Digital twin-driven vibration amplitude simulation for condition monitoring of axial blowers in blast furnace ironmaking

Abstract

Long-term reliable condition monitoring (CM) of blast furnace blowers is essential to avoid catastrophic failure. Due to variable working conditions, the predefined thresholds in current CM systems influence the accuracy of the monitoring process and can lead to misdiagnoses. In order to overcome this limitation, we propose a digital twin (DT)-based scheme to monitor vibrations found in blowers. Factors believed to impact the distribution of vibration amplitudes are analysed using data collected from a constant speed axial blower operating in an industrial commercial environment and, based on which, a machine learning-based adaptive amplitude simulation model is developed on our on-site private cloud computing platform. Outcomes reveal that different guide vane openings in the manufacturing process can cause changes in amplitudes. By integrating the newly-arriving sensor data, vibration amplitudes can be more accurately predicted in the virtual space. The gap between the simulated and actual value narrowed from ±5 µm to within ±3 µm, from which a dynamic threshold can be defined. The resulting DT model, coupled with the on-site private cloud computing platform, which alleviates the shortage of computational and storage capacity in steel plants, allows for a much more effective CM system.