Aero-Engine On-Board Model Based on Batch Normalize Deep Neural Network

• Main Authors: Qiangang Zheng, Juan Fang, Zhongzhi Hu, Haibo Zhang
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Aero-engine model; batch normalize; deep neural network; turbo-fan on-board model; mini-batch gradient descent; data storage
• Online Access: https://ieeexplore.ieee.org/document/8704262/

A new on-board turbo-fan engine modeling method based on a batch normalize (BN) mini-batch gradient descent (MGD) deep neural network (NN) is proposed. This new method adopts BN algorithm, which accelerates the network training speed and overcomes the gradient vanish problem. Hence, using the BN algorithm, the neural network adopts the deeper structure, which means the network has a stronger representation capacity. This mini-batch gradient descent (MGD-NN) algorithm that consumes much less time to update the NN parameters is adopted. Therefore, it is more suitable for training big dataset and establishing a high-accuracy engine model in a large flight envelope. Finally, to verify whether the proposed method could be applied to larger flight envelope, the conventional NN also adopts MGD (called MGD-NN). The turbo-fan engine models based on these two modeling methods are both conducted within a sub-sonic cruise envelope. The simulation results show that the proposed modeling method has much higher accuracy than the MGD-NN. Moreover, the proposed method has the characteristics of less data storage, low computation complexity, and good real-time performance, which are the most importance indices for model realize on-board.