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Tag: Prognostics and health management
  • Adverse Outcome Pathways for Engineered Systems

    Abstract: Companies and organizations around the world spend massive amounts of money each year to discover, predict, and remediate failures within engineered systems. These tasks require individuals with specialized knowledge in a variety of topics related to failure. This knowledge is often acquired through years of academic and on-the-job training centered around the review of scientific documentation such as books, reports, manuals, and peer-reviewed publications. The loss of this knowledge through employee attrition can be detrimental to a group as knowledge is often difficult to reacquire. The aggregation and representation of known failure mechanisms for engineered materials could aid in the sharing of knowledge, the acquisition of knowledge, and the discovery of failure causes, reducing the risk of failure. Thus, the current work proposes the Adverse Outcome Pathway for Engineered Systems (AOP-ES) framework, an extension of the Adverse Outcome Pathway used in toxicology. The AOP-ES is designed to document failure knowledge, enabling knowledge transfer and the prediction of failures of novel engineered materials based on the performance of similar materials. This paper introduces the AOP-ES framework and its key elements alongside the principles that govern the framework. An application of the framework is presented, and additional benefits are explored.
  • An Ontology for an Epigenetics Approach to Prognostics and Health Management

    Abstract: Techniques in prognostics and health management have advanced considerably in the last few decades, enabled by breakthroughs in computational methods and supporting technologies. These predictive models, whether data-driven or physics-based, target the modeling of a system’s aggregate performance. As such, they generalize assumptions about the modelled system’s components, and are thus limited in their ability to represent individual components and the dynamic environmental factors that affect composite system health. To address this deficiency, we have developed an epigenetics-inspired knowledge representation for engineered system state that encompasses components and environmental factors. Epigenetics is concerned with explaining how environmental factors affect the expression of an organism’s genetic material. The field has derived important insights into the development and progression of disease states based on how environmental factors impact genetic material, causing variations in how a gene is expressed. The health of an engineered system is similarly influenced by its environment. A foundation for a new approach to prognostics based on epigenetics must begin by representing the entities and relationships of an engineered system from the perspective of epigenetics. This paper presents an ontology for an epigenetics-inspired representation of an engineered system. An ontology describing the epigenetics of an engineered system will enable the composition of a formal model and the incremental development of a more robust, causal reasoning system.
  • A Fuzzy Epigenetic Model for Representing Degradation in Engineered Systems

    Abstract: Degradation processes are implicated in a large number of system failures, and are crucial to understanding issues related to reliability and safety. Systems typically degrade in response to stressors, such as physical or chemical environmental conditions, which can vary widely for identical units that are deployed in different places or for different uses. This situational variance makes it difficult to develop accurate physics-based or data-driven models to assess and predict the system health status of individual components. To address this issue, we propose a fuzzy set model for representing degradation in engineered systems that is based on a bioinspired concept from the field of epigenetics. Epigenetics is concerned with the regulation of gene expression resulting from environmental or other factors, such as toxicants or diet. One of the most studied epigenetic processes is methylation, which involves the attachment of methyl groups to genomic regulatory regions. Methylation of specific genes has been implicated in numerous chronic diseases, so provides an excellent analog to system degradation. We present a fuzzy set model for characterizing system degradation as a methylation process based on a set-theoretic representation for epigenetic modeling of engineered systems. This model allows us to capture the individual dynamic relationships among a system, environmental factors, and state of health .