An adaptive synchroextracting transform for the analysis of noise contaminated multi-component nonstationary signals

作者：Li, Jiaxin; Mba, David; Li, Xiaochuan; Shang, Yajun; He, Shuai; Lin, Tian Ran^*

来源：APPLIED ACOUSTICS, 2023, 202: 109169.

DOI：10.1016/j.apacoust.2022.109169

• Nonstationary signal
• Time-frequency analysis
• Synchroextracting transform
• Ridge curve identification
• Fault diagnosis

The Synchro-Extracting Transform technique (SET) can capture the changing dynamic in a non-stationary signal which can be applied for fault diagnosis of rotating machinery operating under varying speed or/ and load conditions. However, the time frequency representation (TFR) of a signal produced by SET can be affected by noise contained in the signal, which can largely reduce the accuracy of fault diagnosis. This paper addresses this drawback and presents a new extraction operator to improve the energy concentration of the TFR of a noise contaminated multi-component signal by using an adaptive ridge curve identification process together with SET. The adaptive ridge curve extraction is deployed to extract the signal components of a multi-component signal via an iterative approach. The effectiveness of the algorithm is verified using one set of simulated noise-added signals and two sets of experimental bearing and gearbox defect signals. The result shows that the proposed technique can accurately identify the fault components from noise contaminated multi-component non-stationary machine defect signals.

• 单位
  青岛理工大学