Improved multi-order Vold-Kalman filter for order tracking analysis using split cosine and sine terms

Alsalaet, Jaafar

doi:10.1016/j.measurement.2023.113901

Cited by 3 publications

(1 citation statement)

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“…An adaptive multivariate VMD combined with an improved deep discrimination transfer learning network is proposed in [18] for vibration signal decomposition and fault diagnosis of wind turbines under variable working conditions. In [19], an enhanced Vold-Kalman filter for signal decomposition was presented using separated cosine and sine kernels. VMD was used in [20] to separate the components unrelated to the resonance based on the parameters optimized using the artificial bee colony algorithm.…”

Section: Introductionmentioning

confidence: 99%

Multicomponent collaborative time-frequency state-space model for vibration signal decomposition under nonstationary conditions

Huang,

Chen,

et al. 2024

Meas. Sci. Technol.

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The implementation of Prognostics and Health Management (PHM) strategies is essential for enhancing the safety and maintenance of rotating equipment in chemical plants. The examination of vibration signal behaviours under variable-speed conditions and the development of signal decomposition methods in such contexts are of substantial theoretical and practical relevance. This paper proposes a novel multicomponent collaborative time-frequency state-space method for the decomposition of vibration signals. The method employs a multi-component proportional model to accurately describe the synchronisation of high-frequency components with the rotational frequency component. Based on this framework, the instantaneous frequency change curves of multiple components are input into the Vold-Kalman filter algorithm for precise decomposition of multicomponent vibration signals under variable-speed conditions. Experimental results demonstrate the effectiveness of the proposed method in achieving accurate instantaneous frequency tracking and signal decomposition, showing clear advantages over traditional methods.

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Section: Introductionmentioning

confidence: 99%