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

Published online by Cambridge University Press:  27 July 2001

QING (CHARLIE) LIU  and
HSU-PIN (BEN) WANG
QING (CHARLIE) LIU
Affiliation:
Senior Research Engineer, Department of Industrial Engineering, Florida A&M University—Florida State University, 2525 Pottsdamer Street, Tallahassee, FL 32310, U.S.A.
HSU-PIN (BEN) WANG
Affiliation:
Professor and Department Chair, Department of Industrial Engineering, Florida A&M University—Florida State University, 2525 Pottsdamer Street, Tallahassee, FL 32310, U.S.A.
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Abstract

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.

Keywords

Data FusionImbalanceMachine DiagnosticsMultiple Sensors
Type
Research Article
Information
AI EDAM , Volume 15 , Issue 3 , June 2001 , pp. 203 - 210
Copyright
2001 Cambridge University Press

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