稳健性(进化)
计算机科学
特征(语言学)
保险丝(电气)
人工智能
特征学习
模式识别(心理学)
一般化
块(置换群论)
冗余(工程)
可靠性(半导体)
机器学习
数据挖掘
工程类
数学
物理
数学分析
哲学
电气工程
操作系统
基因
量子力学
功率(物理)
生物化学
化学
语言学
几何学
作者
Liang Zhou,Huawei Wang,Shanshan Xu
标识
DOI:10.1016/j.ress.2023.109182
摘要
Aero-engine prognosis is helpful to ensure its safety and reliability, and effectively reduce the maintenance cost. However, the existing works only perform RUL prediction, ignoring the fault factors that lead to engine degradation. In addition, most prognosis methods can only extract single-scale features, ignoring the potential degradation features at other scales and layers. Therefore, this work proposes an aero-engine prognosis framework based on multi-scale feature fusion and multi-task parallel learning. In the proposed framework, multi-scale feature fusion blocks are designed to explore and fuse the potential degradation features of samples under different scales. And a layers concatenation block is constructed to integrate feature details from different layers and avoid losing useful information. Then a multi-task parallel learning block is constructed, and a joint loss function is developed for parallel learning of RUL prediction and fault diagnosis tasks. Meanwhile, a stacked image conversion method is proposed to integrate multi-sensor data with multiple cycles into image sample and make it contains more information beneficial to engine degradation. Finally, experimental results on CMAPSS and NCMAPSS datasets show that the proposed framework exhibits superiority over other state-of-the-art methods and demonstrates good generalization and robustness.
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