Details

Autor(en) / Beteiligte

• Sun, Huer
• Liang, Fuwang
• Liu, Yutao
• Liu, Kexin
• Wang, Zhijian
• Zhang, Tianyuan
• Zhu, Jiyang
• Zhao, Yang

Titel

Application of a Novel Improved Adaptive CYCBD Method in Gearbox Compound Fault Diagnosis

Ist Teil von

• IEEE access, 2021, Vol.9, p.133835-133848

Ort / Verlag

Piscataway: IEEE

Erscheinungsjahr

2021

Link zum Volltext

Quelle

EZB Free E-Journals

Beschreibungen/Notizen

• Recently, cyclostationary blind deconvolution (CYCBD) is often used in gearbox fault feature extraction, and it is more effective in recovering single periodic pulse. However, in the engineering application of CYCBD, the filter length (<inline-formula> <tex-math notation="LaTeX">L </tex-math></inline-formula>) and cycle frequency (<inline-formula> <tex-math notation="LaTeX">\alpha </tex-math></inline-formula>) need to be verified and set through a large number of experiments, and the efficiency is very low; Moreover, the effect is not good when it is used to extract composite fault features under the background of strong noise. In order to overcome the above limitations, empirical mode decomposition (EEMD) is used to preprocess the composite fault. EEMD can remove the high-frequency noise and weak correlation components in the sampled signal. The strong correlation component is reconstructed to obtain the modal function closer to the fault frequency. The chimpanzee intelligent algorithm is applied to the determination of <inline-formula> <tex-math notation="LaTeX">L </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">\alpha </tex-math></inline-formula> of CYCBD by optimization to form an adaptive CYCBD. Adaptive CYCBD takes the dispersion entropy of envelope spectrum as the fitness function of chimpanzee optimization algorithm (CHOA), and finds the optimal <inline-formula> <tex-math notation="LaTeX">L </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">\alpha </tex-math></inline-formula> through iteration. The optimal parameter value is applied to CYCBD, and the reconstructed modal function is deconvoluted to obtain the optimal inverse filter, so as to accurately separate the fault characteristic components. Simulation and experimental results show that this method is effective for gearbox composite fault diagnosis and extraction under strong noise background.