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Flow shop failure prediction problem based on Grey-Markov model

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Abstract

Mechanical equipment in the process of operation appears a variety of faults due to different reasons. These faults affect the operation of machinery, causing economic losses, moreover, it may cause accidents, or even casualties. Predicting the nature, degree, development trend and position of mechanical faults is of great significance for making fault early warning, changing scheduling scheme and determining optimal maintenance time. This study aims to propose a generalized mechanical fault prediction method under the condition of short data validity. In the aspect of application, this paper hopes to combine the fault prediction with the shop dynamic scheduling, and constructs the mode of forecasting and optimizing the scheduling plan. The results show that the DGM (1,1) model based on amplitude compression is effective on the prediction of oscillation sequence. Markov chain modification could reduce the error greatly. The feasibility of fault prediction by using Grey-Markov chain has been proven by an illustrative example.

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Acknowledgements

This work was supported by National Natural Science Foundation of China: Research on mining and dynamic optimization of machinery manufacturing process supporting the integration of process planning and workshop scheduling (ID: U1904186), National Natural Science Foundation of China (ID:71801085), Henan Province Soft Science Research Project: Construction and Management Countermeasures of Henan technological innovation center in the new era (ID: 202400410019), Henan Province Soft Science Research Project: Evaluation and Countermeasures of Technology Transfer Status in Henan Province (ID: 202400410211), Henan Province Major project of Applied Research on philosophy and Social Sciences (2018-yyzd-04). The authors declare that there is no conflict of interest with any financial organizations regarding the material reported in this manuscript.

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Authors and Affiliations

  1. School of Management, Henan University of Science and Technology, Luoyang, China

    Kai Guo & Yan Liang

  2. Henan Collaborative Innovation Center of Nonferrous Metals, Luoyang, China

    Kai Guo

  3. Jinan University–University of Birmingham Joint Institute at Jinan University, Guangzhou, China

    Jiyao Zhao

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  1. Kai Guo
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  2. Jiyao Zhao
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  3. Yan Liang
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Correspondence to Kai Guo.

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Guo, K., Zhao, J. & Liang, Y. Flow shop failure prediction problem based on Grey-Markov model. Pers Ubiquit Comput 28, 207–214 (2024). https://doi.org/10.1007/s00779-021-01618-0

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