Today, Industry 4.0 is being introduced. Machines are equipped with internet connection and increasingly sensorized using Industrial Internet of Things (IIoT) sensors. Especially the emergence of 5G is a game changer in this regard. It becomes possible to send data at high speeds to cloud computing data-centers. However, streaming all data is deemed to be unfeasible. It is more advantageous to use the additionally available bandwidth to drastically increase the number of connected sensors. Thus, on-board processing of the data directly at the edge is necessary.
This paper illustrates this edge computing concept using data of wind turbines. The spectral coherence approach is one of the most promising approaches for bearing fault diagnostics to extract the most optimal envelope. This approach requires a significant amount of computational power. Today, different Advanced Risc Machine (ARM) processors are available in embedded architectures. Moreover, CPU based single board computers are available. The edge computing concept is validated by processing a vibration processing pipeline containing this spectral coherence method using such architectures. Both healthy and faulty data sets are processed. Performance benchmarking is done compared to a traditional computer. Adaptations are done to the spectral coherence method to make it more suitable for the embedded architecture.