Air Filter Loading Characteristics for an Epigenetic Modeling Approach to Adaptive Prognostics

New capabilities involving sensors, data collection, and data analysis have enabled innovations in how engineered systems are monitored and maintained. Whereas each new evolution of maintenance philosophies has relied upon the current technological state, this research examines potential future capabilities in the field of prognostics and health management (PHM). PHM algorithms for predicting the estimated time to failure for a system are based on sensor data, physical models, or a combination of both. Each approach has its documented strengths and weaknesses; however, a common shortcoming is the limited ability to represent the individualized dynamic interplay of factors that affect system health. As materials science and engineering progress toward components that have “genetic” intelligence—inherent manufactured capabilities to sense, store, and analyze environmental and system conditions—a new paradigm for evaluating system health is required. The corresponding paper presents a natural computing approach to prognostics based on the biological phenomenon of epigenetics that is applicable to this next generation of systems. Epigenetics is concerned with how environmental factors influence an organism’s genetic expression over time, directly impacting the health of one or more systems. A multitiered framework is presented that provides a formal representation of a system and its environment, an ontology, an epigenetic degradation model, and a way to represent failure pathways. The framework components are demonstrated in an air filtration case study, illustrating the utility of the new paradigm within an existing prognostics application.   This dataset is used to provide the primary performance characteristics and their evolution over time of a fibrous air filter subject to loading from solid aerosols. A simulation of realistic aerosol filtration was conducted in order to generate the dataset, which was utilized in conjunction with the corresponding paper as a case study for filter degradation and the application of the proposed epigenetic modeling approach.