Fault diagnosis of rotating machinery plays a significant role for the reliability and safety of
modern industrial systems. As an emerging field in industrial applications and an effective …

Malware detection and classification are becoming more and more challenging, given the
complexity of malware design and the recent advancement of communication and …

Deep learning based fault diagnosis methods assume that training and testing data with
sufficient labels are available and share a same distribution. In practical scenarios, this …

Recurrent Neural Networks (RNNs) have demonstrated their outstanding ability in sequence
tasks and have achieved state-of-the-art in wide range of applications, such as industrial …

A recurrent neural network (RNN) has demonstrated its outstanding ability in sequence
tasks and has achieved state of the art in many applications, such as industrial and medical …

External and internal factors can cause disturbances or disruptions in daily train operations,
leading to deviations from official timetables and passenger delays. As a result, efficient train …

This paper aims to develop an accurate and efficient end-to-end fault detection model
trained by small-scale data for the rotating machinery. The echo state network (ESN) is …

Restricted Boltzmann machines (RBMs) and their variants are usually trained by contrastive
divergence (CD) learning, but the training procedure is an unsupervised learning approach …

Remaining useful life (RUL) prediction is essential to ensure safety and reliability of
engineering systems. To achieve better prediction performance, causalities among the …

In this paper, we present a novel unsupervised feature learning architecture, which consists
of a multi-clustering integration module and a variant of RBM termed multi-clustering …