A Prognostic Degradation-Based Methodology for Improving Reliability Assessment and Maintenance Management

GUEST LECTURER
Dr. Nagi Gebraeel

AFFILIATION
University of Iowa

ABSTRACT
Unexpected failures of engineering systems are major contributors to human fatalities and result in astounding maintenance costs. It is estimated that US industry spends $200 billion each year on reliability and maintenance. The high degree of uncertainty associated with degradation processes (even for similar components operating under similar environmental conditions), and our limited understanding of the physics-of-failure are major obstacles in accurately assessing reliability measures and predicting unexpected failures.

This seminar discusses the development of a sensor-based stochastic degradation modeling framework that combines conventional reliability formalisms with condition/health monitoring methods to improve failure predictability. Many physical degradation processes that occur prior to failure can only be observed indirectly using condition/health monitoring techniques that capture degradation-based sensory information. This research rests on the idea that the functional forms of degradation-based sensory signals are driven by the underlying physical degradation phenomena that occur prior to failure. The approach used in this research is to model the evolution of degradation signals using continuous-time continuous-state stochastic models. These stochastic models combine two sources of information; (1) population-specific reliability and degradation characteristics, which are used to estimate preliminary residual life distributions; and (2) in-situ component-specific degradation signals, which are used to update the residual life distributions, in real-time, based on the latest degradation states of the systems being monitored. These dynamically evolving degradation-based residual life distributions will then be utilized to improve reliability assessment and enable the development of sensor-driven replacement and spare parts inventory decision models.