Gearbox fault diagnosis of rolling mills using multiwavelet sliding window neighboring coefficient denoising and optimal blind deconvolution

  • Yuan J
  • He Z
  • Zi Y
 et al. 
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Fault diagnosis of rolling mills, especially the main drive gearbox, is
of great importance to the high quality products and long-term safe
operation. However, the useful fault information is usually submerged in
heavy background noise under the severe condition. Thereby, a novel
method based on multiwavelet sliding window neighboring coefficient
denoising and optimal blind deconvolution is proposed for gearbox fault
diagnosis in rolling mills. The emerging multiwavelets can seize the
important signal processing properties simultaneously. Owing to the
multiple scaling and wavelet basis functions, they have the supreme
possibility of matching various features. Due to the periodicity of
gearbox signals, sliding window is recommended to conduct local
threshold denoising, so as to avoid the ``overkill{''} of conventional
universal thresholding techniques. Meanwhile, neighboring coefficient
denoising, considering the correlation of the coefficients, is
introduced to effectively process the noisy signals in every sliding
window. Thus, multiwavelet sliding window neighboring coefficient
denoising not only can perform excellent fault extraction, but also
accords with the essence of gearbox fault features. On the other hand,
optimal blind deconvolution is carried out to highlight the denoised
features for operators' easy identification. The filter length is vital
for the effective and meaningful results. Hence, the foremost filter
length selection based on the kurtosis is discussed in order to full
benefits of this technique. The new method is applied to two gearbox
fault diagnostic cases of hot strip finishing mills, compared with
multiwavelet and scalar wavelet methods with/without optimal blind
deconvolution. The results show that it could enhance the ability of
fault detection for the main drive gearboxes.

Author-supplied keywords

  • Blind deconvolution
  • Gearbox fault diagnosis
  • Multiwavelet denoising
  • Rolling mill

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  • Jing Yuan

  • Zhengjia He

  • Yanyang Zi

  • Han Liu

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