Optimal Sparse Volterra Modeling for Transient Behavior of Turbofan Engines based on Internet of Things

Abstract

In recent years, Internet of Things (IoT) technologies have been increasingly utilized to collect enormous volumes of performance data for intelligent analysis and modeling of aero-engines. This has aided in the development of numerous data-driven solutions so that a strong knowledge of the intricate operations within the equipment is no longer needed. To characterize the dynamic nonlinear transient behavior of turbofan engines, fast response changes have the possibility of being captured accurately through high-order, long-memory-length Volterra series. However, exponentially increasing coefficients are still challenging to be handled properly. For fast and reliable modeling of turbofan engines, an Optimal Sparse Volterra (OSV) model is developed in this paper by reconstructing sparse nonzero coefficients after a global selection through particle swarm optimization. The OSV model focuses on the optimal sparsity of the Volterra kernels while being insensitive to the signal length. Besides, noise reduction and the correlation analysis method are specifically designed for sensor measurements of low-bypass ratio turbofan engines. The OSV model, as well as retaining the powerful descriptive capability of the Volterra series for nonlinear characteristics, finds the most relevant sets of variables and the set of model parameters automatically under the minimum computing workload. According to the experimental results, when real test data are used for turbofan transient maneuvers, the OSV model ensures that the mean absolute error is less than [Formula: see text] for high-pressure rotor speed, thrust and exhaust temperature. Moreover, the nonzero identification coefficients produced by the OSV model in the experiments are less than 6% of the total coefficients. At the same time, the average running time required by the OSV model is less than 35% of that of traditional identification algorithms.