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Application of Machine Learning and Vision for real-time condition monitoring and acceleration of product development cycles

机译：机器学习和愿景在实时条件监测和产品开发周期加速度的应用

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Development work within an experimental environment, in which certain properties are investigated and optimized, requires many test runs and is therefore often associated with long execution times, costs and risks. This can affect product, material and technology development in industry and research. New digital driver technologies offer the possibility to automate complex manual work steps in a cost-effective way, to increase the relevance of the results and to accelerate the processes many times over. In this context, this article presents a low-cost, modular and open-source machine vision system for test execution and evaluates it on the basis of a real industrial application. For this purpose a methodology for the automated execution of the load intervals, the process documentation and for the evaluation of the generated data by means of machine learning to classify wear levels. The software and the mechanical structure are designed to be adaptable to different conditions, components and for a variety of tasks in industry and research. The mechanical structure is required for tracking the test object and represents a motion platform with independent positioning by machine vision operators or machine learning. An evaluation of the state of the test object is performed by the transfer learning after the initial documentation run. The manual procedure for classifying the visually recorded data on the state of the test object is described for the training material. This leads to an increased resource efficiency on the material as well as on the personnel side since on the one hand the significance of the tests performed is increased by the continuous documentation and on the other hand the responsible experts can be assigned time efficiently. The presence and know-how of the experts are therefore only required for defined and decisive events during the execution of the experiments. Furthermore, the generated data are suitable for later use as an additional source of data for predictive maintenance of the developed object.
机译：在实验环境中的开发工作，其中调查和优化某些属性，需要许多测试运行，因此通常与长时间执行时间，成本和风险相关联。这会影响工业和研究中的产品，材料和技术开发。新的数字驱动技术提供了以经济高效的方式自动化复杂的手动工作步骤，以增加结果的相关性并多次加速流程。在此背景下，本文提出了一种低成本，模块化和开源机器视觉系统，用于测试执行，并根据真正的工业应用评估它。为此目的，用于通过机器学习来分类磨损水平的加载间隔自动执行的方法，处理负载间隔，过程文档和对所生成的数据进行评估。该软件和机械结构旨在适应不同的条件，组件和工业和研究中的各种任务。跟踪测试对象所需的机械结构，并且表示具有独立定位的运动平台，通过机器视觉操作员或机器学习。在初始文档运行之后，通过转移学习执行测试对象状态的评估。针对培训材料描述了对测试对象状态的视觉记录数据进行分类的手动过程。这导致材料上的资源效率提高以及人员侧，因为一方面所执行的测试的意义由连续文件增加，另一方面，可以有效地分配负责任的专家。因此，专家的存在和专业知识只需要在执行实验期间定义和决定性事件。此外，所生成的数据适用于以后用作用于预测型对象的预测维护的附加数据源。

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