王海涛

副教授    硕士生导师

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  • 教师拼音名称: wanghaitao
  • 所在单位: 机电工程学院
  • 学历: 研究生(博士)毕业
  • 性别: 男
  • 学位: 博士学位
  • 在职信息: 在职

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Remaining Useful Life Prediction Based on Adaptive SHRINKAGE Processing and Temporal Convolutional Network

发布时间:2024-11-07
点击次数:
所属单位:
机电工程学院
发表刊物:
SENSORS
关键字:
remaining useful life;deep learning;temporal convolutional network;adaptive shrinkage processing
摘要:
The remaining useful life (RUL) prediction is important for improving the safety, supportability, maintainability, and reliability of modern industrial equipment. The traditional data-driven rolling bearing RUL prediction methods require a substantial amount of prior knowledge to extract degraded features. A large number of recurrent neural networks (RNNs) have been applied to RUL, but their shortcomings of long-term dependence and inability to remember long-term historical information can result in low RUL prediction accuracy. To address this limitation, this paper proposes an RUL prediction method based on adaptive shrinkage processing and a temporal convolutional network (TCN). In the proposed method, instead of performing the feature extraction to preprocess the original data, the multi-channel data are directly used as an input of a prediction network. In addition, an adaptive shrinkage processing sub-network is designed to allocate the parameters of the soft-thresholding function adaptively to reduce noise-related information amount while retaining useful features. Therefore, compared with the existing RUL prediction methods, the proposed method can more accurately describe RUL based on the original historical data. Through experiments on a PHM2012 rolling bearing data set, a XJTU-SY data set and comparison with different methods, the predicted mean absolute error (MAE) is reduced by 52% at most, and the root mean square error (RMSE) is reduced by 64% at most. The experimental results show that the proposed adaptive shrinkage processing method, combined with the TCN model, can predict the RUL accurately and has a high application value.
第一作者:
史丽晨,王海涛
论文类型:
期刊论文
通讯作者:
杨杰,王瑞华
卷号:
22/1-18
ISSN号:
1424-8220
是否译文:
CN号:
1424-8220
发表时间:
2022-11-23