Smart factory is the main keyword in the field of manufacturing processes about the fourth industrial revolution. To realize the smart factory, making all pieces of device into smart devices that are connected to the centralized system to enable a real-time exchange of information is essential. Sound can be efficient means to make devices as smart devices because sound can contain the status information of various devices simultaneously, and it can be recorded easily outside of a device using only a microphone. In this study, multi-device operation monitoring system by analyzing sound is developed. Mic arrays for acquiring the sound were installed at the outside the devices and recorded the sounds from several devices simultaneously. By analyzing the recorded sound with log-mel spectrogram and Convolutional Neuron Network (CNN), the system could detect the operational status of three devices with an accuracy of 71–92 %. To improve the performance, virtual data set was created by composition of individual device operating sounds of different intensities. With this virtual data set, accuracy can be enhanced to 87 % ∼ 99 % accuracy and, required sound data amount could be reduced. Developed system was applied successfully in monitoring experiments in two different environments: a workshop in which hand-operated device was used and a factory with a computer numerical control machine and verifying the performance.

智能工厂是关于第四次工业革命的制造过程领域的主要关键字。为了实现智能工厂，将所有设备变成连接到集中式系统以实现实时信息交换的智能设备至关重要。声音可以成为使设备成为智能设备的有效方法，因为声音可以同时包含各种设备的状态信息，并且可以仅使用麦克风将其轻松记录在设备外部。在这项研究中，开发了通过分析声音的多设备运行监控系统。用于获取声音的麦克风阵列安装在设备外部，并同时记录了来自多个设备的声音。通过使用log-mel频谱图和卷积神经元网络（CNN）分析记录的声音，该系统可以检测到三个设备的运行状态，准确度为71–92％。为了提高性能，通过组合各个强度不同的设备操作声音来创建虚拟数据集。利用该虚拟数据集，可以将精度提高到87％〜99％的精度，并且可以减少所需的声音数据量。所开发的系统已成功应用于两种不同环境的监测实验：使用手动设备的车间和配备计算机数控机床并验证性能的工厂。可以将精度提高到87％〜99％，并且可以减少所需的声音数据量。所开发的系统已成功应用于两种不同环境的监测实验：使用手动设备的车间和配备计算机数控机床并验证性能的工厂。可以将精度提高到87％〜99％，并且可以减少所需的声音数据量。所开发的系统已成功应用于两种不同环境的实验监控：使用手动设备的车间和配备计算机数控机床并验证其性能的工厂。