Predictive maintenance applied to mission critical supercomputing environments: remaining useful life estimation of a hydraulic cooling system using deep learning

Given the growth and availability of computing power, artificial intelligence techniques have been applied to industrial equipment and computing devices in order to identify abnormalities in operation and predict the remaining useful life (RUL) of equipment with superior performance than traditional predictive maintenance. In this sense, this research aims to develop a neural network applied to predictive maintenance in mission critical supercomputing environments (MCSE) using deep learning techniques to predict the RUL of an equipment before the occurrence of failures, by using real historical unlabeled data, which were collected by sensors installed in a supercomputing environment. The method was developed using a hybrid approach based on a combination of Fully Convolutional Neural Network, Long Short-Term Memory and Multilayer Perceptron. The results presented a Pearson R of 0.87, R2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${R}^{2}$$\end{document} of 0.77, Factor of 2 of 0.89, and Normalized mean square error of 0.79, considering the predicted RUL value and the observed RUL value for the pre-failure behavior moments of the equipment. Thus, we can conclude that the developed approach had good performance to predict the RUL, increasing the ability to anticipate the failure situation of the MCSE, further increasing its availability and operating time.