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Article summary:
1. An edge computing enabled production anomalies detection and energy-efficient decision approach is proposed for discrete manufacturing workshops.
2. The approach combines energy consumption data with manufacturing system anomalies detection, and can assist production process monitoring and energy conservation.
3. An energy consumption data preprocessing algorithm is established, and a production anomaly analysis model is constructed based on long short-term memory network.
Article analysis:
The article provides a comprehensive overview of the proposed edge computing enabled production anomalies detection and energy-efficient decision approach for discrete manufacturing workshops. The article presents the architecture of the approach in detail, as well as an energy consumption data preprocessing algorithm and a production anomaly analysis model based on long short-term memory network. The article also includes a case study to demonstrate the effectiveness of the proposed method.
The article appears to be reliable and trustworthy overall, as it provides detailed information about the proposed approach, including its architecture, algorithms, and case study results. Furthermore, the article cites relevant research studies to support its claims. However, there are some potential biases that should be noted in this article. For example, the authors may have a vested interest in promoting their own research or technology solutions; thus they may be biased towards presenting only positive aspects of their work without considering any potential drawbacks or risks associated with it. Additionally, while the authors cite relevant research studies to support their claims, they do not explore any counterarguments or alternative approaches that could potentially provide different results or insights into the topic at hand. Finally, while the authors present their own findings from their case study in detail, they do not discuss any other similar studies that could provide additional evidence for their claims or offer alternative perspectives on the issue at hand.