1.南京信息工程大学自动化学院 南京 210044; 2.无锡学院自动化学院 无锡 214105; 3.中国船舶集团有限公司第七〇三研究所无锡分部 无锡 214105
TP391.5；TN807
江苏省高等学校自然科学基金（22KJB460039）项目资助
1.School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China; 2.School of Automation, Wuxi University, Wuxi 214105, China; 3.Wuxi Branch, No. 703 Research Institute of China State Shipbuilding Corporation, Wuxi 214105, China
作为现代航空的重要组成部分，维护和预测涡扇发动机的使用寿命对于确保安全和降低运营成本至关重要。为了应对涡扇发动机RUL预测中的复杂非线性特征处理难题，本研究提出了一种基于并行TCN与BiLSTM的新型混合模型。针对传统方法难以同时捕捉局部特征和长时间依赖性的问题，该模型通过TCN提取短期局部特征，并利用BiLSTM捕捉数据中的双向时序依赖。同时，针对特征重要性识别不足的挑战，引入了改进的SE注意力机制模块，以动态调整网络的特征权重，增强对关键信息的关注。在C-MAPSS数据集的FD001和FD003子集上的实验验证中，RMSE和Score分别为12.15、230.4和11.16、209.84。结果表明，该方法与其他方法相比具有更高的精度。
The maintenance and prediction of turbofan engine lifespan are critical to modern aviation, playing a key role in ensuring safety and minimizing operational costs. This study addresses the challenge of predicting the RUL of turbofan engines by proposing a novel hybrid model that integrates Parallel TCN and Bidirectional BiLSTM. Traditional methods often struggle to capture both local features and long-term dependencies simultaneously; the proposed model overcomes this limitation by using TCN to extract short-term local features and BiLSTM to capture bidirectional temporal dependencies. To further improve feature importance recognition, an enhanced SE attention mechanism is introduced, which dynamically adjusts feature weights to better highlight critical information. Experiments conducted on the FD001 and FD003 subsets of the C-MAPSS dataset demonstrated that the proposed model achieved RMSE values of 12.15 and 11.16, and Scores of 230.4 and 209.84, respectively, outperforming other approaches in terms of accuracy.
张鑫阳,王可庆,贾新旺,郭永信,蒋亮.基于并行TCN-SE-BiLSTM模型的涡扇发动机剩余寿命预测[J].电子测量技术,2024,47(24):179-187
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