Improved Fault Diagnosis in Hydraulic Systems with Gated Convolutional Autoencoder and Partially Simulated Data

Improved Fault Diagnosis in Hydraulic Systems with Gated Convolutional Autoencoder and Partially Simulated Data

This paper examines the effectiveness of neural network algorithms for hydraulic system fault detection and a novel neural network architecture is suggested. The proposed gated convolutional autoencoder was trained on a simulated training set augmented with just 0.2% data from the real test bench, d...

Full description

Bibliographic Details
Main Authors: Albert Gareev, Vladimir Protsenko, Dmitriy Stadnik, Pavel Greshniakov, Yuriy Yuzifovich, Evgeniy Minaev, Asgat Gimadiev, Artem Nikonorov
Format: Article
Language:English
Published: MDPI AG 2021-06-01
Series:Sensors
Subjects:
intelligent fault detection
hydraulic systems
sensor signals
classification
Online Access:https://www.mdpi.com/1424-8220/21/13/4410

Internet

https://www.mdpi.com/1424-8220/21/13/4410

Similar Items