Hybrid DBN monitoring and anomaly detection algorithms for on-line SHM

This paper presents a new modeling approach, computational algorithm, and an example application for health monitoring and anomaly detection in on-line System Health Management (SHM). A hybrid Dynamic Bayesian network (DBN) is introduced with component-based structure to represent complex engineering systems with underlying physics of failure by modeling an empirical degradation model with continuous variables. The methodology is designed to be flexible and intuitive, and scalable from small localized functionality to large complex dynamic systems. Markov Chain Monte Carlo (MCMC) inference is optimized using precomputation and dynamic programming for real-time monitoring of system health. The anomaly detection algorithm uses pattern recognition to improve failure detection and estimation of Remaining Useful Life (RUL). The scope of this research includes a new modeling approach, computation algorithm, and an example application for on-line SHM.