MODELING OF DYNAMIC SYSTEMS WITH INTERACTING FAILURE MECHANISMS. Katherine Gromek. Mohammad Modarres. Center for Risk and Reliability.
AN AGENT AUTONOMY APPROACH TO PROBABILISTIC PHYSICS-‐OF-‐FAILURE MODELING OF DYNAMIC SYSTEMS WITH INTERACTING FAILURE MECHANISMS
Katherine Gromek Mohammad Modarres Center for Risk and Reliability Department of Mechanical Engineering University of Maryland College Park, MD 20742 As products and systems are becoming increasingly complex, they experience new and multifaceted failure modes resulting from convoluted and often interdependent physical failure mechanisms. At the same time life tests of such systems are costly and time consuming. Coupled with the requirement for extensive field data for empirical modeling and assessment of reliability measures, prediction of reliability and risk of complex systems needs consideration of fresh and more efficient methods. As an alternative to fully empirical reliability modeling established without consideration of the underlying physical processes that lead to failure (i.e., failure mechanisms), the Physics-‐of-‐Failure (PoF) approach [1] is a powerful option for reliability assessment of complex systems. The PoF approach is based on modeling and simulation of the relevant physical processes that contribute to degradation and leading to failures. The PoF-‐based (or mechanistic-‐based) reliability models provide comprehensive representation of system degradation, capable of bringing many influential factors into the life and reliability assessment. These factors include environmental and operational stresses, mission profile and manufacturing processes. As such, the PoF approach makes reliability and risk assessment more relevant and highly system-‐specific. Due to the diversity of components and their failure mechanisms, complexity of system logic and various types of dependencies at all levels of system hierarchy, comprehensive use of PPoF approach in risk and reliability modeling of a complex dynamic system can be very challenging, if not impossible. Traditional static models of system reliability (such as Fault Tree, Event Tree and Reliability Block Diagram), as well as dynamic methods of system modeling (Markov Chains, Stochastic Petri Nets, Dynamic Event Trees) are not always capable of properly incorporating physical models of system components and also impose new challenges. For example: 1) very limited ability to model dynamics of system hardware over time, including degraded states of a system, and 2) inability to provide quantitative causal relations between several competing interacting and interdependent failure mechanisms of one or multiple components. This paper outlines a framework for PPoF-‐based reliability modeling using the agent-‐based computing. PPoF models will be used to make a robust real-‐time simulation of system components and failure processes, so that the system level modeling will constitute checking the status of components at any given time. “Agent Autonomy” concept will be used as a solution method for the PPoF modeling. This concept has originated from Artificial Intelligence (AI) developments in Multi Agents System (MAS) [2]. 1
Critical challenges addressed in this research include modeling agent anatomy within the scope of PoF models of the system and introduction of agent learning as a main property of intelligent agents. Bayesian probabilistic framework used in risk assessment provides the formalisms for agent learning. Another key property of intelligent agents is their ability to activate, deactivate and completely redefine themselves, which makes the agents autonomous and fundamentally different than existing methods of PoF reliability modeling. The agent structure proposed in this work introduces and combines several types of agents to optimize the use of data, and allow mutual communication between agents possible. Different levels of agent autonomy can be defined, depending on the nature of degradation and physical failure processes occurring in the given system and complexity of interactions between system components and piece parts, such as in the example conceptually described by Figure 1. An intelligent agent further represents each element of the hierarchy shown in Figures 1 and is simulated using agent-‐based computing. The paper will demonstrate the agent-‐based PPoF method with an example of reliability assessment of a turbine system. Figure1 Overall Structure of POF models used in agent-‐based system reliability assessment System Hierarchy Flow Chart
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[1] K. Chatterjee, M. Modarres, J. Bernstein, D. Nicholls, Celebrating Fifty Years of Physics of Failure, Proceedings of the 2013 Reliability and Maintainability Symposium (RAMS), Jan. 2013, Orlando, FL. [2] Panait, Liviu; Luke, Sean, Cooperative Multi-‐Agent Learning: The State of the Art, Autonomous Agents and Multi-‐Agent Systems 11 (3): 387–434 (2005). 2
