Vibration Analysis of a Wind Turbine Gearbox for Off-cloud Health Monitoring through Neuromorphic-computing

Considering the recent transition towards renewable energy sources such as off-shore wind turbines, solar farms, and hydroelectric power plants, Structural Health Monitoring (SHM) of these novel infrastructures using Machine Learning (ML) methods has became extremely attractive. However, the strong dependence of energy-thirsty ML approaches on cloud computations limits their application at the edge, which is significantly important for SHM in remote locations. Therefore, development of edge-oriented machine learning models and their integration with edge-computing technologies such as neuromorphic platforms is vital for the real-time and on-site processing of sensory signals without cloud computations for SHM. Therefore, the objective of this work was to develop a neuromorphic-compatible ML model for time-series analysis of accelerometer data acquired from a wind turbine gearbox for fault detection purposes. The hardware-friendly model in this work provided an accuracy of 83.2% for the recognition of healthy and damaged gearboxes, providing promising results for off-cloud SHM using neuromorphic-computing technologies.

Automated summary

With the recent transition to renewable energy sources, the use of machine learning methods for structural health monitoring of new infrastructures has become attractive. Developing edge-oriented machine learning models and integrating them with edge-computing technologies are vital for real-time processing of sensory signals on-site, without cloud computations, especially in remote locations.