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 previous 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.

得益于新的先进估算方法，基于类似系统的可用运行至失败测量结果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。但是，特征提取和RUL预测是具有挑战性的任务，特别是对于数据驱动的预测。关键问题是如何设计合适的特征提取器，该提取器能够为时变传感器的原始数据提供更有意义的表示，从而以较低的计算成本提高预测精度。本文提出了一种具有双动态遗忘因子（DDFF）和更新选择策略（USS）的新型降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，引入了使用改进的降噪自动编码器（DAE）进行的强大特征提取，以从数据中学习重要模式。然后，将USS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降级功能，并通过卡车运输新的即将到来的数据并基于建议的DDFF逐渐遗忘旧的数据来解决动态编程问题。拟议的DOS-ELM在涡轮风扇发动机的商业模块化航空推进系统仿真（C-MAPSS）的公共数据集上进行了测试，并与由普通自动编码器（AE）训练的OS-ELM，基本OS-ELM以及以前文献中的工作进行了比较。 。