Submitted:
17 September 2020
Posted:
20 September 2020
You are already at the latest version
Abstract
This paper presents a methodology for predictive and prescriptive analytics of complex engineering systems. The methodology is based on a combination of physics-based and data-driven modeling using machine learning techniques. Combining these approaches results in a set of reliable, fast, and continuously updating models for prescriptive analytics. The methodology is demonstrated with a case study of a jet-engine power plant preventive maintenance and diagnostics of its flame tube.
Keywords:
hybrid modelling
; prescriptive analytics
; gas engine
; machine learning
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