Feature extraction by enhanced time–frequency analysis method based on Vold-Kalman filter. (15th February 2023)

• Record Type: Journal Article
• Authors: Yan, Zhu; Xu, Yonggang; Wang, Liang; Hu, Aijun
• Is Part Of: Measurement. Volume 207(2023)
• Publication Date: 2023-02-15
• Subjects: Feature extraction -- Fault diagnosis -- Time-frequency analysis -- Generalized S-synchroextracting transform -- Vold-Kalman filter; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.112383
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: An enhanced time–frequency analysis method is proposed for fault feature extraction of variable-speed rolling bearings. Enhancing the applicability of generalized S-synchroextracting transform (GS-SET) based on the Vold-Kalman filter (VKF). A method for estimating IF based on an improved synchroextracting operator (ISEO) is proposed. It can characterize the signal in the full range of the time–frequency plane. The enhanced method can effectively characterize the time-varying characteristics of the signal, and the readability is high. Abstract: The time–frequency analysis method can extend a one-dimensional signal to a two-dimensional time–frequency plane, revealing the signal's time-varying characteristics. The time–frequency representation (TFR) obtained by the time–frequency postprocessing algorithm has the characteristics of energy aggregation and high resolution. The generalized S-synchroextracting transform (GS-SET) stands out for its strong adaptability. However, this method cannot obtain effective information when analyzing multicomponent complex signals. We propose an enhanced time–frequency analysis method to solve this problem. First, the multicomponent complex signal is decomposed into multiple mono-component signals by the Vold-Kalman time-varying filtering technique. Second, these signals are processed by the GS-SET method. Last, the obtained TFRs are linearly superimposed to obtain the results of the enhanced method. The simulated signal verifies that the proposed method can effectively represent its time-varying characteristics. The experimental signal of the rolling bearing verifies the practicability of this method.