Emmanuel Nwadiogbu

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This paper describes a case study of model-based diagnostics system development for an aircraft Auxiliary Power Unit (APU) turbine system. The off-line diagnostics algorithms described in the paper work with historical data of a flight cycle. The diagnostics algorithms use detailed turbine engine systems models and fault model knowledge available to an(More)
Accurate fault detection and diagnosis in turbine engines is key to effective maintenance and improved availability of systems dependent on these engines. In this paper, we present a novel method for accurate fault detection and diagnosis using Hidden Markov Models (HMMs). In particular, we focus on engine faults that are manifest in transient operating(More)
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