Aiming at the problems of frequent failure of powertrain of new energy vehicles under complex working conditions, delayed early warning and high cost of traditional operation and maintenance methods, a set of fault diagnosis and predictive maintenance system driven by digital twin (DT) is constructed in this paper. Firstly, a multi-physical field coupling model of powertrain integrating electromagnetic, temperature and vibration fields is established, and a synchronization and fusion mechanism of virtual and real data is designed to realize high-precision virtual and real mapping. Secondly, a CNN-LSTM hybrid model is proposed to complete the fault feature extraction and pattern recognition, and combined with the degradation model to achieve accurate prediction of remaining service life (RUL). Finally, a decision-making model aiming at minimizing maintenance cost is constructed to optimize maintenance strategy. The accuracy of the proposed CNN-LSTM model is over 97.83%, and the MAPE predicted by RUL is as low as 2.35%.The maintenance cost is reduced by 30.6% and the failure rate is reduced by 56.2%. The research results provide an efficient technical scheme for intelligent operation and maintenance of powertrain of new energy vehicles.
Jibin Sun, Yun-Ming Zhang, Jinpeng Gong et al.· International Conference on...· 0 citations
Carbon catabolite repression (CCR) is a widespread regulatory strategy across diverse microorganisms that prioritises the utilisation of preferred carbon sources. In industrial bioprocesses, however, CCR-mediated substrate hierarchy can delay the utilisation of secondary carbon sources in mixed feedstock, thereby extending fermentation time and limiting carbon conversion efficiency toward target products. Existing reviews have largely addressed CCR in a microorganism-specific manner, potentially obscuring conserved principles, limiting cross-species comparisons, and constraining generalisable engineering; computational modelling and engineering applications also remain underrepresented. This review therefore first summarises CCR mechanisms across diverse microorganisms through a common systems-level framework, in which transport-linked sensing converts carbon flux into intracellular signals, transcriptional regulators reprogramme genome-wide expression, RNA-based mechanisms fine-tune the timing and magnitude of responses, and protein stability shapes their persistence. Multi-omics approaches, particularly interactomics and single-cell omics, are then highlighted for revealing CCR as a dynamic and multilayered regulatory system by resolving interaction networks, temporal changes, and cell-to-cell heterogeneity beyond bulk measurements. Quantitative computational modelling is also discussed, with emphasis on kinetic and constraint-based frameworks that connect regulatory mechanisms to metabolic flux and predict system behaviour under changing carbon substrates. Engineering strategies, including adaptive laboratory evolution, transcription factor engineering, transporter and promoter engineering, and pathway rewiring, are further compared for the relief of CCR and improvement of substrate co-utilisation and production performance. Finally, these advances are presented within a Design-Build-Test-Learn framework, through which mechanistic discovery and omics-scale analysis are used to inform model development, and modelling and engineering are iteratively coupled to improve microbial performance. We propose that such an integrative paradigm is emerging as a unifying direction for CCR research, and provides a foundation for rational strain design and more efficient, sustainable biomanufacturing.
Mengtian Lu, Haoran Ding, Hong-Yu Zhang et al.· Synthetic and Systems Biotec...· 0 citations
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