Time Latency-Centric Signal Processing: A Perspective of Smart Manufacturing.

Sharifu Ura,Angkush Kumar Ghosh

doi:10.3390/s21217336

Abstract

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.

Highlights

Nowadays, the fourth industrial revolution, popularly known as Industry 4.0 [1] or smart manufacturing [2], has been fostering profound transformations in the traditional manufacturing landscape
Apart from the conventional methods, alternative methods must be investigated and incorporated for signal processing and handling the abovementioned difficulties from the smart manufacturing context. This study addresses this issue by adapting delay domain-based signal processing, as follows
This tematic pattern is shown in delay the delay map. This means that value of the signal at a point of time, S2(t), cannot change to a value taken from the interval [0,1] randomly in the point of time, (S2(t+1)); it follows an order

Summary

Introduction

The fourth industrial revolution, popularly known as Industry 4.0 [1] or smart manufacturing [2], has been fostering profound transformations in the traditional manufacturing landscape. Processcondition monitoring means monitoring the process-relevant components (e.g., cutting tool and workpiece surface) In both aspects, sensor signals (e.g., cutting force, torque, surface roughness, vibration, acoustic emission (AE), and alike) obtained from the machining environment play a key role. Cannot frequency domain-based processing) require high data acquisition rates mimic the the dynamics dynamics underlying underlying the the signals signals [11].

Literature Review

Significances of Time Latency and Delay Domain

Consider thethe delay map

Manufacturing Signal Processing

Analyzing under Different Sampling

Analyzing Sensor Signals under Different Sampling Windows

Distinguishing Machining Situations Using Delay Maps

Sensor Signal Acquisition

Signal Pre-Processing

Analysis in the Frequency and Delay Domains

16. Frequency

Concluding Remarks