Division of Mechanical and Electrical Engineering, Kitami Institute of Technology, 165 Koen-cho, Kitami 090-8507, Japan.
Graduate School of Engineering, Kitami Institute of Technology, 165 Koen-cho, Kitami 090-8507, Japan.
Sensors (Basel). 2021 Nov 4;21(21):7336. doi: 10.3390/s21217336.
Smart manufacturing employs embedded systems such as CNC machine tools, programable logic controllers, automated guided vehicles, robots, digital measuring instruments, cyber-physical systems, and digital twins. These systems collectively perform high-level cognitive tasks (monitoring, understanding, deciding, and adapting) by making sense of sensor signals. When sensor signals are exchanged through the abovementioned embedded systems, a phenomenon called time latency or delay occurs. As a result, the signal at its origin (e.g., machine tools) and signal received at the receiver end (e.g., digital twin) differ. The time and frequency domain-based conventional signal processing cannot adequately address the delay-centric issues. Instead, these issues can be addressed by the delay domain, as suggested in the literature. Based on this consideration, this study first processes arbitrary signals in time, frequency, and delay domains and elucidates the significance of delay domain over time and frequency domains. Afterward, real-life signals collected while machining different materials are analyzed using frequency and delay domains to reconfirm its (the delay domain's) significance in real-life settings. In both cases, it is found that the delay domain is more informative and reliable than the time and frequency domains when the delay is unavoidable. Moreover, the delay domain can act as a signature of a machining situation, distinguishing it (the situation) from others. Therefore, computational arrangements enabling delay domain-based signal processing must be enacted to effectively functionalize the smart manufacturing-centric embedded systems.
智能制造采用嵌入式系统,如数控机床、可编程逻辑控制器、自动导引车、机器人、数字测量仪器、信息物理系统和数字孪生。这些系统通过感知传感器信号来共同执行高级认知任务(监控、理解、决策和适应)。当传感器信号通过上述嵌入式系统进行交换时,会出现一种称为时间延迟或延迟的现象。因此,信号在其起源处(例如机床)和接收器端接收的信号(例如数字孪生)不同。基于时间和频率的传统信号处理方法无法充分解决以延迟为中心的问题。相反,正如文献中所建议的,可以通过延迟域来解决这些问题。基于这一考虑,本研究首先在时间、频率和延迟域中处理任意信号,并阐明延迟域相对于时间和频率域的重要性。然后,使用频率和延迟域分析加工不同材料时收集的实际信号,以重新确认其(延迟域)在实际设置中的重要性。在这两种情况下,都发现当延迟不可避免时,延迟域比时间和频率域更具信息量和可靠性。此外,延迟域可以作为加工情况的特征,将其(情况)与其他情况区分开来。因此,必须制定基于延迟域的信号处理的计算安排,以使以智能制造为中心的嵌入式系统有效发挥功能。