基于数据的工业制造系统动态可靠性和性能指标估计

Ondo Boniface, Nasso Toumba Richard, Ombété Tsimi Giscard, Kombé Timothée, Elé Pierre
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引用次数: 0

摘要

目的是为复杂工业系统的性能和动态可靠性评估提供一种更简单有效的方法。通过使用工业系统的运行数据,并将其应用于时间序列的人工智能预测算法,该模型将从复杂制造系统的行为中学习,使操作员或决策者能够更好地定位维护、生产和质量政策。此外,我们提出这种方法是为了避免与动态可靠性计算和性能评估相关的繁琐的数学方法,通过识别系统行为中的未来瓶颈来预测公司的长期运营。许多第三代产业表现出低绩效指标和不相关的可靠性,这是由于缺乏有效和简单的可靠性评估工具,考虑到生产链、维修部门、生产部门和质量部门的不同要素的动态方面。我们建议开发一个模型,该模型将从传统的、复杂的、低效的数学方法中抽象出来,用于制造工业中受组合爆炸问题影响的系统。
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Data-Based Estimation of the Dynamic Reliability and Performance Indicator of an Industrial Manufacturing System
The aim is to develop a more simple and effective method's performance and dynamic reliability assessment for complex industrial systems. By using the operating data of the industrial system characterized by a strong desynchronization and applying to it prediction algorithms of artificial intelligence applied to the time series, the model will have learned from the behavior of the complex manufacturing system allowing the operator or decision-maker to better orientate the maintenance, production, and quality policies. Furthermore, we propose this approach to avoid tedious mathematical methods related to dynamic reliability calculations and performance evaluation to make forecasts of the company's operation over a long period by identifying future bottlenecks in the system's behavior. The low-performance indicators and irrelevant reliability presented by many third-generation industries are due to the lack of efficient and simple tools for reliability assessment taking into account the dynamic aspect of the different elements of the production chain, maintenance department, production department, and quality department. We propose to develop a model that will abstract from conventional, complex, and inefficient mathematical methods for systems subject to combinatorial explosion problems in the manufacturing industry.
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