Abstract
During the operation of the equipment, a large amount of data that can reflect whether the equipment is in good condition will be generated to a certain extent, but these data are discrete and unprocessed, and the operation status of the equipment cannot be judged. In order to solve the maintenance of abnormal faults in the production equipment during the operation and processing of the intelligent production line, predict the state of the equipment at the next moment in time, implement the equipment for pre-maintenance, and ensure the efficient operation of the equipment, this paper proposes a method on proposes status prediction of production equipment based on digital twins and multidimensional time series. First, by collecting the operation data and preprocessing of the production equipment, a digital twin model of the equipment’s state is established; secondly, the sensitive feature information is extracted from a large amount of historical state data of the equipment and combined with the digital twin to model the state of the running process, and construct a predictive analysis model based on multi-dimensional time series, effectively predict the operating status of production equipment and realize fault early warning. Finally, the case of a numerical control machine tool in this paper shows that this method can effectively visually monitor and predict the running state of automated equipment, and provide a valuable method for equipment maintenance.
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Acknowledgements
The authors would like to express appreciation to mentors in Shanghai University for their valuable comments and other helps. Thanks for the pillar program supported by Shanghai Economic and Information Committee of China (No. 2019-GYHLW-01012).
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Miao, Q., Liu, L., Chen, C., Wan, X., Xu, T. (2021). Research on Operation Status Prediction of Production Equipment Based on Digital Twins and Multidimensional Time Series. In: Wang, Y., Martinsen, K., Yu, T., Wang, K. (eds) Advanced Manufacturing and Automation X. IWAMA 2020. Lecture Notes in Electrical Engineering, vol 737. Springer, Singapore. https://doi.org/10.1007/978-981-33-6318-2_31
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DOI: https://doi.org/10.1007/978-981-33-6318-2_31
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