In manufacturing industry, one of the main targets is to increase automation and ultimately to avoid failures under all circumstances. The plugging and locking of connectors is a class of tasks which is yet hard to be automatized with sufficiently high process stability. Due to the variation of plugging positions and external disturbances, e.g. occlusion due to cables, the quality assessment of plugging processes has emerged as a challenging task for image-based systems. For this reason, the proposed approach analyzes the inherent acoustic connector locking properties in combination with different neural network architectures in order to correctly identify connector locking signals and further to distinguish them from other machining events occurring in assembly plants. For this specific task, highly sensitive optical microphones have been applied for data acquisition. The proposed experiments are carried out under laboratory conditions as well as for the more complex situation in a real manufacturing environment. In this context, the usage of multimodal neural network architectures achieved highest levels in classification performance with accuracy levels close to 90%.

中文翻译：

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使用光学麦克风数据监督连接器锁定事件的检测

在制造业中，主要目标之一是提高自动化程度并最终避免在任何情况下出现故障。连接器的插拔和锁定是一类难以自动化且具有足够高的工艺稳定性的任务。由于插接位置的变化和外部干扰，例如由于电缆造成的遮挡，插接过程的质量评估已成为基于图像的系统的一项具有挑战性的任务。出于这个原因，所提出的方法结合不同的神经网络架构分析了固有的声学连接器锁定特性，以便正确识别连接器锁定信号，并进一步将它们与装配厂中发生的其他加工事件区分开来。对于这个特定的任务，高灵敏度光学麦克风已应用于数据采集。建议的实验是在实验室条件下以及在真实制造环境中更复杂的情况下进行的。在这种情况下，多模态神经网络架构的使用在分类性能方面达到了最高水平，准确率接近 90%。