Representation learning-based remaining useful life (RUL) prediction plays a crucial role in
improving the security and reducing the maintenance cost of complex systems. Despite the …

Data-driven methods routinely achieve promising results on remaining useful life prediction,
but under a window-manner end-to-end paradigm, they suffer from unsatisfying …

For prognostics and health management of mechanical systems, a core task is to predict the
machine remaining useful life (RUL). Currently, deep structures with automatic feature …

Accurate estimation of remaining useful life (RUL) of industrial equipment can enable
advanced maintenance schedules, increase equipment availability and reduce operational …

Convolution neural network (CNN) has been widely used in the field of remaining useful life
(RUL) prediction. However, the CNN-based RUL prediction methods have some limitations …

Prognostic techniques attempt to predict the Remaining Useful Life (RUL) of a subsystem or
a component. Such techniques often use sensor data which are periodically measured and …

Data-driven prediction of remaining useful life (RUL) has emerged as one of the most sought-
after research in prognostics and health management (PHM). Nevertheless, most RUL …

Remaining useful life (RUL) prediction is essential in prognostics and health management
(PHM) applications, where data-driven approaches employ the tendency of the degradation …

A novel encoder-decoder model based on deep neural networks is proposed for the
prediction of remaining useful life (RUL) in this work. The proposed model consists of an …

Recently, deep learning is widely used in the field of remaining useful life (RUL) prediction.
Among various deep learning technologies, recurrent neural network (RNN) and its variant …