are performing under close capacity and demand level

Human factors information requirements for space system development Final report

Human factors information requirements for space system developmen

Fault detection and diagnosis in HVAC systems using analytical models

Faults that develop in the heat exchanger subsystems in air-conditioning installations can lead to increased energy costs and jeopardise thermal comfort. The sensor and control signals associated with these systems contain potentially valuable information about the condition of the system, and energy management and control systems are able to monitor and store these signals. In practice, the only checks made are to verify set-points are being maintained and that certain critical variables remain within predetermined limits. This approach may allow the detection of certain abrupt or catastrophic faults, but degradation faults often remain undetected until their effects become quite severe. This thesis investigates the appropriateness of using mathematical models to track the development of degradation faults. An approach is developed, which is based on the use of analytical models in conjunction with a recursive parameter estimation algorithm. A subset of the parameters of the models, which are closely related to faults, is estimated recursively. Significant deviations in the values of the estimated parameters from nominal values, which represent `correct operation', are used as an indication that the system has developed a fault. The extent of the deviation from the nominal values is used as an estimate of the degree of fault. This thesis develops the theory and examines the robustness of the parameter estimator using simulation-based testing. Results are also presented from testing the fault detection and diagnosis scheme with data obtained from a simulated air-conditioning system and from a full size test installation