工業 4.0 的實施帶來了新技術，例如網絡物理系統 (CPS)，可實現對物理機器的數字控制。 該 CPS 可以更精確地控制和監控製造過程，從而能夠準確預測機器性能和故障。 例如，預測性維護可以預測機器故障並深入了解根本原因。 語義的使用還有助於理解製造系統生成的大量數據。 然而，由於一個或多個組件不可用而導致的製造系統性能下降仍然是一個挑戰。 雖然一些研究通過高精度預測模型或緩解策略提出了解決方案，但這些方法之間缺乏整合。 因此，本研究旨在模擬一個結合了預測性維護、按需自動負載平衡和語義的集成系統。
使用 Python 創建的模擬模擬了真實工業機器的行為和關係。 它提供類似於真實機器的合成數據，並使用語義來標準化模擬中的數據通信。 這種語義網絡網格類似於知識圖譜，包含有關機器當前狀態的信息。 因此，集成系統可以預測機器故障，並通過將工作負載分配給其他機器來自動降低對整體系統性能的影響。 與傳統的非預測、非緩解製造系統相比，該仿真表明系統吞吐量有了顯著提高。 此外，仿真表明集成系統增加了系統正常運行時間和機械利用率。

The implementation of Industrial 4.0 brings about new technologies, such as Cyber-Physical Systems (CPS), that enable digital control of physical machines. This CPS results in more precise control and monitoring of the manufacturing process, enabling accurate predictions of machine performance and failure. Predictive maintenance, for instance, can predict machine failures and provide insight into the root causes. The use of semantics also helps to make sense of the large amounts of data generated by the manufacturing system. However, the reduced performance of a manufacturing system due to the unavailability of one or more components remains a challenge. While some studies have proposed solutions through high-accuracy prediction models or mitigation policies, there is a lack of integration between these approaches. Therefore, this study aims to simulate an integrated system that combines predictive maintenance, on-demand automatic load balancing, and semantics.
The simulation, created using Python, mimics the behavior and relationships of real industrial machines. It provides synthetic data that resembles real machines and uses semantics to standardize data communication within the simulation. This semantic network mesh, similar to a knowledge graph, contains information about the machine's current state. Thus, the integrated system can predict machine failures and automatically reduce the impact on overall system performance by distributing the workload to other machines. The simulation demonstrates a significant improvement in system throughput compared to traditional non-predictive, non-mitigation manufacturing systems. Additionally, the simulation shows that the integrated system increases system uptime and mechanic utilization.

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