RUL Prediction of Sensor Based Machines Using Deep Learning

Abstract

One of the key aspects of prognostics and health management (PHM) is predicting remaining useful life (RUL), specifically for machinery that uses sensors and requires periodic machine health surveillance. Predicting RUL and scheduling maintenance appropriately may significantly enhance operational efficiency and safeguard against unexpected failures. But in order to make accurate predictions, data-driven models frequently face uncertainty that must be evaluated. In this study, we have proposed an approach as CNN-GRU-SAM with Variational inference to address this problem. Our approach integrates convolutional neural networks (CNN) with self-attention mechanism (SAM) and gated recurrent units (GRU) with variational inference in order to deliver precise RUL predictions while addressing uncertainty. Employing comprehensive training and testing, we demonstrate that our suggested model is efficient in predicting RUL and quantifying uncertainty, enhancing the PHM of equipments potential to guarantee the dependability and durability of equipment that relies on sensors.