STFT spectrogram based hybrid evaluation method for rotating machine transient vibration analysis

The main purpose of this paper is to represent a methodenabling vibration components to be extracted from a high-resolution Short-Time Fourier-Transformation (STFT) based spectrogram assessed as an image to support transient analysis on rotating machines. Therefore, an improved STFT algorithm was developed to allocate and utilize computational memory more efficiently. The resulting spectrogram was compressed into a grey-scale image without any kind of information loss and was used for further image processing methods to obtain details about the vibration components. Furthermore, differential and moving average predictive tracking algorithms were developed for frequency ridge evaluation in the spectrogram image. For further analysis, the obtained results were transformed back with an inverse transformation method from image-space to time-frequency plane. Moreover, these results are able to be used to estimate the speed of rotation of the machine and to observe the frequency components. The methods were tested and validated with simulated signals and transient measurements on rotating machines. With the combination of vibration signal- and image processing techniques the evaluation time and computational resource requirements are decreased enhancing more efficient and accurate analysis, nevertheless opens the possibility of a real-time condition monitoring based on a basic vibration measurement. (C) 2021 The Authors. Published by Elsevier Ltd.

Automated summary

The paper introduces a method to extract vibration components from high-resolution STFT spectrograms for transient analysis on rotating machines. An improved STFT algorithm efficiently allocates and utilizes computational memory. The resulting grey-scale image is used for further analysis, with the possibility of real-time condition monitoring based on basic vibration measurement.