Collaborative Research: Collaborative Degradation Analysis for Enterprise-Level Maintenance Management via Dynamic Segmentation

在有大量单位运作的许多应用中，出现了对企业级管理的新需求，这需要彻底了解它们的退化模式。虽然最近传感技术的进步提供了前所未有的数据收集机会，但开发所需的企业级框架面临着几个挑战。一种常见的做法是确定一个具有代表性的退化模型，该模型假定所有机组在其整个运行寿命内都是同质的。这种方法抓住了平均特征，但忽略了单元之间的差异和不同单元采取的不同退化路径。考虑到企业一级涉及的单位数量庞大，另一种办法是使每个单位的管理业务个人化，这种做法要么难以处理，要么代价不切实际。该项目将形成一个可执行的综合框架，以了解大量设备的异质退化过程，并指导有限的监测和维护资源的分配。这项研究的结果将使各种经营着大量工作单位的美国制造或生产企业受益。这项研究与为制造企业培养国家下一代工程劳动力的教育努力很好地一致，通过将代表性不足的本科生指导整合到高级研究中，K-12推广计划结合了基础和高级工程设计活动，以及为学生提供与工业现场工程师互动和与国际合作者合作的机会。该项目的目标是为制造企业创建协作预测和健康管理方法。综合框架将通过调查单个单元之间的差异和相似之处来模拟大量单元的异质退化过程：总体特征将由形成企业知识库的可管理数量的规范模型来表示，而个体退化特征将通过动态分段来捕获，该动态分段将对每个单元的退化模式与规范模型之间的相似性进行建模。研究结果将有助于以下科学进展：1)一种新的协同退化建模方法，它可以表征种群和个体水平退化机制中的异质性；2)一种可扩展的传感方法，它可以结合统计预测信息和片段结构来有效地监测大量单元；3)一种企业级维护决策，它可以在进行多单元维修的同时，通过利用成本结构的相互依赖来最小化总体成本。

There is an emerging need for enterprise-level management in many applications where a large number of units operate, which requires thorough understanding of their degradation patterns. While recent advancements in sensing technology provide unprecedented data collection opportunities, developing the desired enterprise-level framework, however, faces several challenges. A common practice is to identify a representative degradation model that assumes the homogeneity of all units throughout their operational life. Such approaches capture average characteristics, but ignore differences among the units and the different degradation paths taken by different units. Another alternative, that of individualizing management operations for each unit, is either intractable or unrealistically costly, given the sheer number of units involved at the enterprise level. This project will lead to an implementable integrated framework for learning heterogeneous degradation processes of a large number of units and guiding the allocation of limited monitoring and maintenance resources. The results from this research will benefit a variety of US manufacturing or production enterprises that operate massive number of working units. This research aligns well with the educational efforts to prepare the nation's next-generation engineering workforce for manufacturing enterprises via integration of underrepresented undergraduate student mentoring into advanced research, K-12 outreach programs incorporating basic and advanced engineering design activities and opportunities for students to interact with field engineers in industry and to partner with international collaborators.The objective of this project is to create a collaborative prognostics and health management methodology for manufacturing enterprises. The integrative framework will model the heterogeneous degradation processes of a large number of units by investigating the differences and similarities among individual units: the population characteristics will be represented by a manageable number of canonical models forming an enterprise knowledge base, whereas the individual degradation characteristics will be captured via dynamic segmentation that models the resemblance between each unit's degradation pattern with the canonical models. The results will contribute to the following scientific advancements: 1) a new collaborative degradation modeling method which can characterize both population-level and individual-level heterogeneities in their degradation mechanism; 2) a scalable sensing method which can incorporate both statistical prognostics information and segment structure for effectively monitoring a large number of units; 3) an enterprise-level maintenance decision-making which can minimize the overall costs by exploiting the interdependency of the cost structure while conducting multi-unit repairs.