A reduced-order time-frequency transform technique for processing nonlinear and non-stationary vibration measurement data

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement Pub Date : 2025-03-20 DOI:10.1016/j.measurement.2025.117327

Javad Isavand , Florian Bossmann , Afshar Kasaei , Andrew Peplow , Jihong Yan

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引用次数: 0

Abstract

This paper introduces a novel time–frequency signal analysis method for analyzing nonlinear and non-stationary signals called the Reduced-order Time-Frequency Transform (RTFT). The RTFT technique offers the capabilities of traditional time–frequency transformations by employing Pearson’s Correlation Coefficient to selectively reduce the data volume in the joint time–frequency domain. This method emphasizes highly correlated frequencies and phases leading to a more efficient data representation without significant loss of accuracy. The RTFT is validated through comparative analysis with established methods, including the Short-Time Fourier Transform (STFT), Hilbert-Huang Transform (HHT), Fourier Synchrosqueezed Transform (FSST), and Wavelet Synchrosqueezed Transform (WSST). Several non-stationary synthesized and real-world vibration-based condition monitoring signals are analyzed using the RTFT and these mentioned methods to demonstrate the superiority of the RTFT in reducing data volume while maintaining accuracy.

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来源期刊

Measurement 工程技术-工程：综合

CiteScore

10.20

自引率

12.50%

发文量

1589

审稿时长

12.1 months

期刊介绍： Contributions are invited on novel achievements in all fields of measurement and instrumentation science and technology. Authors are encouraged to submit novel material, whose ultimate goal is an advancement in the state of the art of: measurement and metrology fundamentals, sensors, measurement instruments, measurement and estimation techniques, measurement data processing and fusion algorithms, evaluation procedures and methodologies for plants and industrial processes, performance analysis of systems, processes and algorithms, mathematical models for measurement-oriented purposes, distributed measurement systems in a connected world.