摘要
多工况设备在工况变化时存在特征值跳动问题,给剩余寿命预测带来挑战.本文提出了一种多工况设备剩余寿命预测方法.首先给出了多工况设备健康状态数据的标准化方法,降低工况变化对特征值的波动影响;其次提出了一种设备健康指标多源数据融合方法,将多维健康特征融合成一维健康指标,该指标可以反映设备的退化态势;再利用滑动窗口方法构建了训练和测试时间序列样本.为了降低训练样本不均衡导致的过拟合影响问题,提出了一种KNN样本权重赋值方法;最后建立了多层BLSTM剩余寿命预测网络,为了增强网络的预测精度,加入了改进的Huber损失函数.C-MAPSS数据实验结果表明本文提出的多工况设备剩余寿命预测方法具有很好的预测效果.
It is a challenge to predict the residual life of the electromechanical equipment under multiple working conditions because of the unstable characteristic value when the working condition changes.In this paper,a prediction method is proposed for the residual life of the equipment under multiple working conditions.Firstly,a standardized method of the health status data is proposed for the equipment under multiple working conditions,which can reduce the impact of working conditions on the characteristic value fluctuation.Secondly,a multi-source data fusion method is proposed for equipment health indicators,which can fuse multi-dimensional health features into one-dimensional health indicators,and the deterioration situation of the equipment can be reflected by the fused health indicators.Thirdly,the sliding window method is employed to construct the time series of the training and testing samples.A sample weight assignment method via the KNN is proposed to reduce the over-fitting problem caused by the unbalanced training samples.Finally,a multi-layer BLSTM prediction network with an improved Huber loss function is established to enhance the prediction accuracy of the network.The experimental results of the C-MAPSS data show a good prediction effect on the residual life of the equipment under multiple working conditions via the proposed prediction method.
作者
葛阳
马家欣
任勇
秦健聪
GE Yang;MA Jiaxin;REN Yong;QIN Jiancong(School of Mechanical Engineering,Changshu Institute of Technology,Changshu 215500;Dongnan Elevator Co.,Ltd.,Suzhou 215200,China)
出处
《常熟理工学院学报》
2022年第5期65-72,共8页
Journal of Changshu Institute of Technology
基金
苏州市科技计划资助项目“基于多源传感数据融合的电梯健康态势自适应感知关键技术研究”(SYG202021)
江苏省高等学校自然科学研究重大项目“基于多源传感数据融合的电梯安全态势云感知系统研究”(20KJA460011)
“电梯交通客流智能感知与调度技术研究”(21KJA510003)。
关键词
多工况
剩余寿命预测
非均衡数据
多层神经网络
特征融合
multiple working conditions
residual life prediction
unbalanced data
multi-layer neural network
feature fusion