In recent years, the physics-of-failure (POF) modeling, also referred to as mechanistic failure modeling, has emerged as a powerful approach for reliability assessment of mechanical components. The POF approach to reliability utilize scientific knowledge of degradation processes, the load profile, component architecture, material properties and environmental conditions to identify and model potential failure mechanisms that lead to failure of the item.

POF models are usually used to construct the component time-to-failure distribution which is consequently used in the probabilistic reliability prediction. Distribution of time-to-failure is conditioned on the operational and environmental conditions, which can vary significantly in a dynamic system. POF modeling provides many features to include dynamic variability of the influential factors. Nevertheless, despite the considerable achievements in component reliability assessment, the POF approach lacks a formal structure to be applicable at the system-level. This issue, however, may be viewed from another perspective. That is, POF models are treated the same as the traditional hierarchical reliability models of the system such as fault/event trees and reliability block diagrams that are not concerned with capturing the causality of failures.

In this research a framework is proposed to bring the POF-based reliability models of components into the system-level reliability assessment. Consider a virtual environment in which each component is replaced with a piece of intelligent software that not only contains all properties of the component, but also is able to mimic all its behaviors. This substitute contains all available knowledge about the failure of the component and acts autonomously. This replica of the component is also able to communicate with other components and not only has memory to keep the history of events, but also is able to share information to include functional dependencies.

In this research, POF models are used to make a robust real-time simulation that mimics the failure processes applicable to the components and the system. Utilizing this approach, system-level modeling becomes as simple as checking the status of components at any given time. This research is an attempt to borrow "Agent Autonomy" concept from artificial intelligence (AI) and adapt it to system-level reliability modeling purposes. Agent programming is one of the most advanced methods in modeling of Multi Agents Systems (MAS). In this dissertation the terminology of agent autonomy is represented in the reliability engineering context using case studies, such that the equivalent terms and conditions are defined and practical advantages are highlighted.