Characteristic enhancement of RV reducer under non-stationary condition based on WDT-IF-SS-VMD

Xie, Wensong; Zhou, Jun; Liu, Tao

doi:10.1088/1361-6501/ace071

Cited by 2 publications

(1 citation statement)

References 34 publications

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“…A novel two-level chirp mode decomposition approach was proposed in [21] to decompose complex AM-FM signals with strong noise. A fault diagnosis method for RV reducer was proposed in [22], which combines instantaneous frequency trend graph based on current signal and the parameter adaptive VMD 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%

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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A variable‐speed‐condition fault diagnosis method for crankshaft bearing in the RV reducer with WSO‐VMD and ResNet‐SWIN

Qiu,

Nie,

Peng

et al. 2024

Quality & Reliability Eng

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Due to the noise interference and the weak characterization ability of the fault vibration signal of rotation vector (RV) reducer crankshaft bearing, it is difficult to obtain satisfactory results for the available fault diagnosis methods. For that, this paper proposes a variable‐speed‐condition fault diagnosis method with WSO‐VMD and ResNet‐SWIN. A signal reconstruction method with WSO‐VMD was carried out, Firstly, the performance of VMD algorithm is improved by using war strategy optimization algorithm to select parameters adaptively. Then the signal is reconstructed considering the fault characteristic frequency, so as to realize the noise reduction of the signal. By using the residual network module and attention mechanism to replace the first stage of the original SWIN model, a novel ResNet‐SWIN fault diagnosis model is established to enhance the feature extraction ability for the weak signal. The experiments with the constant‐operating‐condition and the variable‐operating‐condition are carried out to verify the effectiveness of the proposed method. The results show that, whether at variable‐speed or constant‐speed conditions, WSO algorithm has been proven to be the fastest convergence speed compared with WOA, SSA, and NGO optimization algorithms, and by the signal reconstruction with WSO‐VMD, the variance evaluation indicator of the reconstructed signal has 36%, 21%, 46%, and 40%, respectively. ResNet‐SWIN model has achieved the optimal diagnosis accuracy compared with SWIN, VIT, and CNN‐SVM models in both variable‐speed and constant‐speed conditions.

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Characteristic enhancement of RV reducer under non-stationary condition based on WDT-IF-SS-VMD

Cited by 2 publications

References 34 publications

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

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

A variable‐speed‐condition fault diagnosis method for crankshaft bearing in the RV reducer with WSO‐VMD and ResNet‐SWIN

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