Numerically controlled (NC) machine tools, as vital production equipment in manufacturing, have been widely applied across various sectors and have become a core competitive advantage for enterprises in the global market. Therefore, ensuring the normal and efficient operation of NC machine tool groups and promptly diagnosing faults have become critical concerns for many enterprises and scholars today. This paper focuses on bearing fault diagnosis, utilizing the vibration signals from the Case Western Reserve University Bearing Data Center as the input dataset. This study constructed a dual-stream convolutional neural network (CNN) fault diagnosis model, where the first stream processes one-dimensional vibration signal spectra and the second stream handles two-dimensional time-frequency maps derived from the same signals. The model uniquely integrates convolutional attention mechanisms to enhance feature extraction along with dropout algorithms and batch normalization to prevent overfitting and improve training stability. The proposed approach enables a comprehensive learning of both temporal and spatial features, effectively identifying bearing faults with high accuracy. The model’s performance was validated against this widely recognized dataset, demonstrating superior accuracy compared to traditional methods.

Lights-out factories represent the pinnacle of manufacturing advancement, utilizing automation, digitalization, and intelligent technologies for unmanned production. These factories employ advanced tools such as process control systems, smart sensors, and industrial robots to enhance efficiency. Constructing lights-out factories is complex, involving multiple disciplines, and there is a lack of comprehensive literature on the subject, posing challenges for interested companies. This paper presents a lights-out factory architecture diagram and reviews key literature on the topic, summarizing relevant technologies and their social impact. The lights-out factory model can help enterprises reduce costs, increase efficiency, improve production quality, enhance safety, and promote high-quality development. However, challenges remain, including high costs, reliability issues, personnel requirements, applicability concerns, and technological hurdles. Future advancements in sensor technology, digital twin technology, and other innovations will drive further development of lights-out factories.

Mission-oriented supply chains involve multi-phase tasks, strong resource interdependencies, and stringent reliability requirements, which make demand planning complex and uncertain. This study develops a structured demand modeling framework to support multi-phase mission-oriented supply chains under budget and reliability constraints by integrating digital twin technology with an adaptive inertia weight particle swarm optimization (AIW-PSO) algorithm. The supply support process is decomposed into four sequential phases—storage, transportation, preparation, and execution—and phase-specific demand models are constructed based on system reliability theory, explicitly incorporating redundancy, maintainability, and repairability. In this work, digital twin technology functions as a data acquisition and virtual experimentation layer that supports parameter calibration, state-aware scenario simulation, and event-triggered re-optimization rather than continuous real-time control. Physical-state updates are mapped to model parameters such as phase durations, failure rates, repair rates, and instantaneous availability, after which the integrated optimization model is re-solved using a warm-start strategy to generate updated demand plans. The resulting multi-phase demand optimization problem is solved using AIW-PSO to enhance global search performance and mitigate premature convergence. The proposed method is validated using a representative mission-oriented supply support scenario with operational and simulated data. Simulation results demonstrate that, under identical budget constraints, the proposed approach achieves higher mission completion capability than conventional PSO-based methods, providing effective and practical decision support for multi-phase mission-oriented supply chain planning.