.png){kind=icon}
返回
A spatiotemporal feature learning-based RUL estimation method for predictive maintenance
.png){kind=icon}
PRE
AI
DOI：10.1016/j.measurement.2023.112824.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Studies that apply deep learning (DL) methods to maintenance support systems have achieved many successes because degradation patterns and remaining useful life (RUL) of critical equipment can be described and pre-dicted by DL techniques. However, mining spatial and temporal dependencies from multivariate sensor signals and fusing spatiotemporal features sufficiently are challenging tasks. In this proposal, a novel signal-level DL framework containing three layers called STRUL is proposed for end-to-end RUL estimation. The first data segmentation layer is designed based on the sliding window manner which makes STRUL work directly on raw signals. Then, in the information extraction layer, two feature extractors based on the convolutional neural network are used synchronously to learn spatial and temporal features from each time series. The last infor-mation aggregation layer is designed to fuse features so that the holistic spatiotemporal features can be learned and further contribute to RUL prediction. The proposed STRUL model achieves better comprehensive perfor-mance on RUL estimation tasks than existing models, which has been verified by two case studies.
Keyword:
Manufacturing intelligence
Predictive maintenance
Remaining useful life
Spatiotemporal data mining
期刊
IF:
5.6
论文数:
2.0W
被引数:
5.4W
机构
引用论文
High-frequency characterization and modeling of distortion behavior of MOSFETs for RF IC design
PREAI
分享收藏分享收藏A BiGRU Autoencoder Remaining Useful Life Prediction Scheme With Attention Mechanism and Skip Connection.png)
IEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
PREAI
分享收藏Remaining useful life estimation of engineered systems using vanilla LSTM neural networksNEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏Remaining useful life estimation in prognostics using deep convolution neural networks使用深度卷积神经网络在预测中的剩余使用寿命估计
OAAI
OAAI分享收藏A multi-sensor approach to remaining useful life estimation for a slurry pump泥浆泵剩余使用寿命估算的多传感器方法MEASUREMENT
MEASUREMENTIF5.6
PREAI
分享收藏
查看更多内容