基于物联网的工厂生产机器损伤检测系统模型的案例推理分析

Marisa Marisa, Suhadi Suhadi, M. Nur, Prima Dina Atika, Sugiyatno Sugiyatno, Davi Afandi
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引用次数: 0

摘要

计算机在工业过程中非常重要,因为它们在公司制造的产品系统的生命周期中发挥着重要作用。由于缺乏详细的定期维护,经常发生生产设备的损坏,使操作人员和技术人员难以维护生产机器。由于它仍然采用人工方法,维修时间长,精度高,成本高。基于案例的推理(Case-Based Reasoning, CBR)是一种基于以往经验并应用于当前的问题解决技术,是人类常用来帮助和促进工作的计算机科学学科之一。CBR用于通过利用或分析先前收集的案例数据来找到解决方案。本研究采用CBR方法来解决这一问题。案例表示、案例索引、案例检索、案例适应和案例维护是案例推理在知识形成中的五大目标。寻找和测量最接近病例的过程称为病例检索。本研究的目的是创建一种在机器上自动检测系统故障的方法,这样,如果基于cbr的系统发生故障,将更容易早期发现,更快地修复,更准确。利用CBR方法进行的研究结果表明,该系统的精度达到90%,与生产工具的测试结果一致,对生产机器的维修管理有效。根据传感器对目标的精度高低,测试误差为20次,最高误差为33.33%,最低误差为0%。本研究的目的是为公司提供分析,以发现生产机器的损坏,为操作员和管理人员提供跟踪现有的损坏。该研究的建议被用于改进生产机器的公司,这些公司需要从现有系统到一个基于计算机的系统进行全面的维修处理,以提高维修机床的成本效率和经济性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Internet of Things-based Analysis of Factory Production Machine Damage Detection System Model Using Case-Based Reasoning Method
Computers are very important in industrial processes because they play a role in the life cycle of the product systems that companies manufacture. Damage to production equipment often occurs due to lack of detailed periodic maintenance, making it difficult for operators and technicians to maintain production machines. Because it still uses a manual approach, the repair time is long and accurate at a high cost. Case-Based Reasoning (CBR), a problem solving technique based on previous experience and applied in the present, is one of the computer science disciplines commonly used by humans to help and facilitate work. CBR is used to find solutions by utilizing or analyzing case data collected previously. In this study to solve the problem by using the CBR method. Case representation, case indexing, case retrieval, case adaptation, and case maintenance are the five goals of CBR in knowledge formation. The process of finding and measuring cases with the greatest closeness is known as case retrieval. The purpose of this research is to create a way to automatically detect system failures on machines, so that if a malfunction occurs with a CBR-based system, it will be easier to detect early, repair faster, and be more accurate. The results of the research using the CBR method are that the accuracy of the system used is 90%, in accordance with the results of testing the tools produced, and effective for managing production machine repairs. While the test error is twenty times with the highest result of 33.33% and the lowest 0% according to the level of accuracy of the sensor on the object. The purpose of this research is to provide analysis for companies to detect damage to production machines for operators and management to follow up on existing damage. Recommendations from the research are used to improve production machines for companies that need a comprehensive repair treatment from the existing system to a system that has computer-based reasoning for cost efficiency and economics of repairing machine tools.
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