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Event-based fault detection of manufacturing cell: Data inconsistencies between academic assumptions and industry practice

机译:基于事件的制造单元故障检测:学术假设和行业实践之间的数据不一致

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Some problems with event-based faults in manufacturing systems cannot be handled by existing fault detection solutions, including finding faults in event-based data for systems for which limited information is known. A new fault detection solution that finds faults in event-based data using model generation is presented here. This solution assumes that some information is known about the system from its design information and data structure. An example application of this solution is presented for a Ford machining cell that has been experiencing a gantry waiting problem. In the course of this example application, five inconsistencies were found between relatively common academic assumptions made by this fault detection solution (as well as others) and the actual cell's set-up and data. These inconsistencies and possible means of addressing them are discussed. Some of these means to resolve the inconsistencies have been implemented, and preliminary results in generating models using the fault detection solution are presented.
机译:在制造系统与基于事件的故障的一些问题不能由现有的故障检测方案,包括找到在基于事件的数据的故障,用于其有限的信息是已知的系统来处理。该发现使用模型生成的基于事件的数据故障故障检测新解决方案是这里提出。这种解决方案假定一些信息是已知的关于从其设计信息和数据结构的系统。这种解决方案的示例性应用被呈现为已经历的机架等待问题福特加工单元。在这个例子中应用的过程中,通过该故障检测溶液(以及其他)由相对常见学术假设和电池实际的设置和数据之间发现有不一致。这些不一致和解决这些可能的手段进行了讨论。一些手段来解决不一致的问题已得到落实,并且在使用故障检测解生成模型的初步结果。

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