In the automotive industry, despite the robotic systems on the production lines, factories continue employing workers in several custom tasks getting for semi-automatic assembly operations. Specifically, the assembly of electrical harnesses of engines comprises a set of connections between electrical components. Despite the task is easy to perform, employees tend not to notice that a few components are not being connected properly due to physical fatigue provoked by repetitive tasks. This yields a low quality of the assembly production line and possible hazards. In this work, we propose a sound detection system based on machine/deep learning (ML/DL) approaches to identify click sounds produced when electrical harnesses are connected. The purpose of this system is to count the number of connections properly made and to feedback to the employees. We collect and release a public dataset of 25,000 click sounds of 25 ms length at 22 kHz during three months of assembly operations in an automotive production line located in Mexico. Then, we design an ML/DL-based methodology for click sound detection of assembled harnesses under real conditions of a noisy environment (noise level ranging from $-16.67$ dB to $-12.87$ dB) including other machinery sounds. Our best ML/DL model (i.e., a combination between five acoustic features and an optimized convolutional neural network) is able to detect click sounds in a real assembly production line with an accuracy of $94.55\pm 0.83$ %. To the best of our knowledge, this is the first time a click sounds detection system in assembling electrical harnesses of engines for giving feedback to the workers is proposed and implemented in a real-world automotive production line. We consider this work valuable for the automotive industry on how to apply ML/DL approaches for improving the quality of semi-automatic assembly operations.

在汽车工业中，尽管生产线上配备了机器人系统，但工厂仍继续雇用工人完成一些自定义任务，以进行半自动装配操作。具体地，发动机的线束的组件包括电气部件之间的一组连接。尽管该任务很容易执行，但是由于重复任务引起的身体疲劳，员工往往不会注意到一些组件未正确连接。这导致组装生产线的质量低下并可能造成危害。在这项工作中，我们提出了一种基于机器/深度学习（ML / DL）方法的声音检测系统，以识别连接线束时产生的喀哒声。该系统的目的是计算正确建立的连接数并反馈给员工。在位于墨西哥的汽车生产线中进行组装工作的三个月中，我们收集并发布了一个公共数据集，其中包含225,000 kHz的25,000声单击声音，时长为25毫秒（22 kHz）。然后，我们设计了一种基于ML / DL的方法，用于在嘈杂环境（噪声水平范围从$-16。67$ 分贝到 $-12。87$ dB），包括其他机械声音。我们最好的ML / DL模型（即五个声学特征和优化的卷积神经网络的组合）能够检测真实装配生产线中的喀哒声。$94。55\pm 0。83$ ％。据我们所知，这是首次在实际的汽车生产线中提出并实施一种用于组装发动机线束以向工人提供反馈的咔嗒声检测系统。我们认为这项工作对于汽车行业如何应用ML / DL方法来提高半自动装配操作的质量非常有价值。