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Total impulse is highly related to the performance of solid rocket motors. Accurate prediction of total impulse is essential for both design and operation purposes. However, the traditional methods heavily rely on expert knowledge and are incapable of analyzing modern sophisticated equipment. In this paper, a novel total impulse prediction method based on deep learning is proposed. We established a CNN-LSTM-Attention deep neural network model, which can automatically process raw data of highly nonlinear for feature extraction and prediction with high accuracy. Practical rocket data are used for validations which are collected in ignition process. We compared the proposed method with the other popular algorithms to verify the effectiveness and superiority of this method. The outcomes indicate that the proposed data processing and prediction method can achieve promising performance, with the average percentage error of under 2%. By using the downsampling method in data processing, the dependency of the deep learning based method on the data amount is largely reduced. In this way, the proposed method has good application prospects in engineering problems.
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