Machine learning and artificial intelligence provide methods and algorithms to take advantage of sensor and actuator data in automated production systems. Product quality monitoring is one of the promising applications available for data-driven modeling, particularly in cases where the quality parameters cannot be measured with reasonable effort. This is the case for defects such as cracks in workpieces of hydraulic metal powder presses. However, the variety of shapes produced at a powder press requires training of individual models based on a minimal sample size of unlabeled data to adapt to changing settings. Therefore, this article proposes an unsupervised product quality monitoring approach based on dynamic time warping and non-linear regression to detect anomalies in unlabeled sensor and actuator data. A preprocessing step that isolates only the relevant intervals of the process is further introduced, facilitating efficient product quality monitoring. The evaluation on an industrial dataset with 37 samples, generated in test runs, shows a true-positive rate for detected product quality defects of 100% while preserving an acceptable accuracy. Moreover, the approach achieves the output within less than 10 seconds, assuring that the result is available before the next workpiece is processed. In this way, efficient product quality management is possible, reducing time- and cost-intensive quality inspections.

中文翻译：

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使用最少的传感器和执行器数据样本监控液压机中的产品质量

机器学习和人工智能提供了在自动化生产系统中利用传感器和执行器数据的方法和算法。产品质量监控是可用于数据驱动建模的有前途的应用之一，特别是在无法通过合理努力测量质量参数的情况下。液压金属粉末压机的工件裂纹等缺陷就是这种情况。然而，粉末压机生产的各种形状需要基于未标记数据的最小样本大小来训练单个模型，以适应不断变化的设置。因此，本文提出了一种基于动态时间扭曲和非线性回归的无监督产品质量监控方法，以检测未标记传感器和执行器数据中的异常。进一步引入了仅隔离过程的相关间隔的预处理步骤，从而促进了有效的产品质量监控。在测试运行中生成的包含 37 个样本的工业数据集的评估显示，检测到的产品质量缺陷的真阳性率为 100%，同时保持了可接受的准确性。此外，该方法可在不到 10 秒的时间内完成输出，确保在处理下一个工件之前即可获得结果。通过这种方式，有效的产品质量管理成为可能，减少了时间和成本密集的质量检查。显示检测到的产品质量缺陷的真阳性率为 100%，同时保持可接受的准确性。此外，该方法可在不到 10 秒的时间内完成输出，确保在处理下一个工件之前即可获得结果。通过这种方式，有效的产品质量管理成为可能，减少了时间和成本密集的质量检查。显示检测到的产品质量缺陷的真阳性率为 100%，同时保持可接受的准确性。此外，该方法可在不到 10 秒的时间内完成输出，确保在处理下一个工件之前即可获得结果。通过这种方式，有效的产品质量管理成为可能，减少了时间和成本密集的质量检查。