Data fusion of acceleration and velocity features (dFAVF) approach for fault diagnosis in rotating machines

Publisher： Edp Sciences

E-ISSN： 2261-236x|211|issue|21005-21005

ISSN： 2261-236x

Source： MATEC Web of conference, Vol.211, Iss.issue, 2018-10, pp. : 21005-21005

Access to resources Favorite

Disclaimer: Any content in publications that violate the sovereignty, the constitution or regulations of the PRC is not accepted or approved by CNPIEC.

Previous Menu Next

Abstract

Related content

Comparison of machine learning models based on time domain and frequency domain features for faults diagnosis in rotating machines

By  

MATEC Web of conference, Vol. 211, Iss. issue, 2018-10 ,pp. : 17009-17009

Edp Sciences

Access to resources Recommend Favorite

Features of technological basing in machining parts on CNC machines

By  

MATEC Web of conference, Vol. 224, Iss. issue, 2018-10 ,pp. : 01078-01078

Edp Sciences

Access to resources Recommend Favorite

An overview of PST for vibration based fault diagnostics in rotating machinery

By  

MATEC Web of conference, Vol. 211, Iss. issue, 2018-10 ,pp. : 01004-01004

Edp Sciences

Access to resources Recommend Favorite

Distinctive Features of Altitude-velocity Characteristics of Detonation Gas Turbine Engines

By  

MATEC Web of conference, Vol. 220, Iss. issue, 2018-10 ,pp. : 03008-03008

Edp Sciences

Access to resources Recommend Favorite

Fault Diagnosis for Rolling Bearings under Variable Conditions Based on Visual Cognition

By Cheng Yujie   Zhou Bo   Lu Chen   Yang Chao  

Materials, Vol. 10, Iss. 6, 2017-05 ,pp. : 582-582

MDPI

Access to resources Recommend Favorite