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Download fileDeep recurrent entropy adaptive model for system reliability monitoring
journal contribution
posted on 2020-10-01, 12:47 authored by Miguel Martinez-GarciaMiguel Martinez-Garcia, Yu Zhang, Kenji Suzuki, Yu-Dong ZhangThe aim of this paper is to develop a methodology
for measuring the degree of unpredictability in dynamical systems
with memory, i.e., systems with responses dependent on a history
of past states. The proposed model is generic, and can be
employed in a variety of settings, although its applicability here is
examined in the particular context of an industrial environment:
gas turbine engines. The given approach consists in approximating the probability distribution of the outputs of a system with
a deep recurrent neural network; such networks are capable of
exploiting the memory in the system for enhanced forecasting
capability. Once the probability distribution is retrieved, the
entropy or missing information about the underlying process is
computed, which is interpreted as the uncertainty with respect
to the system’s behaviour. Hence the model identifies how far
the system dynamics are from its typical response, in order to
evaluate the system reliability and to predict system faults and/or
normal accidents. The validity of the model is verified with sensor
data recorded from commissioning gas turbines, belonging to
normal and faulty conditions.
Funding
Evolutionary Virtual Expert System
Engineering and Physical Sciences Research Council
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School
- Mechanical, Electrical and Manufacturing Engineering