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

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.