Developing Methods of Obtaining Quality Failure Information from Complex Systems

The complexity in most engineering systems is constantly growing due to ever-increasing technological advancements. This result in a corresponding need for methods that adequately account for the reliability of such systems based on failure information from components that make up these systems. This dissertation presents an approach to validating qualitative function failure results from model abstraction details. The impact of the level of detail available to a system designer during conceptual stages of design is considered for failure space exploration in a complex system. Specifically, the study develops an efficient approach towards detailed function and behavior modeling required for complex system analyses. In addition, a comprehensive research and documentation of existing function failure analysis methodologies is also synthesized into identified structural groupings. Using simulations, known governing equations are evaluated for components and system models to study responses to faults by accounting for detailed failure scenarios, component behaviors, fault propagation paths, and overall system performance. The components were simulated at nominal states and varying degrees of fault representing actual modes of operation. Information on product design and provisions on expected working conditions of components were used in the simulations to address normally overlooked areas during installation. The results of system model simulations were investigated using clustering analysis to develop an efficient grouping method and measure of confidence for the obtained results. The intellectual merit of this work is the use of a simulation based approach in studying how generated failure scenarios reveal component fault interactions leading to a better understanding of fault propagation within design models. The information from using varying fidelity models for system analysis help in identifying models that are sufficient enough at the conceptual design stages to highlight potential faults. This will reduce resources such as cost, manpower and time spent during system design. A broader impact of the project is to help design engineers identifying critical components, quantifying risks associated with using particular components in their prototypes early in the design process and help improving fault tolerant system designs. This research looks to eventually establishing a baseline for validating and comparing theories of complex systems analysis

Review of Machine Learning Approaches In Fault Diagnosis applied to IoT System

International audienceWith increasing complex systems, low production costs, and changing technologies, for this reason, the automatic fault diagnosis using artificial intelligence (AI) techniques is more in more applied. In addition, with the emergence of the use of reconfigurable systems, AI can assist in self-maintenance of complex systems. The purpose of this article is to summarize the diagnosis research of systems using AI approaches and examine their application particularly in the field of diagnosis of complex systems. It covers articles published from 2002 to 2018 using Machine Learning tools for fault diagnosis in industrial systems

Making intelligent systems team players: Case studies and design issues. Volume 1: Human-computer interaction design

Initial results are reported from a multi-year, interdisciplinary effort to provide guidance and assistance for designers of intelligent systems and their user interfaces. The objective is to achieve more effective human-computer interaction (HCI) for systems with real time fault management capabilities. Intelligent fault management systems within the NASA were evaluated for insight into the design of systems with complex HCI. Preliminary results include: (1) a description of real time fault management in aerospace domains; (2) recommendations and examples for improving intelligent systems design and user interface design; (3) identification of issues requiring further research; and (4) recommendations for a development methodology integrating HCI design into intelligent system design

TROUBLE 3: A fault diagnostic expert system for Space Station Freedom's power system

Designing Space Station Freedom has given NASA many opportunities to develop expert systems that automate onboard operations of space based systems. One such development, TROUBLE 3, an expert system that was designed to automate the fault diagnostics of Space Station Freedom's electric power system is described. TROUBLE 3's design is complicated by the fact that Space Station Freedom's power system is evolving and changing. TROUBLE 3 has to be made flexible enough to handle changes with minimal changes to the program. Three types of expert systems were studied: rule-based, set-covering, and model-based. A set-covering approach was selected for TROUBLE 3 because if offered the needed flexibility that was missing from the other approaches. With this flexibility, TROUBLE 3 is not limited to Space Station Freedom applications, it can easily be adapted to handle any diagnostic system

System monitoring and diagnosis with qualitative models

A substantial foundation of tools for model-based reasoning with incomplete knowledge was developed: QSIM (a qualitative simulation program) and its extensions for qualitative simulation; Q2, Q3 and their successors for quantitative reasoning on a qualitative framework; and the CC (component-connection) and QPC (Qualitative Process Theory) model compilers for building QSIM QDE (qualitative differential equation) models starting from different ontological assumptions. Other model-compilers for QDE's, e.g., using bond graphs or compartmental models, have been developed elsewhere. These model-building tools will support automatic construction of qualitative models from physical specifications, and further research into selection of appropriate modeling viewpoints. For monitoring and diagnosis, plausible hypotheses are unified against observations to strengthen or refute the predicted behaviors. In MIMIC (Model Integration via Mesh Interpolation Coefficients), multiple hypothesized models of the system are tracked in parallel in order to reduce the 'missing model' problem. Each model begins as a qualitative model, and is unified with a priori quantitative knowledge and with the stream of incoming observational data. When the model/data unification yields a contradiction, the model is refuted. When there is no contradiction, the predictions of the model are progressively strengthened, for use in procedure planning and differential diagnosis. Only under a qualitative level of description can a finite set of models guarantee the complete coverage necessary for this performance. The results of this research are presented in several publications. Abstracts of these published papers are presented along with abtracts of papers representing work that was synergistic with the NASA grant but funded otherwise. These 28 papers include but are not limited to: 'Combined qualitative and numerical simulation with Q3'; 'Comparative analysis and qualitative integral representations'; 'Model-based monitoring of dynamic systems'; 'Numerical behavior envelopes for qualitative models'; 'Higher-order derivative constraints in qualitative simulation'; and 'Non-intersection of trajectories in qualitative phase space: a global constraint for qualitative simulation.

Self-calibrating models for dynamic monitoring and diagnosis

A method for automatically building qualitative and semi-quantitative models of dynamic systems, and using them for monitoring and fault diagnosis, is developed and demonstrated. The qualitative approach and semi-quantitative method are applied to monitoring observation streams, and to design of non-linear control systems

Qualitative Futures

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