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张西宁

教授 博士生导师 硕士生导师

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  • 学历: 博士研究生毕业
  • 学位: 博士
  • 职称: 教授
  • 毕业院校: 西安交通大学
  • 学科: 机械工程

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Improved local cepstrum and its applications for gearbox and rolling bearing fault detection

发布时间:2025-04-30
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发布时间:
2025-04-30
论文名称:
Improved local cepstrum and its applications for gearbox and rolling bearing fault detection
发表刊物:
Measurement Science & Technology
摘要:
Cepstrum is a kind of powerful and widely used method in the fields of speech signal processing, echo signal detection, building acoustics, seismic analysis, condition monitoring and fault diagnosis. But in some circumstances such as signals with localized distribution of spectrum or low signal-to-noise ration (SNR) which are heavily drowned by noise, cepstrum fails to provide effective results. An improved local cepstrum is proposed on the basis of local cepstrum. Principle and algorithm of the proposed method are given. By introducing the autocorrelation denoising processing in time domain and in frequency domain individually, the background noise and nonharmonic components in vibration signal and power spectrum are reduced. In the mean time, the highly localized fault features in power spectrum are enhanced and converged to zero frequency in the power spectral autocorrelation function (PSAF). The difficulty of frequency band selection in local cepstrum analysis is solved with the help of spectral negentropy. At the step of transformation from frequency domain to quefrency domain, autoregressive (AR) spectrum estimation is adopted instead of the fast Fourier transform (FFT) for improving fault feature extraction effectiveness. The proposed method is employed to analyze simulated signal and experimental vibration signals of gearbox and rolling element bearing. In contrast to conventional cepstrum and local cepstrum, the proposed method has strong ability to resist noise and is effective to detect the early fault.
合写作者:
Zhang Xining; Zhou Rongtong; Zhang Wenwen
卷号:
30(7)
是否译文:
发表时间:
2019-06-07