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📋📋📋本文目录如下：🎁🎁🎁
目录
💥1 概述
📚2 运行结果
🎉3 参考文献
🌈4 Matlab代码实现
💥1 概述
文献来源：
由于采用了新的先进估计方法，基于类似系统的可用运行到故障测量的飞机发动机剩余使用寿命（RUL）预测在预后健康管理（PHM）中变得更加普遍。然而，特征提取和RUL预测是一项具有挑战性的任务，特别是对于数据驱动的预测。关键问题是如何设计一个合适的特征提取器，能够为时变传感器的原始测量提供更有意义的表示，以较低的计算成本提高预测精度。该文提出一种具有双动态遗忘因子（DDFF）和更新选择策略（USS）的去噪在线顺序极限学习机（DOS-ELM）。首先，根据来自飞机传感器的训练数据的特征，引入使用改进的去噪自动编码器（DAE）进行鲁棒特征提取，以从数据中学习重要模式。然后，对USS进行集成，以确保只有有用的数据序列才能通过训练过程。最后，OS-ELM用于拟合发动机的非累积线性退化函数，并通过处理新的数据并逐渐忘记基于所提出的DDFF的旧数据来解决动态规划问题。所提出的DOS-ELM在涡扇发动机的商业模块化航空推进系统模拟（C-MAPSS）的公共数据集上进行了测试，并与使用普通自动编码器（AE），基本OS-ELM和文献中以前的作品训练的OS-ELM进行了比较。对比结果证明了新的集成鲁棒特征提取方案的有效性，即使在随机解下，网络响应也具有更高的稳定性。
原文摘要：
Remaining Useful Life (RUL) prediction for aircraft engines based on the available run-to-failure measurements of similar systems becomes more prevalent in Prognostic Health Management (PHM) thanks to the new advanced methods of estimation. However, feature extraction and RUL prediction are challenging tasks, especially for data-driven prognostics. The key issue is how to design a suitable feature extractor that is able to give a raw of time-varying sensors measurements more meaningful representation to enhance prediction accuracy with low computational costs. In this paper, a new Denoising Online Sequential Extreme Learning Machine (DOS-ELM) with double dynamic forgetting factors (DDFF) and Updated Selection Strategy (USS) is proposed. First, depending on the characteristics of the training data that comes from aircraft sensors, robust feature extraction using a modified Denoising Autoencoder (DAE) is introduced to learn important patterns from data. Then, USS is integrated to ensure that only the useful data sequences pass through the training process. Finally, OS-ELM is used to fit the non-accumulative linear degradation function of the engine and to address dynamic programming by trucking the new coming data and forgetting gradually the old ones based on the proposed DDFF. The proposed DOS-ELM is tested on the public dataset of commercial modular aeropropulsion system simulation (C-MAPSS) of a turbofan engine and compared with OS-ELM trained with ordinary Autoencoder (AE), basic OS-ELM and pervious works from the literature. Comparison results prove the effectiveness of the new integrated robust feature extraction scheme by showing more stability of the network responses even under random solutions.
📚2 运行结果
部分代码：
xtr=[]; % initialize training inputs
gamma=[96 97];% size of each image
for i=1:size(allfiles,1)
x=imread([pathname '\\' allfiles(i).name]);
x=imresize(x,gamma);
x=rgb2gray(x);
x=double(x);
xtr=[xtr; x];% training set building
end
%% load testing data
%%
%
% In the folder directory there is a folder named 'Test', after a
% dialogue box appears, choose that folder and click choose.
pathname = uigetdir;
allfiles = dir(fullfile(pathname,'*.jpg'));
xts=[]; % initialize testing inputs
for i=1:size(allfiles,1)
x=imread([pathname '\\' allfiles(i).name]);
x=imresize(x,gamma);
x=rgb2gray(x);
x=double(x);
xts=[xts; x];% testing set building
🎉3 参考文献
部分理论来源于网络，如有侵权请联系删除。
Aircraft Engines Remaining Useful Life Prediction with an Adaptive Denoising Online Sequential Extreme Learning Machine | Request PDF (researchgate.net)
