Data Mining for Fault Diagnosis and Machine Learning for Rotating Machinery

Gang Zhao; Dong Xiang Jiang; Kai Li; Jin Hui Diao

doi:10.4028/www.scientific.net/KEM.293-294.175

Paper Titles

Comparison of Different Methods of Ritz Vectors Extraction
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Considerations for Practical Neural Network Application to a Damage Detection Problem
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Impact Load Identification Based on Local Measurements
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Extracting Gear Fault Features Using Maximal Bispectrum
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Data Mining for Fault Diagnosis and Machine Learning for Rotating Machinery
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Vibration Based Modal Parameters Identification and Wear Fault Diagnosis Using Laplace Wavelet
p.183

Detection of Fatigue Crack Growth in Aircraft Landing Gear, 4 Point Bend Test Specimens
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Detection of Cracks Using Piezoelectric Wafer Active Sensors
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Theoretical Models and Experimental Techniques in Nondestructive Evaluation of Concrete
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HomeKey Engineering MaterialsKey Engineering Materials Vols. 293-294Data Mining for Fault Diagnosis and Machine...

Data Mining for Fault Diagnosis and Machine Learning for Rotating Machinery

Abstract:

Data mining is used not only for database analyses, but also for machine learning. The data mining technique described in this paper was used for steam turbine fault diagnostics based on continuous data measurements. The classification rules are based on standardized vibration frequency data for steam turbines and field experts’ analyses of turbine vibration problems. The expert knowledge enables the steam turbine fault diagnosis system to be more powerful and accurate. The system can identify twenty types of standard steam turbine faults. The system was developed using 2000 simulated data sets. The data mining methods were then used to identify 20 explicit rules for the turbine faults. The method was also used with actual power plant data to successfully diagnose real faults. The results indicate that data mining can be effectively applied to diagnosis of rotating machinery by giving useful rules to interpret the data.

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Periodical:

Key Engineering Materials (Volumes 293-294)

Pages:

175-182

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.293-294.175

Citation:

Cite this paper

Online since:

September 2005

Authors:

Gang Zhao, Dong Xiang Jiang, Kai Li, Jin Hui Diao

Keywords:

Data Mining (DM), Fault Diagnosis, Machinery Learning, Rotating Machinery

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