Reservoir Neural Network Computing for Time Series Forecasting in Aerospace: Potential Applications to Predictive Maintenance

Abstract

Coupling a reservoir neural network and a Grey Wolf optimization algorithm the system hyperparameters space is explored to find the configuration best suited to forecast the input sensor from the NASA CMAPSS dataset. In such a framework, the application to the problem of predictive maintenance is considered. The necessary requirements for the system to generate satisfactory predictions are established, with specific suggestions as to how a forecast can be improved through reservoir computing. The obtained results are used to determine certain common rules that improve the quality of the predictions and focus the optimization towards hyperparameter solutions that may allow for a faster approach to predictive maintenance. This research is a starting point to develop methods that could inform accurately on the remaining useful life of a component in aerospace systems.

Keywords

• reservoir computing
• grey wolf optimizer
• aerospace
• time series forecasting