Convolutional neural network framework to predict remaining useful life in machines

Modern industries require efficient and reliable machinery. To ensure the stability of industrial equipment and avoid unnecessary downtime, it is important to gauge a machine's remaining useful life (RUL) accurately. This has been done using deep learning-based approaches. In particular, deep neural networks are considered promising in this regard. These include recurrent neural networks (RNNs), convolutional neural networks (CNNs), or hybrid networks.