Leveraging the Power of the Combination of CNN and Bi-Directional LSTM Networks for Aircraft Engine RUL Estimation

Remadna, Ikram; Terrissa, Labib Sadek; Zemouri, Ryad; Ayad, Soheyb; Zerhouni, Noureddine

doi:10.1109/phm-besancon49106.2020.00025

Cited by 20 publications

(9 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, an attention mechanism with a feature fusion method is proposed for RUL estimation. A combined CNN with Bi-directional Long Short-Term Memory (BDLSTM) networks is presented in [19] for RUL prediction for aircraft. The CNN was used to extract spatial features while BDLSTM was utilized to extract temporal features.…”

Section: Related Workmentioning

confidence: 99%

“…(5)(6)(7)(8) of the predicted RUL for each engine matches very well with the actual RUL, which indicates the viability of the proposed AE-DBN model for the RUL prediction.For example based on the FD001 dataset, it can be noticed that the RMSE, MAE and Score are decreased from 13.45, 14.19 and 228 in the case of the standard DBN to 11.27, 11.91 and 219 for the proposed AE-DBN whereas the R 2 is increased from 0.9405 in the case of the standard DBN to 0.9545 for the proposed AE-DBN. For FD002 dataset, it can be noticed that the RMSE, MAE and Score are decreased from 17.55,19.15 and 1379 in the case of the standard DBN to 14.24, 14.85 and 1255 for the proposed AE-DBN whereas the R 2 is increased from 0.9120 in the case of the standard DBN to 0.9411 for the proposed AE-DBN. For FD003 dataset, it can be noticed that the RMSE, MAE and Score are decreased from 12.32, 13.25 and 285 in the case of the standard DBN to 11.13, 11.48 and 264 for the proposed AE-DBN whereas the R 2 is increased from 0.9452 in the case of the standard DBN to 0.9513 for the proposed AE-DBN.…”

mentioning

confidence: 94%

See 1 more Smart Citation

Aircraft Engines Remaining Useful Life Prediction Based on A Hybrid Model of Autoencoder and Deep Belief Network

et al. 2022

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 94%

Aircraft Engines Remaining Useful Life Prediction Based on A Hybrid Model of Autoencoder and Deep Belief Network

et al. 2022

View full text Add to dashboard Cite

“…In the perspective of product lifecycle, Rolls-Royce provides a total solution and full diagnosis starting from design, manufacturing, and operation, to maintenance. In practice, EHM describes and transfers sensor signals from an engine on an aircraft to an operational center on the ground, which can be used to record and monitor the performance of an engine to ensure its reliability [3]. Although EHM brings about a new era in PSS, it still has thousands of parameters to monitor and needs to respond to requests from an operational center by sending back hundreds of hours of information speci cally tailored to that request.…”

Section: Introductionmentioning

confidence: 99%

Integrating Feature Engineering with Deep Learning to Conduct Diagnostic and Predictive Analytics for Turbofan Engines

Wang

Liu

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The prediction of remaining useful life (RUL) is a critical issue in many areas, such as aircrafts, ships, automobile, and facility equipment. Although numerous methods have been presented to address this issue, most of them do not consider the impacts of feature engineering. Typical techniques include the wrapper approach (using metaheuristics), the embedded approach (using machine learning), and the extraction approach (using component analysis). For simplicity, this research considers feature selection and feature extraction. In particular, principal component analysis (PCA) and sliced inverse regression (SIR) are adopted in feature extraction while stepwise regression (SR), multivariate adaptive regression splines (MARS), random forest (RF), and extreme gradient boosting (XGB) are used in feature selection. In feature selection, the original 15 sensors can be reduced to only four sensors that accumulate more than 80% degrees of importance and not seriously decrease the predictive performances. In feature extraction, only the top three principal components can account for more than 80% variances of original 15 sensors. Further, PCA combined with RF is more recommended than PCA and CNN (convolutional neural network) because it can achieve satisfactory performances without incurring tedious computation.

show abstract

“…The literatures [ 27 , 28 ] have used Bi-LSTM-based approaches in the RUL prediction task. Jiang et al [ 29 ] and Remadna et al [ 30 ] further combined the Bi-LSTM and CNN to develop a new fusion model. Inspired by the idea of an encoder-decoder, Liu et al [ 31 ] developed a new learning-based encoder-decoder model based on the LSTM and CNN to predict RUL.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Song

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

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.

show abstract

Leveraging the Power of the Combination of CNN and Bi-Directional LSTM Networks for Aircraft Engine RUL Estimation

Cited by 20 publications

References 11 publications

Aircraft Engines Remaining Useful Life Prediction Based on A Hybrid Model of Autoencoder and Deep Belief Network

Aircraft Engines Remaining Useful Life Prediction Based on A Hybrid Model of Autoencoder and Deep Belief Network

Integrating Feature Engineering with Deep Learning to Conduct Diagnostic and Predictive Analytics for Turbofan Engines

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

Contact Info

Product

Resources

About