基于RCNN-ABiLSTM的机械设备剩余寿命预测方法  

Prediction method for mechanical equipment based on RCNN-ABiLSTM

作　　者：闫啸家 梁伟阁[1] 张钢 佘博[1] 田福庆[1] YAN Xiaojia;LIANG Weige;ZHANG Gang;SHE Bo;TIAN Fuqing(College of Weaponry Engineering,Naval University of Engineering,Wuhan 430033,China;College of Missiles and Naval Guns,Dalian Naval Academy,Dalian 116016,China)

机构地区：[1]海军工程大学兵器工程学院,湖北武汉430033 [2]大连舰艇学院导弹与舰炮系,辽宁大连116016

出　　处：《系统工程与电子技术》2023年第3期931-940,共10页Systems Engineering and Electronics

基　　金：国家自然科学基金(61640308);湖北省自然科学基金(2019CFB362)资助课题。

摘　　要：针对机械设备的关键退化信息易淹没在非线性、多维度、长时间、大规模监测数据中的问题,提出了一种基于残差卷积神经网络和注意力双向长短时记忆网络融合(residual convolutional neural network-attentional bidirectional long short-term memory network,RCNN-ABiLSTM)的机械设备剩余寿命预测方法。首先通过训练RCNN提取监测数据的深度空间特征;然后通过引入注意力机制,优化双向长短时记忆网络提取时间相关特征的权重参数,加强关键退化信息对剩余寿命预测的表达;最后通过航空发动机数据集验证了方法的有效性。分析结果表明,对于运行条件复杂和故障模式多变的多维监测数据,所提方法能够准确寻找退化时间点,有效提高长时间运行设备的剩余寿命预测准确度。Aiming at the problem that the key degradation information of mechanical equipment is easy to be submerged in nonlinear,multi-dimensional,long-term and large-scale monitoring data,a method for predicting the remaining useful life of mechanical equipment based on residual convolutional neural network-attentional bidirectional long short-term memory network(RCNN-ABiLSTM)is proposed.Firstly,the RCNN is trained for deep spatial feature extraction of the monitoring data.Then,by introducing the attention mechanism,the weight parameters of the time-related features extracted by BiLSTM are optimized.And the expression of the key degradation information on the remaining life prediction is strengthened.Finally,the effectiveness of the proposed method is verified by the aircraft engine.The analysis results show that the proposed method can accurately find the degradation time point for multi-dimensional monitoring data with complex operating conditions and variable failure modes.The remaining useful life prediction accuracy of long-running equipment is effectively improved.

关 键 词：残差卷积神经网络 注意力机制 融合模型 剩余寿命预测 航空发动机 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]