发表机构
Technische Universität Berlin; Siemens Energy Global GmbH & Co. KG（柏林工业大学; 西门子能源全球股份公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对MRO修理过程，提出基于机器学习的智能专家系统SES，用实际工业数据验证其决策支持与架构稳定性。
AI 中文摘要
多家企业将维护、修理和大修（MRO）原则应用于其现有产品的生命周期。在铸造燃气轮机部件产品生命周期管理（PLM）等案例中，频繁维修部件可延长产品的预期寿命，相比新生产部件提供更高的成本效率，甚至可在修理周期内改进部件设计。修理的另一个方面涉及可持续性，因为产品通常含有稀有材料。修理过程产生的排放通常少于开采材料和铸造新部件。为进一步优化修理过程，我们提出了智能专家系统（SES），该系统在整个修理过程中为工程专家提供基于机器学习的决策支持。我们详细阐述了其IT架构，并展示了用于代表性MRO用例的机器学习模型。SES使用来自领先燃气轮机公司的实际行业数据进行评估，并切实满足了关于整体决策支持适用性和所包含IT架构稳定性的既定要求。
英文摘要
Several businesses apply maintenance, repair, and overhaul (MRO) principles to the life-cycle of their existing products. In cases like casted gas turbine component Product Lifecycle Management (PLM), repairing components in frequent intervals can extend the lifetime expectation of the product, provide higher cost efficiency compared to newly produced components, and even improve the part design during the repair cycle. Another aspect of repair concerns sustainability, as products often contain rare materials. The emissions produced by the repair process are usually smaller than mining materials and casting new components. To optimize the repair process further, we propose the Smart Expert System (SES), which assists engineering experts with machine learning-based decision support throughout the repair process. We elaborate on its IT architecture and present machine learning models employed for representative MRO use cases. The SES is evaluated using actual industry data from a leading gas turbine company and demonstrably fulfills formulated requirements concerning the suitability of the overall decision support and the stability of the enclosing IT architecture.
CommentsPublished in: 2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)
DOI：10.1109/IEEM55944.2022.9989791
