A Stacked Autoencoder-Based Deep Neural Network for Achieving Gearbox Fault Diagnosis

Developing A Fault Diagnosis Approach Based On Artificial Neural Network And Self Organization Map For Occurred ADSL Faults

Telecommunication companies have received a great deal of research attention, which have many advantages such as low cost, higher qualification, simple installation and maintenance, and high reliability. However, the using of technical maintenance approaches in Telecommunication companies could improve system reliability and users' satisfaction from Asymmetric digital subscriber line (ADSL) ser...

Detection of Pitting in Gears Using a Deep Sparse Autoencoder

In this paper; a new method for gear pitting fault detection is presented. The presented method is developed based on a deep sparse autoencoder. The method integrates dictionary learning in sparse coding into a stacked autoencoder network. Sparse coding with dictionary learning is viewed as an adaptive feature extraction method for machinery fault diagnosis. An autoencoder is an unsupervised ma...

A Membership-based Multi-dimension Hierarchical Deep Neural Network Approach for Fault Diagnosis

Accurate fault prognosis of machine component is important to maintain industry operation system. Faults analysis can be very helpful in fault early warning and reducing maintenance cost. The goal of our work is to design an integrated approach of machine faults analysis. A method widely used is Fuzzy Neural Networks (FNNs), but such method lacks of flexibility. We present a Membership-based Mu...

An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox

A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion ...

[Article] EEG Based Eye State Classification using Deep Belief Network and Stacked AutoEncoder