Health prediction of hydraulic cooling circuit using deep neural network with ensemble feature ranking technique

Abstract

Health prediction of the hydraulic systems is of utmost importance as any breakdown may lead to severe losses. In the present manuscript, the emphasis is on developing an artificially intelligent model using a deep neural network to predict the working behaviour of the cooling circuit in the hydraulic system. Overall, four different models have been proposed and compared for their performance. Features are calculated from the pressure signals. The capabilities of XGBoost and ReliefF have been compared as a feature ranking technique and the implications of two different activation function “tanh” and “relu” have been analysed. Features shortlisted through XGBoost gives higher performance with “tanh” activation function. The result reveals that the deep neural network model can be effectively used to predict the health of hydraulic cooling circuit. © 2019 Elsevier Ltd