基于时态数据采掘的汽车装配线故障分析
Temporal data mining for root-cause analysis of machine faults in automotive assembly lines
摘要：
发动机装配是一个复杂的、高度自动化的分布式控制过程，每天都要记录大量的故障数据。我们描述了一个应用程序的时间数据采掘分析故障日志在发动机装配厂。频繁事件发现框架是一种无模型的方法，可用于以有效的方式从日志中推断事件之间的（时间）相关性。除了在理论上优雅和计算效率高之外，频繁发作也很容易以可操作建议的形式解释。结合域特定的信息是成功应用的方法，用于分析故障日志在制造领域的关键。我们展示了如何使用启发式规则，作为前过滤器和后过滤器频繁的插曲发现特定领域的知识可以被纳入。这里描述的系统目前正在通用汽车公司的一个发动机装配厂使用，并计划在其他工厂进行调整。据我们所知，本文提出了第一个真正的，大规模的应用程序的时间数据采掘在制造领域。我们相信，本文中提出的想法可以帮助从业者工程师在其他类似或相关的应用领域的分析工具。
英文摘要：
Engine assembly is a complex and heavily automated distributed-control process, with large amounts of faults data logged everyday. We describe an application of temporal data mining for analyzing fault logs in an engine assembly plant. Frequent episode discovery framework is a model-free method that can be used to deduce (temporal) correlations among events from the logs in an efficient manner. In addition to being theoretically elegant and computationally efficient, frequent episodes are also easy to interpret in the form actionable recommendations. Incorporation of domain-specific information is critical to successful application of the method for analyzing fault logs in the manufacturing domain. We show how domain-specific knowledge can be incorporated using heuristic rules that act as pre-filters and post-filters to frequent episode discovery. The system described here is currently being used in one of the engine assembly plants of General Motors and is planned for adaptation in other plants. To the best of our knowledge, this paper presents the first real, large-scale application of temporal data mining in the manufacturing domain. We believe that the ideas presented in this paper can help practitioners engineer tools for analysis in other similar or related application domains as well.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
