A case study on multisensor data fusion for imbalance diagnosis of rotating machinery

Techniques for machine condition monitoring and diagnostics are gaining acceptance in various industrial sectors. They have proved to be effective in predictive or proactive maintenance and quality control. Along with the fast development of computer and sensing technologies, sensors are being incre...

Full description

Saved in:
Bibliographic Details
Published inAI EDAM Vol. 15; no. 3; pp. 203 - 210
Main Authors LIU, QING (CHARLIE), WANG, HSU-PIN (BEN)
Format Journal Article
LanguageEnglish
Published Cambridge Cambridge University Press 01.06.2001
Subjects
Data Fusion
Imbalance
Machine Diagnostics
Multiple Sensors
Machine Diagnostics
Data Fusion
Imbalance
Multiple Sensors
Online AccessGet full text

Cover

More Information
Summary:Techniques for machine condition monitoring and diagnostics are gaining acceptance in various industrial sectors. They have proved to be effective in predictive or proactive maintenance and quality control. Along with the fast development of computer and sensing technologies, sensors are being increasingly used to monitor machine status. In recent years, the fusion of multisensor data has been applied to diagnose machine faults. In this study, multisensors are used to collect signals of rotating imbalance vibration of a test rig. The characteristic features of each vibration signal are extracted with an auto-regressive (AR) model. Data fusion is then implemented with a Cascade-Correlation (CC) neural network. The results clearly show that multisensor data-fusion-based diagnostics outperforms the single sensor diagnostics with statistical significance.
Bibliography:istex:A55B30DDC2680D83D6CE7982F9A8E962249066F3
ark:/67375/6GQ-01VN2G92-W
PII:S0890060401153011
ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0890-0604
1469-1760
DOI:10.1017/S0890060401153011