Co-Design of Embeddable Diagnostics using Reduced-Order Models*. Issue 1 (July 2017)

• Record Type: Journal Article
• Authors: Provan, Gregory
• Is Part Of: IFAC-PapersOnLine. Volume 50:Issue 1(2017)
• Page Start: 12222
• Page End: 12229
• Publication Date: 2017-07
• Subjects: Fault Detection -- Supervision -- Safety of Technical Process: AI methods for FDI -- Design of fault tolerant/reliable systems -- Computational methods for FDI; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2017.08.2122
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: We develop a system for generating embedded diagnostics from an ODE model that can isolate faults given the memory and processing limitations of the embedded processor. This system trades off diagnosis isolation accuracy for inference time and/or memory in a principled manner. We use a Polynomial Regression approach for tuning the performance of an ensemble of low-fidelity ODE diagnosis models such that we achieve the target of embedded processing limits. We demonstrate our approach on a non-linear tank benchmark system.