Improved Time-Frequency Representation for Non-stationary Vibrations of Slow Rotating Machinery
The short-time Fourier transform (STFT) is a staple analysis tool for vibration signal processing due to it being a robust, non-parametric, and computationally efficient technique to analyze non-stationary signals. However, despite these beneficial properties, the STFT suffers from high variance, high sidelobes, and a low resolution. This paper investigates an alternative non-parametric method, namely the sliding-window iterative adaptive approach, to use for time-frequency representations of non-stationary vibrations. This method reduces the sidelobe levels and allows for high resolution estimates. The performance of the method is evaluated on both simulated and experimental vibration data of slow rotating machinery such as a multi-megawatt wind turbine gearbox. The results indicate significant benefits as compared to the STFT with regard to accuracy, readability, and versatility.
How to Cite
Signal processing, Short-time Fourier transform, Non-stationary signals, Non-parametric method, Wind turbine gearbox
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