&nbsp;Adaptive Detrended Fluctuation Analysis as a Feature Extraction Method to Study Gear&rsquo;s Vibration Signal

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Journal of Vibration and Shock ›› 2015, Vol. 34 ›› Issue (9) : 154-157.

Adaptive Detrended Fluctuation Analysis as a Feature Extraction Method to Study Gear’s Vibration Signal

WU Yongheng, XIAO Han

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Abstract

Scaling exponent is often used as the feature quantity for gear fault identification in detrended fluctuation analysis (DFA), but it easily leads to this problem that aliasing is emerged in among the gear failure modes. According to a logarithmic scale wave function diagram, the scaling exponents and the intercept of characterization of signal intensity were combined to a feature vector of gear vibration signal. According to a dual of gear vibration signal, sliding windowing algorithm was projected to extract adaptive scaling exponent, and used for fault classification combining with neural network algorithm. Studies show that the method can improve the extraction accuracy and extraction efficiency of the scaling exponent.

Key words

DFA / Scaling exponent / Long-range correlation / Clustering / Neural Network

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WU Yongheng, XIAO Han . Adaptive Detrended Fluctuation Analysis as a Feature Extraction Method to Study Gear’s Vibration Signal[J]. Journal of Vibration and Shock, 2015, 34(9): 154-157

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