scholarly journals Correction to: Spatio-temporal graph convolutional neural network for remaining useful life estimation of aircraft engines

2021 ◽  
Author(s):  
Mengni Wang ◽  
Yuanxiang Li ◽  
Yuxuan Zhang ◽  
Lei Jia
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Remaining Useful Life ◽  
Aircraft Engines ◽  
Life Estimation ◽  
Useful Life ◽  
Temporal Graph ◽  
Spatio Temporal

NBLSTM: Noisy and Hybrid Convolutional Neural Network and BLSTM-Based Deep Architecture for Remaining Useful Life Estimation

2020 ◽  
Vol 20 (2) ◽  
Author(s):  
Ali Al-Dulaimi ◽  
Soheil Zabihi ◽  
Amir Asif ◽  
Arash Mohammed
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Remaining Useful Life ◽  
Smart Manufacturing ◽  
Life Estimation ◽  
Noisy Input ◽  
Deep Architecture ◽  
Useful Life

Abstract Smart manufacturing and industrial Internet of things (IoT) have transformed the maintenance management concept from the conventional perspective of being reactive to being predictive. Recent advancements in this regard has resulted in development of effective prognostic health management (PHM) frameworks, which coupled with deep learning architectures have produced sophisticated techniques for remaining useful life (RUL) estimation. Accurately predicting the RUL significantly empowers the decision-making process and allows deployment of advanced maintenance strategies to improve the overall outcome in a timely fashion. In light of this, the paper proposes a novel noisy deep learning architecture consisting of multiple models designed in parallel, referred to as noisy and hybrid deep architecture for remaining useful life estimation (NBLSTM). The proposed NBLSTM is designed by integration of two parallel noisy deep architectures, i.e., a noisy convolutional neural network (CNN) to extract spatial features and a noisy bidirectional long short-term memory (BLSTM) to extract temporal information learning the dependencies of input data in both forward and backward directions. The two paths are connected through a fusion center consisting of fully connected multilayers, which combines their outputs and forms the target predicted RUL. To improve the robustness of the model, the NBLSTM is trained based on noisy input signals leading to significantly robust and enhanced generalization behavior. Through 100 Monte Carlo simulation runs performed under three different signal-to-noise ratio (SNR) values, it can be noted that utilization of the noisy training enhanced the results by reducing the standard deviation (std) between 9% and 67% across different settings in terms of the root-mean-square error (RMSE) and between 21% and 63% in terms of the score value. The proposed NBLSTM model is evaluated and tested based on the commercial modular aero-propulsion system simulation (C-MAPSS) dataset provided by NASA, illustrating state-of-the-art results in comparison with its counterparts.

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.

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