Model Based Mission Assurance: NASA's Assurance Future

Model Based Systems Engineering (MBSE) is seeing increased application in planning and design of NASAs missions. This suggests the question: what will be the corresponding practice of Model Based Mission Assurance (MBMA)? Contemporaneously, NASAs Office of Safety and Mission Assurance (OSMA) is evaluating a new objectives based approach to standards to ensure that the Safety and Mission Assurance disciplines and programs are addressing the challenges of NASAs changing missions, acquisition and engineering practices, and technology. MBSE is a prominent example of a changing engineering practice. We use NASAs objectives-based strategy for Reliability and Maintainability as a means to examine how MBSE will affect assurance. We surveyed MBSE literature to look specifically for these affects, and find a variety of them discussed (some are anticipated, some are reported from applications to date). Predominantly these apply to the early stages of design, although there are also extrapolations of how MBSE practices will have benefits for testing phases. As the effort to develop MBMA continues, it will need to clearly and unambiguously establish the roles of uncertainty and risk in the system model. This will enable a variety of uncertainty-based analyses to be performed much more rapidly than ever before and has the promise to increase the integration of CRM (Continuous Risk Management) and PRA (Probabilistic Risk Analyses) even more fully into the project development life cycle. Various views and viewpoints will be required for assurance disciplines, and an over-arching viewpoint will then be able to more completely characterize the state of the project/program as well as (possibly) enabling the safety case approach for overall risk awareness and communication

On the role of Prognostics and Health Management in advanced maintenance systems

The advanced use of the Information and Communication Technologies is evolving the way that systems are managed and maintained. A great number of techniques and methods have emerged in the light of these advances allowing to have an accurate and knowledge about the systems’ condition evolution and remaining useful life. The advances are recognized as outcomes of an innovative discipline, nowadays discussed under the term of Prognostics and Health Management (PHM). In order to analyze how maintenance will change by using PHM, a conceptual model is proposed built upon three views. The model highlights: (i) how PHM may impact the definition of maintenance policies; (ii) how PHM fits within the Condition Based Maintenance (CBM) and (iii) how PHM can be integrated into Reliability Centered Maintenance (RCM) programs. The conceptual model is the research finding of this review note and helps to discuss the role of PHM in advanced maintenance systems.EU Framework Programme Horizon 2020, 645733 - Sustain-Owner - H2020-MSCA-RISE-201

Cascade Distillation System Design for Safety and Mission Assurance

Per the NASA Human Health, Life Support and Habitation System Technology Area 06 report "crewed missions venturing beyond Low-Earth Orbit (LEO) will require technologies with improved reliability, reduced mass, self-sufficiency, and minimal logistical needs as an emergency or quick-return option will not be feasible".1 To meet this need, the development team of the second generation Cascade Distillation System (CDS 2.0) chose a development approach that explicitly incorporate consideration of safety, mission assurance, and autonomy. The CDS 2.0 preliminary design focused on establishing a functional baseline that meets the CDS core capabilities and performance. The critical design phase is now focused on incorporating features through a deliberative process of establishing the systems failure modes and effects, identifying mitigation strategies, and evaluating the merit of the proposed actions through analysis and test. This paper details results of this effort on the CDS 2.0 design

An optimized fuzzy logic model for proactive maintenance

Fuzzy logic has been proposed in previous studies for machine diagnosis, to overcome different drawbacks of the traditional diagnostic approaches used. Among these approaches Failure Mode and Effect Critical Analysis method(FMECA) attempts to identify potential modes and treat failures before they occur based on subjective expert judgments. Although several versions of fuzzy logic are used to improve FMECA or to replace it, since it is an extremely cost-intensive approach in terms of failure modes because it evaluates each one of them separately, these propositions have not explicitly focused on the combinatorial complexity nor justified the choice of membership functions in Fuzzy logic modeling. Within this context, we develop an optimization-based approach referred to Integrated Truth Table and Fuzzy Logic Model (ITTFLM) that smartly generates fuzzy logic rules using Truth Tables. The ITTFLM was tested on fan data collected in real-time from a plant machine. In the experiment, three types of membership functions (Triangular, Trapezoidal, and Gaussian) were used. The ITTFLM can generate outputs in 5ms, the results demonstrate that this model based on the Trapezoidal membership functions identifies the failure states with high accuracy, and its capability of dealing with large numbers of rules and thus meets the real-time constraints that usually impact user experience.Comment: 16 pages in single column format, 11 figures, 12th International Conference on Artificial Intelligence, Soft Computing and Applications (AIAA 2022) December 22 ~ 24, 2022, Sydney, Australi

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

RELIABILITY TESTING & BAYESIAN MODELING OF HIGH POWER LEDS FOR USE IN A MEDICAL DIAGNOSTIC APPLICATION

While use of LEDs in fiber optics and lighting applications is common, their use in medical diagnostic applications is rare. Since the precise value of light intensity is used to interpret patient results, understanding failure modes is very important. The contributions of this thesis is that it represents the first measurements of reliability of AlGaInP LEDs for the medical environment of short pulse bursts and hence the uncovering of unique failure mechanisms. Through accelerated life tests (ALT), the reliability degradation model has been developed and other LED failure modes have been compared through a failure modes and effects criticality analysis (FMECA). Appropriate ALTs and accelerated degradation tests (ADT) were designed and carried out for commercially available AlGaInP LEDs. The bias conditions were current pulse magnitude and duration, current density and temperature. The data was fitted to both an Inverse Power Law model with current density J as the accelerating agent and also to an Arrhenius model with T as the accelerating agent. The optical degradation during ALT/ADT was found to be logarithmic with time at each test temperature. Further, the LED bandgap temporarily shifts towards the longer wavelength at high current and high junction temperature. Empirical coefficients for Varshini's equation were determined, and are now available for future reliability tests of LEDs for medical applications. In order to incorporate prior knowledge, the Bayesian analysis was carried out for LEDs. This consisted of identifying pertinent prior data and combining the experimental ALT results into a Weibull probability model for time to failure determination. The Weibull based Bayesian likelihood function was derived. For the 1st Bayesian updating, a uniform distribution function was used as the Prior for Weibull á-â parameters. Prior published data was used as evidence to get the 1st posterior joint á-â distribution. For the 2nd Bayesian updating, ALT data was used as evidence to obtain the 2nd posterior joint á-â distribution. The predictive posterior failure distribution was estimated by averaging over the range of á-â values. This research provides a unique contribution in reliability degradation model development based on physics of failure by modeling the LED output characterization (logarithmic degradation, TTF â<1), temperature dependence and a degree of Relevance parameter `R' in the Bayesian analysis