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Sensitive method for detecting tooth faults in gearboxes based on wavelet denoising and empirical mode decomposition

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Abstract

Many signal processing methods have been developed to detect gear system faults. However, signal noise greatly influences the monitoring process. In addition, useful fatigue information can be misinterpreted by other useless oscillation components in vibration signal and noise. These conditions lead to unclear results that hinder researchers from effectively detecting faults. To overcome this problem, this study first adopts wavelet theory to remove noise and then utilizes the empirical mode decomposition characteristics of the Hilbert-Huang transform to analyze useful Intrinsic mode functions (IMFs) on the basis of signal modulation and correlation theory. Sifted IMFs are then reconstructed as new signals called D-E signals. Finally, Hilbert energy spectrum and kurtosis value are used to complete fault diagnosis. This study compares the proposed method with the Discrete wavelet transform (DWT) method to verify the superiority of the proposed method. Experiment results from using different degrees of gear crack demonstrate that the proposed method is more sensitive in gear fault detection than the DWT method.

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Authors and Affiliations

  1. School of Mechanical Engineering, Inha University, Inchon, 402-751, Korea

    Shuai Jin, Jong-Sik Kim & Sang-Kwon Lee

Authors
  1. Shuai Jin
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  2. Jong-Sik Kim
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  3. Sang-Kwon Lee
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Corresponding author

Correspondence to Sang-Kwon Lee.

Additional information

Recommended by Associate Editor Cheolung Cheong

Shuai Jin is a graduate student in the Department of Mechanical Engineering at Inha University. He studied signal processing and health monitoring.

Sang-Kwon Lee was born in Pusan, Korea, in 1959. He acquired his bachelor’s degree in mechanical engineering from Pusan National University, Pusan, Korea. He received his Ph.D. degree in signal processing from the Institute of Sound and Vibration Research of Southampton University in the UK in 1998. He has accumulated 11 years of experience in automotive noise control by working with Hyundai Motor Co. and Renault-Samsung Motor Company in Korea. He moved to Inha University, Inchon, Korea in 1999. He has continued his sound and vibration research in the Department of Mechanical Engineering at Inha University.

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Jin, S., Kim, JS. & Lee, SK. Sensitive method for detecting tooth faults in gearboxes based on wavelet denoising and empirical mode decomposition. J Mech Sci Technol 29, 3165–3173 (2015). https://doi.org/10.1007/s12206-015-0715-8

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  • DOI: https://doi.org/10.1007/s12206-015-0715-8

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