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Framework for Customized, Machine Learning Driven Condition Monitoring System for Manufacturing

机译：定制，机器学习驱动条件监测系统的框架

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摘要

Knowledge about the technical state of a complex machine is crucial regarding the reliability and availability within the entire usage phase of a certain technical product. By gathering and gaining information about the exact health condition, production stops can be minimized while optimizing the efficiency of a machine leading to increased customer satisfaction.Since more and more data is available and gets recorded by plenty of sensors, classical statistical methods are not applicable anymore or are just not designed to process such large amount of data. Due to that purpose, many methods from the field of machine learning emerged in the past years. Machine learning in general addresses the issue of recognizing patterns and rules within large amounts of data with the goal to make predictions.Within this paper, the focus is laid upon a detailed step-by-step guide on the development of customized condition monitoring solutions in manufacturing since the application of these still poses a major problem for lots of users.The entire concept of such solutions starting with data collection, through signal-interpretation and index development, finishing with reliable monitoring of the machine state during the manufacturing process is presented throughout. This is done on a real-life application of the concept using synthesized yet realistic data.Moreover, an in-depth analysis of advantages, possibilities and opportunities of the presented concept as well as its weaknesses and boundaries is performed.
机译：关于复杂机器技术状态的知识对于某种技术产品的整个使用阶段内的可靠性和可用性至关重要。通过收集和获取有关确切健康状况的信息，可以最大限度地减少生产停止，同时优化机器的效率导致客户满意度提高。越来越多的数据可获得并通过大量传感器记录，经典统计方法不适用不再或只是不设计用于处理大量数据。由于这种目的，过去几年中出现了机器学习领域的许多方法。机器学习一般地解决了在大量数据中识别模式和规则的问题，以实现预测。在本文的情况下，在详细的逐步指南下奠定了对自定义条件监控解决方案的详细逐步指导由于这些应用程序的应用仍然为大量用户构成了一个主要问题。通过数据收集，通过信号解释和索引开发，在制造过程中通过可靠监测机器状态的可靠监测来实现这种解决方案的整个概念。这是在使用合成但实际数据的概念的真实应用程序中完成的。oreover，对所提出的概念的优点，可能性和机遇以及其弱点和边界进行了深入的分析。

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