A Novel RLS-KS Method for Parameter Estimation in Particle Filtering-Based Fatigue Crack Growth Prognostics

Liu, Xiaopeng; Zhang, Weifang; Liu, Xuerong; Dai, Wei; Fu, Guicui

doi:10.1109/access.2019.2948291

Cited by 7 publications

(4 citation statements)

References 48 publications

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“…Artificial neural networks [25,26] Support vector machines [27,28] Bayesian [29,30] Deep learning [31,32] methods rely on the degradation or failure mechanisms of the equipment to establish mathematical expressions describing the degradation process, thereby achieving RUL prediction through these expressions [1][2][3]. However, as systems continue to scale up, structures become increasingly complex, and inter-device coupling strengthens, establishing precise physical models tailored to specific critical equipment poses significant challenges.…”

Section: Statistical Model-based Prediction Methodsmentioning

confidence: 99%

Research on bearing remaining useful life anti-noise prediction based on fusion of color-grayscale time-frequency features

Jia,

An,

et al. 2024

Meas. Sci. Technol.

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In contemporary industrial processes, vibration signals collected from bearings often contain significant noise, challenging the efficacy of conventional predictive models in extracting critical degradation features and accurately predicting the remaining useful life (RUL) of bearings. Addressing these challenges, this paper introduces a novel method for predicting bearing RUL under noisy conditions, leveraging a dual-branch multi-scale convolutional attention network (DMCSA) integrated with a dense residual feature fusion network (DRF). Initially, the method applies continuous wavelet trans-form (CWT) to vibration signals to extract color time-frequency image data, followed by grayscale processing to construct a comprehensive color-grayscale time-frequency image dataset, thereby augmenting the model's input features. Enhanced channel and spatial attention mechanisms, combined with multi-scale convolutions, facilitate supe-rior feature extraction and selection. The model's resilience to noise is fortified by in-corporating noise into the training dataset. Subsequently, selected color-gray time-frequency features undergo fusion and relearning through the DRF framework at the model's backend. The crayfish optimization algorithm (COA) is deployed for the astute determination of the model's critical hyperparameters. The proposed DMCSA-DRF model is then applied to predict the health indicator (MSCA-DRF-HI) of the test dataset, culminating in the accurate prediction of the bearings' RUL. Validation experiments demonstrate that our method surpasses comparative models in prediction accuracy un-der diverse noise interferences, signifying a substantial advancement in predictive performance.

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Section: Statistical Model-based Prediction Methodsmentioning

confidence: 99%

Research on bearing remaining useful life anti-noise prediction based on fusion of color-grayscale time-frequency features

Jia,

An,

et al. 2024

Meas. Sci. Technol.

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“…During the fatigue test, the Lamb wave monitoring signals corresponding to different crack lengths were collected. As shown in Figure 1, the S0 wave packet from t 1 to t 3 is intercepted for analysis (Liu et al, 2019). The damage index DI based on the correlation coefficient is used to quantitatively characterize the influence of the crack length on the Lamb wave signals:…”

Section: Observation Equation Based On the Lamb Wave Signalsmentioning

confidence: 99%

Prediction of fatigue crack propagation in metals based on IBAS-PF

Wensheng

Zhenhua

Jiefei

et al. 2022

Front. Built Environ.

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Owing to particle leanness, the standard Particle Filter (PF) algorithm is prone to the problem of reduced prediction accuracy when predicting fatigue crack propagation. An improved particle filter algorithm based on the optimization algorithm of beetle antenna search (IBAS-PF) for fatigue crack propagation in metals is proposed in this paper. The discrete Paris formula was used to establish the state equation of fatigue crack propagation, in which the uncertainty of material and crack propagation process were considered. Meanwhile, the characteristics of Lamb wave signals under different crack lengths were extracted to establish the observation equation. The sampling process of the PF algorithm was optimized based on the beetle antennae search algorithm to improve the particle diversity and the prediction accuracy. Compared with the standard PF algorithm, the improved BASO-PF algorithm has higher accuracy for metal fatigue crack propagation, as well as better state estimation ability.

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“…12 Effective prediction of tool RUL has been achieved by optimizing particle filter-based fatigue crack growth analysis. 13 However, building physical models relies on extensive expert knowledge. More critically, models defined based on 1 School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China tool geometry and cutting parameters cannot be updated in real time with online monitoring, which makes them unsuitable for online analysis and prediction scenarios that need to process a large amount of monitoring data.…”

Section: Introductionmentioning

confidence: 99%

Tool remaining useful life prediction considering wear state based on hybrid attention network

Wu,

Li,

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part B:

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Accurate prediction of the remaining useful life for the cutting tool is a key part of the predictive maintenance of computer numerical control machines. However, the wide variety of tools makes the process of modeling different tool wear regularities redundant and cumbersome. In addition, it is difficult to deal with the input characteristics of multi-sensor monitoring signals in a targeted manner. To solve the above problems, a hybrid predictive model with squeeze-and-excitation (SE) module is proposed. Combined with adaptive feature extraction based on convolutional neural network and observation based on bidirectional gated recurrent unit, accurate multivariate regression prediction is achieved. The SE module enhances the focus on crucial features. Finally, through the design of the tool wear experiment and the combination of the public dataset, the accuracy and generalization ability of the proposed model are verified under different tool types and different working conditions.

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A Novel RLS-KS Method for Parameter Estimation in Particle Filtering-Based Fatigue Crack Growth Prognostics

Cited by 7 publications

References 48 publications

Research on bearing remaining useful life anti-noise prediction based on fusion of color-grayscale time-frequency features

Research on bearing remaining useful life anti-noise prediction based on fusion of color-grayscale time-frequency features

Prediction of fatigue crack propagation in metals based on IBAS-PF

Tool remaining useful life prediction considering wear state based on hybrid attention network

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