Aero-Engine Remaining Useful Life Estimation Based on Multi-Head Networks

Ren, Likun; Qin, Haiqin; Xie, Zhenbo; Li, Bianjiang; Xu, Kun

doi:10.1109/tim.2022.3149094

Cited by 23 publications

(5 citation statements)

References 31 publications

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“…The C-MAPSS dataset defines the remaining useful life of a turbofan engine as the number of remaining operating cycles until a fault occurs. Current research primarily centers on predicting the remaining operating cycles of turbofan engines [6][7][8][9][34][35][36]. However, the remaining operating cycles are influenced by various external and internal factors, resulting in significant errors and a low coefficient of determination between the predicted and actual values.…”

Section: Health Index Degradationmentioning

confidence: 99%

“…The prediction model for the RUL of turbofan engines is developed based on either their physical structure model or the degradation trends among various monitoring sensors. There are two types of existing methods for estimating RUL: those based on physical models [4,5] and data-driven approaches [6][7][8][9]. Data-driven methods analyze data and identify complex data relationships, achieving accurate predictions of changes in the turbofan engine's state [10].…”

Section: Introductionmentioning

confidence: 99%

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Turbofan Engine Health Prediction Model Based on ESO-BP Neural Network

Zhang,

Xu,

Dai

et al. 2024

Applied Sciences

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Predicting the health index of turbofan engines is critical in reducing downtime and ensuring aircraft safety. This study introduces the elite snake optimizer-back propagation (ESO-BP) model to address the challenges of low accuracy and poor stability in predicting the health index of turbofan engines through neural networks. Firstly, the snake optimizer (SO) was improved into the elite snake optimizer (ESO) through an elite-guided strategy and a reverse learning mechanism. The performance improvement was validated using benchmark functions. Additionally, feature importance was introduced as a feature selection method. Finally, the optimization results of the ESO were employed to set the initial weights and biases of the BP neural network, preventing convergence to local optima. The prediction performance of the ESO-BP model was validated using the C-MAPSS datasets. The ESO-BP model was compared with the CNN, RNN, LSTM, baseline BP, and unimproved SO-BP models. The results demonstrated that the ESO-BP model has a superior accuracy with an impressive R-squared (R2) value of 0.931 and a root mean square error (RMSE) of 0.060 on the FD001 sub-dataset. Furthermore, the ESO-BP model exhibited lower standard deviations of evaluation metrics on 100 trials. According to the study, ESO-BP demonstrated a greater prediction accuracy and stability when compared to commonly used models such as CNN, RNN, LSTM, and BP.

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Section: Health Index Degradationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Turbofan Engine Health Prediction Model Based on ESO-BP Neural Network

Zhang,

Xu,

Dai

et al. 2024

Applied Sciences

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“…Deep learning have more powerful feature extraction and nonlinear relationship learning ability than shallow machine learning prediction methods [14]- [16], therefore, it is widely used for RUL prediction. For example, Ma et al [17] used stacked sparse autoencoder to automatically extract performance degradation features from multiple sensors on the aircraft engine, and applied logistic regression to predict the RUL.…”

Section: Introductionmentioning

confidence: 99%

A Novel Remaining Useful Life Prediction Approach Combined eXtreme Gradient Boosting and Multi-Quantile Recurrent Neural Network

Yin,

Zhao,

Zhang

et al. 2024

IEEE Access

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Remaining useful life (RUL) prediction is a key technology to ensure the reliability and safety of high-end equipment. Deep learning is widely used for RUL prediction due to the excellent feature extraction ability and nonlinear fitting ability. Traditional recurrent neural networks adopt recursive strategy, which easily lead to the problems of error accumulation and low stability. On the other hand, most deep learning methods are used for point prediction and cannot quantify uncertainty in the prediction results. Although some probability prediction methods based on deep learning can provide probability prediction results, it require the assumption that the prediction results follow a specific distribution in advance. However, the distribution of most prediction results does not match a suitable distribution function. To solve above problems, a novel RUL prediction approach based on eXtreme gradient boosting (XGBoost) and multiquantile recursive neural network (MQ-RNN) is proposed in this article. Specifically, XGBoost is used to select features closely related to RUL, and the selected features are fed into MQ-RNN to train the RUL prediction model. The advantage of MQ-RNN is that it has non-parametric framework, prediction results can be obtained by multi-quantile regression, which does not require prior assumptions about the distribution of predicted results. Furthermore, the proposed framework is verified by C-MAPSS dataset. Finally, comparative experiments are conducted. The experimental results show that the proposed method maintains good predictive performance in both point estimation and interval estimation.INDEX TERMS Remaining useful life, eXtreme gradient boosting, Multi-quantile recursive neural network, Quantile regression, Forking sequences training scheme.

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“…CNNs can extract spatial features from raw data, in recent years there has also been research on introducing CNN to the task of aero-engine RUL prediction. For example, Ren et al [21] proposed a multi-head CNN to improve the ability of the prediction network to extract temporal features from the original data. Kim and Sohn [22] proposed a multi-task learning model based on CNN, which can reflect some implicit correlations between RUL prediction and data monitoring.…”

Section: Introductionmentioning

confidence: 99%

Parallel processing of sensor signals using deep learning method for aero-engine remaining useful life prediction

Wang,

Li,

Fei

et al. 2024

Meas. Sci. Technol.

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Accurately predicting the remaining useful life of aerospace engines is crucial for enhancing the reliability of aviation equipment. While some methods have taken note of the challenges posed by vast sensor data and complex signal interrelationships, there is still room for improvement in performance. This paper proposes a novel deep learning model that utilizes a parallel structure to independently process inputs from various sensor signals. Each branch in this parallel structure employs a combination of an improved Inception module and a novel feature filtering module as a feature extractor. The improved Inception module boasts a larger perceptual field to ensure the integrity of feature information. The feature filtering module calculates the importance weights of feature information through convenient computation, allowing the network to focus more on feature information without significantly increasing computational complexity. Finally, the feature extractor is combined with a gated recurrent unit module to learn features from sensor signals. Extensive experiments were conducted on the C-MAPSS standard dataset, comparing the proposed method with other state-of-the-art methods. Ablation experiments were performed on the new generation N-CMAPSS standard dataset. The results of the experiments confirm the superiority and rationality of the proposed prediction method.

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Aero-Engine Remaining Useful Life Estimation Based on Multi-Head Networks

Cited by 23 publications

References 31 publications

Turbofan Engine Health Prediction Model Based on ESO-BP Neural Network

Turbofan Engine Health Prediction Model Based on ESO-BP Neural Network

A Novel Remaining Useful Life Prediction Approach Combined eXtreme Gradient Boosting and Multi-Quantile Recurrent Neural Network

Parallel processing of sensor signals using deep learning method for aero-engine remaining useful life prediction

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