A meta-learning approach to select appropriate prognostic methods for the predictive maintenance of digital manufacturing systems

一种元学习方法，用于选择适当的预测方法来进行数字制造系统的预测维护

• 批准号: 418821892
• 负责人: Professor Dr.-Ing. Michael Freitag
• 金额: --
• 依托单位: BIBA - Bremer Institut für Produktion und Logistik GmbH an der Universität Bremen
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/418821892?language=en
• 关键词: meta; learning; approach; select; appropriate

Maintenance planning is of paramount importance for manufacturing companies in order to assure a constant availability of their machines and to avoid high repair costs. The technological developments of the last years, which can be summarized under the terms Industry 4.0 and Advanced Digitalization, offer new potentials to describe the state of a digitalized manufacturing system in real-time. This provides a basis for moving from classical reactive or periodic maintenance planning to more efficient condition-based and predictive maintenance. However, the suitability and performance of prognostic methods to predict the future failure behavior of machines and components depends strongly on the state of a machine, its components and their configuration. Hence, the suitability can change over time. Despite the large amount of research regarding predictive maintenance in the last years, there is a clear research gap regarding a method to select appropriate prognostic methods for predictive maintenance depending on the current state of a manufacturing system and its machines.The objective of this joint research project is to develop a meta-learning system that selects suitable prognostic methods for predictive maintenance depending on the current state of a manufacturing system using sensor data of the machines. The project will be jointly conducted by three research groups at (i) BIBA - Bremer Institut für Produktion und Logistik at the University of Bremen, Germany, (ii) the Federal University of Santa Catarina (UFSC), Florianopolis, Brazil, and (iii) the Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, Brazil, who have complementary research profiles. The research group at BIBA will develop a meta-learning method to select suitable prognostic methods based on historical and sensor data. The research group at UFSC will develop an integrated production and maintenance planning method. The research group at UFRGS will develop a service-oriented architecture to assure an appropriate data-exchange between a manufacturing system and the prognostic as well as the planning method. Integrating these different modules, the result of the proposed project will be a meta-learning predictive maintenance system that will be capable to (i) use sensor data of digitalized manufacturing systems to describe the system state in real-time, (ii) select suitable prognostic methods dynamically based on the state of a machine and it’s components, (iii) compute an integrated production and maintenance plan and (iv) evaluate the performance of the selected prognostic methods as well as the subsequent planning decisions by their forecast errors and, in addition, by using logistic key performance indicators such as machine utilization and throughput times. In this way, the meta-learning predictive maintenance system will help a manufacturing company to achieve a better production and maintenance planning and an increased production performance.

维护计划对于制造公司来说至关重要，以便确保机器的持续可用性并避免高昂的维修成本。过去几年的技术发展可以概括为工业4.0和先进数字化，为实时描述数字化制造系统的状态提供了新的潜力。这为从传统的反应性或定期维护计划转向更有效的基于状态和预测性维护提供了基础。然而，预测机器和部件未来故障行为的预测方法的适用性和性能在很大程度上取决于机器的状态、其部件及其配置。因此，适应性可能会随着时间的推移而变化。尽管在过去的几年里有大量关于预测性维护的研究，在根据制造系统及其机器的当前状态选择适当的预测方法进行预测性维护方面，存在明显的研究空白。学习系统，其使用机器的传感器数据根据制造系统的当前状态选择用于预测性维护的合适的预测方法。该项目将由以下三个研究小组联合开展：（i）德国不莱梅大学的BIBA - Bremer Institut für Produktion und Logistik，（ii）巴西弗洛里亚诺波利斯的卡塔里纳联邦大学（UFSC），（iii）巴西阿莱格雷波尔图的南里奥格兰德联邦大学（UFRGS），他们具有互补的研究概况。BIBA的研究小组将开发一种元学习方法，根据历史和传感器数据选择合适的预测方法。UFSC的研究小组将开发一种集成的生产和维护计划方法。UFRGS的研究小组将开发一个面向服务的体系结构，以确保制造系统和预测以及规划方法之间的适当数据交换。整合这些不同的模块，所提出的项目的结果将是一个元学习预测维护系统，该系统将能够（i）使用数字化制造系统的传感器数据来实时描述系统状态，（ii）基于机器及其组件的状态动态选择合适的预测方法，（iii）计算综合生产和维护计划，以及（iv）通过预测误差评估所选预测方法的性能以及随后的规划决策，此外，通过使用物流关键性能指标，如机器利用率和吞吐时间。通过这种方式，元学习预测维护系统将帮助制造企业实现更好的生产和维护计划，并提高生产性能。