Adaptive iterative generalized demodulation for nonstationary complex signal analysis: Principle and application in rotating machinery fault diagnosis

作者：Feng, Zhipeng^*; Chen, Xiaowang

来源：Mechanical Systems and Signal Processing, 2018, 110: 1-27.

DOI：10.1016/j.ymssp.2018.03.004

• Adaptive iterative generalized
• demodulation
• Surrogate test
• Time-frequency analysis
• Rotating machinery
• Nonstationary

Effective identification of signal frequency contents and their time variability is a key to success in rotating machinery fault diagnosis under nonstationary conditions. Fine time frequency resolution and free from both inner and outer interferences are necessary to achieve this purpose. Iterative generalized demodulation (IGD) can separate a nonstationary multi-component signal into constituent mono-components, and derive quality time frequency representation based on Hilbert spectrum of each mono-component, thus offering an effective approach to nonstationary complex signal analysis. Nevertheless, it requires prior expertise knowledge about signal structure to ascertain true constituent components and construct proper demodulation phase functions. This leads to a major difficulty in real signal analysis tasks, where expertise knowledge is usually unavailable and signal time-frequency structure identification via visual observation is susceptible to noise interferences. To address this issue, the capability of surrogate test technique in recognizing true signal components is exploited, and thereby an adaptive iterative generalized demodulation (AIGD) is proposed. Proper demodulation phase functions corresponding to constituent components are derived accordingly, and Hilbert spectra of all constituent mono-components are superposed to generate time-frequency representation (TFR). This method features good merits, such as fine time-frequency resolution and free of both inner and outer interferences. Additionally, it neither relies on prior expertise knowledge about signals, nor needs to construct any basis function. It is highly adaptive to reveal the frequency contents and track their time variability of a signal, and provides an effective approach to nonstationary complex multi-component signal analysis. It is illustrated and validated via numerical simulations, lab experiments of a planetary gearbox and rolling bearings, and on-site tests of a hydraulic turbine in a hydraulic power plant.

• 出版日期2018-9-15
• 单位北京科技大学