  Aero-engine fault prediction aims to accurately predict the development trend
of the future state of aero-engines, so as to diagnose faults in advance.
Traditional aero-engine parameter prediction methods mainly use the nonlinear
mapping relationship of time series data but generally ignore the adequate
spatiotemporal features contained in aero-engine data. To this end, we propose
a brain-inspired spike echo state network (Spike-ESN) model for aero-engine
intelligent fault prediction, which is used to effectively capture the
evolution process of aero-engine time series data in the framework of
spatiotemporal dynamics. In the proposed approach, we design a spike input
layer based on Poisson distribution inspired by the spike neural encoding
mechanism of biological neurons, which can extract the useful temporal
characteristics in aero-engine sequence data. Then, the temporal
characteristics are input into a spike reservoir through the current
calculation method of spike accumulation in neurons, which projects the data
into a high-dimensional sparse space. In addition, we use the ridge regression
method to read out the internal state of the spike reservoir. Finally, the
experimental results of aero-engine states prediction demonstrate the
superiority and potential of the proposed method.
