端到端原则
主管(地质)
计算机科学
端铣
均方误差
系列(地层学)
组分(热力学)
人工智能
模拟
数学
统计
工程类
机械加工
机械工程
古生物学
地貌学
生物
地质学
物理
热力学
作者
Changchang Che,Huawei Wang,Xiaomei Ni,Minglan Xiong
标识
DOI:10.1088/1361-6501/ac7f80
摘要
Abstract In order to reduce error accumulation caused by multistep modeling and achieve a generally accurate model, this paper proposes an end-to-end remaining useful life (RUL) prediction model based on a multi-head self-attention bidirectional gated recurrent unit (BiGRU). Taking multivariable samples with long time series as the model input and multistep RUL values as the model output, the BiGRU model is constructed for continuous prediction of RUL. In addition, single-head self-attention models are applied for time series and variables of samples before or after the BiGRU, which can be fused into a multi-head attention BiGRU. Aeroengines and rolling bearings are selected to testify the effectiveness of the proposed method from the system level and component level respectively. The results show that the proposed method can achieve end-to-end RUL prediction efficiently and accurately. Compared with single-head models and individual deep learning models, the prediction mean square error of the proposed method is reduced by 20%–70%.
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