Remaining useful life prediction of mechanical system based on improved adaptive fractional Lévy stable motion with statistical dependence measurement error

Li, Qiang; Li, Hongkun; Ma, Zhenhui; Liu, Xuejun; Guan, Xichun; Zhang, Xiaoli

doi:10.1016/j.ymssp.2023.110646

Cited by 6 publications

(1 citation statement)

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“…Sun et al [22] used a covariance model to fit the degradation process and implemented joint RUL prediction for binary systems based on the Copula function. Li et al [23] established an improved stochastic degradation model that adapts to the variability of indicator degradation trends by updating model parameters. These improved methods have all achieved superior results, and most of the research is based on equipment degradation data.…”

Section: Introductionmentioning

confidence: 99%

The RUL prediction based on improved Wiener degradation model for wet friction components

Wu,

Li,

Wang

et al. 2024

Meas. Sci. Technol.

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The wet clutch is a critical component of the comprehensive transmission system, and its reliability determines the stability of heavy equipment operation. As the core component of the wet clutch, the life of the wet friction components directly affects the reliability of the wet clutch. To accurately predict the wet friction components remaining useful life (RUL), an improved Wiener degradation model is established. Compared to traditional models, it sets drift parameters as random parameters to consider the randomness of degradation process. The life and degradation data of wet friction components are obtained through accelerated life test, and the composite degradation indicator is constructed by screening and fusing degradation data. Innovatively, we fuse life and degradation data to reduce early errors in RUL prediction of wet friction components. The result shows that the RUL prediction average absolute error of the improved Wiener degradation model is 0.31h in the accelerated life range of 80h to 110h, which verifies the accuracy of the model.

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Section: Introductionmentioning

confidence: 99%