A quality-driven stability analysis framework based on state fluctuation space model for manufacturing process

Zhao, Liping; Hu, Sheng; Yao, Yiyong

doi:10.1177/0954408918760903

Cited by 3 publications

(1 citation statement)

References 37 publications

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“…When the process state variables are disturbed by abnormal factors, the final product quality characteristics will fluctuate around the ideal value. 7 It is difficult to control the quality fluctuation caused by multiple variables in the manufacturing process. Xin-chun et al 8 proposed a quality control method based on big data and pattern recognition.…”

Section: Related Work and Motivationsmentioning

confidence: 99%

A method for yarn quality fluctuation prediction based on multi-correlation parameter feature subspace mechanism in spinning process

Hu,

Zhang,

Zhao

et al. 2023

Journal of Engineered Fibers and Fabrics

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Yarn quality is a key indicator in spinning production process, which directly determines the processing quality of the finished fabric. Considering the problems that the yarn quality is difficult to predict due to many influencing factors and widely monitoring dimensions existed. This paper proposes a multi-correlation parameters-based feature subspace model for yarn quality fluctuation prediction method. Firstly, Kernel Principal Component Analysis (KPCA) algorithm is used to analyze the relationship between the relevant parameters affecting yarn quality, and the characteristic subspace of yarn quality fluctuation is reconstructed. Then, a feature subspace-oriented deep learning prediction structure based on Deep Belief Network (DBN) is constructed, and the intelligent prediction model of yarn quality is established by using Particle Swarm Optimization (PSO) algorithm to optimize the modeling parameters. Finally, a case for spinning spun yarn process data set is used to validate the effectiveness of the proposed method, result shows that the proposed method can effectively analyze the multi-correlation parameters fluctuation law, which shows good effect for predicting yarn quality accurately.

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Section: Related Work and Motivationsmentioning

confidence: 99%

A method for yarn quality fluctuation prediction based on multi-correlation parameter feature subspace mechanism in spinning process

Hu,

Zhang,

Zhao

et al. 2023

Journal of Engineered Fibers and Fabrics

Self Cite

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A novel dynamic predictive maintenance framework for gearboxes utilizing nonlinear Wiener process

Dong,

Zhang,

Zhan

et al. 2024

Meas. Sci. Technol.

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In the context of advancing industrial automation, gearboxes, as pivotal components in power transmission systems, have a direct bearing on the operational efficiency and safety of the entire machinery. This study introduces a novel dynamic predictive maintenance (PdM) framework for gearboxes using a nonlinear Wiener process. Comprehensive experiments validate the framework, demonstrating significant reductions in maintenance costs and improvements in reliability. First, a full-life degradation experiment was executed on the gearbox, leveraging the root mean square value of the vibration signal as an indicator of system degradation. Subsequently, the signals from four vibration sensors were synthesized and normalized through kernel principal component analysis, thereby enabling a more nuanced representation of the gearbox’s degradation profile. The degradation trajectory was then modeled using a nonlinear Wiener process framework. The Wiener process’s parameters and state variables were iteratively refined utilizing an online filtering algorithm grounded in Bayesian inference. This facilitated the derivation of the probability density function for the remaining useful life (RUL), thereby enabling a robust prediction of the gearbox’s RUL. Finally, to minimize maintenance costs per unit of time, an optimization model for dynamic maintenance decision-making was formulated. The optimal maintenance timing was ascertained by solving this model. The empirical findings of this investigation demonstrate the efficacy of the proposed approach in executing dynamic PdM for gearboxes. This research endeavors to furnish novel theoretical underpinnings and pragmatic directives for the field of PdM in the context of gearboxes.

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A quality spectra-based SVDD method for multi-dimensional quality fluctuation evaluation in complex industrial process

Hu,

Zhang,

2023

Proceedings of the Institution of Mechanical Engineers, Part E:

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To address the problem that fluctuation caused by multi-dimensional quality-related process variables is difficult to evaluate, this paper proposes a quality spectra-based support vector data description (SVDD) method for multi-dimensional quality fluctuation evaluation in complex industrial process. Firstly, based on quality spectra modelling, a multi-dimensional state feature vector of quality fluctuation is constructed from the perspective of quality spectra and statistical distribution, which is used to characterize the difference between normal and abnormal fluctuation. Then the quality fluctuation evaluation model is established based on SVDD algorithm. On this basis, the spherical distance between the sample to be evaluated and the quality evaluation model is calculated to measure the quality fluctuation degree. Finally, a case for TE process data sets is used to verify the quality fluctuation evaluation approach, it reveals that the proposed method could evaluate the degree of quality fluctuation, which could provide some theoretical guides for quality stability control.

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A quality-driven stability analysis framework based on state fluctuation space model for manufacturing process

Cited by 3 publications

References 37 publications

A method for yarn quality fluctuation prediction based on multi-correlation parameter feature subspace mechanism in spinning process

A method for yarn quality fluctuation prediction based on multi-correlation parameter feature subspace mechanism in spinning process

A novel dynamic predictive maintenance framework for gearboxes utilizing nonlinear Wiener process

A quality spectra-based SVDD method for multi-dimensional quality fluctuation evaluation in complex industrial process

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