预言
人工神经网络
计算机科学
对偶(语法数字)
数据流
感知器
数据挖掘
多层感知器
人工智能
保险丝(电气)
估计
机器学习
工程类
模式识别(心理学)
电气工程
文学类
电信
艺术
系统工程
作者
Danyang Xu,Haobo Qiu,Liang Gao,Zan Yang,Dapeng Wang
标识
DOI:10.1016/j.ress.2022.108444
摘要
Remaining useful life (RUL) estimation plays a crucial role in evaluating health states and improving maintenance plans of mechanical systems. Recently, artificial intelligence-based data-driven methods that use monitoring data as input have made significant progress in machine prognostics. However, current methods commonly ignore the correlations and internal differences of monitoring data, consequently leading to limited estimation performance. Therefore, this paper proposes a novel data-driven RUL estimation method named Dual-Stream Self-Attention Neural Network (DS-SANN). First, the multi-head self-attention mechanism is employed to learn correlations between different monitoring data and weigh the features dynamically to obtain global degraded information. Then, a dual-stream structure network is established to extract features from the original and auxiliary data simultaneously to make a comprehensive reflection of health states. The original and auxiliary data represent absolute values and internal differences of monitoring data, respectively. Finally, the multilayer perceptron is adopted to fuse the obtained features and estimate RUL. In addition, the effectiveness of DS-SANN is validated by the public degradation dataset of turbine engines. Compared with several existing prognostics methods, DS-SANN shows better estimation performance when averaging across all sub-datasets. Specifically, estimation effects evaluated by RMSE and Score improve 21.77% and 32.67%, respectively.
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