Peng Cheng, Wu Jiaqi, Wang Qilong, Gui Weihua, Tang Zhaohui
School of Computer, Hunan University of Technology, Zhuzhou 412007, China.
School of Automation, Central South University, Changsha 410083, China.
Entropy (Basel). 2022 Dec 13;24(12):1818. doi: 10.3390/e24121818.
At present, the research on the prediction of the remaining useful life (RUL) of machinery mainly focuses on multi-sensor feature extraction and then uses the features to predict RUL. In complex operations and multiple abnormal environments, the impact of noise may result in increased model complexity and decreased accuracy of RUL predictions. At the same time, how to use the sensor characteristics of time is also a problem. To overcome these issues, this paper proposes a dual-channel long short-term memory (LSTM) neural network model. Compared with the existing methods, the advantage of this method is to adaptively select the time feature and then perform first-order processing on the time feature value and use LSTM to extract the time feature and first-order time feature information. As the RUL curve predicted by the neural network is zigzag, we creatively designed a momentum-smoothing module to smooth the predicted RUL curve and improve the prediction accuracy. Experimental verification on the commercial modular aerospace propulsion system simulation (C-MAPSS) dataset proves the effectiveness and stability of the proposed method.
目前，关于机械剩余使用寿命（RUL）预测的研究主要集中在多传感器特征提取，然后利用这些特征来预测RUL。在复杂运行和多种异常环境下，噪声的影响可能导致模型复杂度增加以及RUL预测精度降低。同时，如何利用时间传感器特性也是一个问题。为克服这些问题，本文提出了一种双通道长短期记忆（LSTM）神经网络模型。与现有方法相比，该方法的优势在于自适应选择时间特征，然后对时间特征值进行一阶处理，并利用LSTM提取时间特征和一阶时间特征信息。由于神经网络预测的RUL曲线呈锯齿状，我们创新性地设计了一个动量平滑模块来平滑预测的RUL曲线并提高预测精度。在商业模块化航空航天推进系统仿真（C-MAPSS）数据集上的实验验证证明了所提方法的有效性和稳定性。
