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.

翻译：航空发动机故障预测旨在准确预测航空发动机未来状态的发展趋势，从而实现故障的提前诊断。传统的航空发动机参数预测方法主要利用时间序列数据的非线性映射关系，但通常忽略了航空发动机数据中所蕴含的充分时空特征。为此，我们提出一种受大脑启发的脉冲回声状态网络（Spike-ESN）模型，用于航空发动机智能故障预测，该模型能够在时空动力学框架下有效捕捉航空发动机时间序列数据的演化过程。在所提出的方法中，我们受生物神经元脉冲神经编码机制的启发，设计了一个基于泊松分布的脉冲输入层，该层能够提取航空发动机序列数据中有用的时间特征。随后，通过神经元中脉冲累积的电流计算方法，将这些时间特征输入到一个脉冲储备池中，从而将数据映射到一个高维稀疏空间。此外，我们采用岭回归方法来读取脉冲储备池的内部状态。最后，航空发动机状态预测的实验结果证明了所提方法的优越性和潜力。