Sensory-Based Prognostics and Life Prediction for Components with Exponential Degradation

Research on interpreting sensory data communicated by smart sensors and distributed sensor networks, and utilizing these data streams in making critical decisions stands to provide significant advancements across a wide range of application domains such as maintenance management. In this paper, a stochastic degradation modeling framework is developed for computing and continuously updating residual life distributions of partially degraded components. The methodology combines sensory data acquired through condition monitoring technology with reliability and degradation characteristics using novel sensory updating techniques. A sensory updating procedure is developed and validated using real world degradation data from rolling element thrust bearings.