Extracting Gear Fault Features Using Maximal Bispectrum

Gui Cai Zhang; Jin Chen; Fu Cai Li; Wei Hua Li

doi:10.4028/www.scientific.net/KEM.293-294.167

Paper Titles

Classification Using Radial Basis Function Networks with Uncertain Weights
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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
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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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HomeKey Engineering MaterialsKey Engineering Materials Vols. 293-294Extracting Gear Fault Features Using Maximal...

Extracting Gear Fault Features Using Maximal Bispectrum

Abstract:

Bispectrum is a powerful tool for non-Gaussian signal processing and nonlinearity detection. However, it is difficult to use in practical applications due to that it is a 2-dimensional function. Bispectral slices are widely used reduction methods, and they can only represent a small part of the whole bispectral information. Integrated bispectrum contains more signal features than that of the bispectral slices, whereas the integration will lose the focus of some signal features. To overcome these problems, a new approach called maximal bispectrum is proposed to extract signal features. Maximal bispectrum is obtained by selecting the maximal values of every row of the magnitude bispectrum in the whole bispectral plane and it is a 1-dimensional function. Feature extraction based on maximal bispectrum is investigated and the maximal bispectrum is used to extract features of gear fault. Experimental results indicate that the maximal bispectrum is effective for diagnosing gear crack fault.