Remaining Useful Life Prognostics of Aircraft Engines Based on Damage Propagation Modeling and Data Analysis

2015 ◽  
Author(s):  
Xinxin Xiong ◽  
Hui Yang ◽  
Nong Cheng ◽  
Qing Li
Keyword(s):  
Data Analysis ◽  
Remaining Useful Life ◽  
Aircraft Engines ◽  
Damage Propagation ◽  
Propagation Modeling ◽  
Useful Life

scholarly journals An SW-ELM Based Remaining Useful Life Prognostic Approach for Aircraft Engines

2020 ◽  
Vol 53 (2) ◽  
pp. 13601-13606
Author(s):  
Dingzhou Peng ◽  
Shen Yin ◽  
Kuan Li ◽  
Hao Luo
Keyword(s):  
Remaining Useful Life ◽  
Aircraft Engines ◽  
Useful Life

scholarly journals A hybrid ARIMA–SVM model for the study of the remaining useful life of aircraft engines

2019 ◽  
Vol 346 ◽  
pp. 184-191 ◽  
Author(s):  
Celestino Ordóñez ◽  
Fernando Sánchez Lasheras ◽  
Javier Roca-Pardiñas ◽  
Francisco Javier de Cos Juez
Keyword(s):  
Remaining Useful Life ◽  
Aircraft Engines ◽  
Svm Model ◽  
Useful Life

scholarly journals Remaining Useful Life Estimation of Aircraft Engines Using a Joint Deep Learning Model Based on TCNN and Transformer

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Hai-Kun Wang ◽  
Yi Cheng ◽  
Ke Song
Keyword(s):  
Deep Learning ◽  
Working Conditions ◽  
Health Management ◽  
Remaining Useful Life ◽  
Aircraft Engines ◽  
Key Technology ◽  
Life Estimation ◽  
Useful Life ◽  
New Generation ◽  
Deep Learning Model

The remaining useful life estimation is a key technology in prognostics and health management (PHM) systems for a new generation of aircraft engines. With the increase in massive monitoring data, it brings new opportunities to improve the prediction from the perspective of deep learning. Therefore, we propose a novel joint deep learning architecture that is composed of two main parts: the transformer encoder, which uses scaled dot-product attention to extract dependencies across distances in time series, and the temporal convolution neural network (TCNN), which is constructed to fix the insensitivity of the self-attention mechanism to local features. Both parts are jointly trained within a regression module, which implies that the proposed approach differs from traditional ensemble learning models. It is applied on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset from the Prognostics Center of Excellence at NASA Ames, and satisfactory results are obtained, especially under complex working conditions.

scholarly journals Aircraft Engines Remaining Useful Life Prediction with an Improved Online Sequential Extreme Learning Machine

2020 ◽  
Vol 10 (3) ◽  
pp. 1062 ◽  
Author(s):  
Tarek Berghout ◽  
Leïla-Hayet Mouss ◽  
Ouahab Kadri ◽  
Lotfi Saïdi ◽  
Mohamed Benbouzid
Keyword(s):  
Extreme Learning Machine ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Selection Strategy ◽  
Mapping Technique ◽  
Model Parameters ◽  
Aircraft Engines ◽  
Useful Life ◽  
Learning Machine

The efficient data investigation for fast and accurate remaining useful life prediction of aircraft engines can be considered as a very important task for maintenance operations. In this context, the key issue is how an appropriate investigation can be conducted for the extraction of important information from data-driven sequences in high dimensional space in order to guarantee a reliable conclusion. In this paper, a new data-driven learning scheme based on an online sequential extreme learning machine algorithm is proposed for remaining useful life prediction. Firstly, a new feature mapping technique based on stacked autoencoders is proposed to enhance features representations through an accurate reconstruction. In addition, to attempt into addressing dynamic programming based on environmental feedback, a new dynamic forgetting function based on the temporal difference of recursive learning is introduced to enhance dynamic tracking ability of newly coming data. Moreover, a new updated selection strategy was developed in order to discard the unwanted data sequences and to ensure the convergence of the training model parameters to their appropriate values. The proposed approach is validated on the C-MAPSS dataset where experimental results confirm that it yields satisfactory accuracy and efficiency of the prediction model compared to other existing methods.

Ensemble Prognostics With Degradation-Dependent Weights: Prediction of Remaining Useful Life for Aircraft Engines

2017 ◽  
Author(s):  
Zhixiong Li ◽  
Dazhong Wu ◽  
Chao Hu ◽  
Janis Terpenny ◽  
Sheng Shen
Keyword(s):  
Predictive Model ◽  
Ensemble Learning ◽  
Learning Algorithm ◽  
Remaining Useful Life ◽  
Weighted Sum ◽  
Aircraft Engines ◽  
Data Set ◽  
Ensemble Learning Algorithm ◽  
Useful Life ◽  
Mechanical Components

The objective of this research is to introduce a new ensemble prognostics method with degradation-dependent weights. Specifically, this method assigns an optimized, degradation-dependent weight to each learner (i.e., learning algorithm) such that the weighted sum of the prediction results from all the learners predicts the RUL of mechanical components with better accuracy. The ensemble prognostic algorithm is demonstrated using a data set collected from an engine simulator. Analysis results show that the predictive model trained by the ensemble learning algorithm outperform the existing methods.

scholarly journals A Hybrid PCA-CART-MARS-Based Prognostic Approach of the Remaining Useful Life for Aircraft Engines

Sensors ◽  
2015 ◽  
Vol 15 (3) ◽  
pp. 7062-7083 ◽  
Author(s):  
Fernando Lasheras ◽  
Paulino Nieto ◽  
Francisco de Cos Juez ◽  
Ricardo Bayón ◽  
Victor Suárez
Keyword(s):  
Remaining Useful Life ◽  
Aircraft Engines ◽  
Useful Life
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