Deep-Learning-Based Information Fusion Methodology for Oil Film Coefficient Identification of Squeeze Film Dampers

Abstract

Squeeze film damper (SFD) is one of the key components in the sophisticated rotating machines in the high-end aerospace industry. Rolling bearings in the aeroengine spindle system are often used in combination with elastic supports and SFDs to reduce rotor vibrations. Accurate coefficient identification of oil films in SFDs is critical to ensure safe and reliable operations of the complex equipment. However, this task remains a challenging issue in the past decades due to the difficulties in real-time measurement and computation during operations. The highly nonlinear relationship between the measurement and oil film coefficient cannot be effectively captured using traditional methods. This article proposes a novel deep-learning-based information fusion methodology for oil film coefficient identification of SFDs. An end-to-end deep neural network model structure is established, which explores heterogeneous SFD data simultaneously with fusion architecture and directly outputs the identification results. The high-dimensional data can be automatically processed, and high identification accuracy can be achieved. Experiments on the SFD test rig are carried out for validation. The experimental results demonstrate that the proposed method can well identify the oil film coefficients in an intelligent manner and is robust against environmental noise and different parameter settings. The proposed methodology is, thus, promising for applications in real industries.