Predictive Fault Diagnosis System for Intelligent and Robust Health Monitoring

Abstract

This paper describes work related to the Predictive Fault Diagnosis System for Intelligent and Robust Health Monitoring, which exists as a solution to complete failure detection, identification, and prognostics (FDI&P) in health monitoring applications. Several advanced FDI analytical redundancy techniques have been applied for such a purpose, with the most notable being a compound method comprised of optimal filtering, statistical analysis, and neuro-fuzzy algorithms that is able to detect and diagnose both known and unknown failures. Although this scheme has been proven to be quite successful for systems that can be well described in a state space representation, current research has shown the viability in extending the process to highly complex systems with considerable nonlinearities while still maintaining the FDI capabilities. This paper highlights the utility of these algorithms for determining failures in (1) a known reusable liquid rocket engine model and (2) an unknown input-output relation in a fluid flow testbed. Other research has focused on prognostic capabilities provided by a neural architecture enhanced with fuzzy logic using rule-based knowledge. An example of using data to construct fuzzy rules for determining the remaining useful life of components is provided to give insight into the process.