A Data-Driven Aero-Engine Degradation Prognostic Strategy

Wang, Cunsong; Lu, Ningyun; Cheng, Yuehua; Jiang, Bin

doi:10.1109/tcyb.2019.2938244

Cited by 85 publications

(21 citation statements)

References 36 publications

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“…It shows that the two sensors are not correlated well with the usage of the engine and their ranges are both very small (actually less than 0.5) from start to failure, which validates these sensors have high variation in metric Corr and score low value in metric Pre and Pro. In previous studies using the C-MAPSS data, including [9,20], the above two sensors were also wiped out as non-informative sensors.…”

Section: Results and Analysis On Informative Sensor Selectionmentioning

confidence: 99%

“…With the rapid development of data mining techniques and the growing availability of health monitoring data, data-driven methods attract increasing attention. Data-driven methods utilize the information extracted or learned from observed data to identify the degradation behavior and predict the future health condition without using any particular physical model [8,9]. In this view, data-driven methods may be the more applicable prognostic solution for complicated systems, as aero-engines that have limited knowledge of physics-of-failure but have massive multi-sensor monitoring data.…”

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confidence: 99%

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Aircraft Engine Prognostics Based on Informative Sensor Selection and Adaptive Degradation Modeling with Functional Principal Component Analysis

Zhang

Zheng

Huang

et al. 2020

Sensors

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Engine prognostics are critical to improve safety, reliability, and operational efficiency of an aircraft. With the development in sensor technology, multiple sensors are embedded or deployed to monitor the health condition of the aircraft engine. Thus, the challenge of engine prognostics lies in how to model and predict future health by appropriate utilization of these sensor information. In this paper, a prognostic approach is developed based on informative sensor selection and adaptive degradation modeling with functional data analysis. The presented approach selects sensors based on metrics and constructs health index to characterize engine degradation by fusing the selected informative sensors. Next, the engine degradation is adaptively modeled with the functional principal component analysis (FPCA) method and future health is prognosticated using the Bayesian inference. The prognostic approach is applied to run-to-failure data sets of C-MAPSS test-bed developed by NASA. Results show that the proposed method can effectively select the informative sensors and accurately predict the complex degradation of the aircraft engine.Sensors 2020, 20, 920 2 of 21 reducing unnecessary maintenance and minimizing operational costs, prognostics have been an active research field for aircraft engine applications [3].Generally, the existing methods pertaining to aircraft engine prognostics can be classified into two categories: model-based and data-driven methods. For model-based methods, a mathematical model that can describe the engine health degradation process that is normally required to be constructed according to physical failure characteristics before prediction. Some typical model-based methods have been developed for engine health estimation and RUL prediction, such as the Markov model-based [4,5] and particle filtering-based method [6,7]. While model-based methods enable improved accuracy of engine prognostics, certain assumptions and simplifications of the adopted models may pose limitations on their practical deployment. With the rapid development of data mining techniques and the growing availability of health monitoring data, data-driven methods attract increasing attention. Data-driven methods utilize the information extracted or learned from observed data to identify the degradation behavior and predict the future health condition without using any particular physical model [8,9]. In this view, data-driven methods may be the more applicable prognostic solution for complicated systems, as aero-engines that have limited knowledge of physics-of-failure but have massive multi-sensor monitoring data. Among the data-driven prognostic methods, machine learning and statistical approaches are two popular branches. The common machine learning techniques used for prognostics include support vector regression [10], artificial neural network [11,12], fuzzy logic [13], etc. Machine learning methods are capable of dealing with prognostic issues of complex systems, but the predicted results are hard to be explained because ...

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Section: Results and Analysis On Informative Sensor Selectionmentioning

confidence: 99%

mentioning

confidence: 99%

Aircraft Engine Prognostics Based on Informative Sensor Selection and Adaptive Degradation Modeling with Functional Principal Component Analysis

Zhang

Zheng

Huang

et al. 2020

Sensors

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“…where W is the weight in (13). In this equation, first, Wθ τ projects the τ -th admission weight into the output dimension.…”

Section: F Model Interpretabilitymentioning

confidence: 99%

“…A variety of predictive models using deep learning technology has been proposed for predicting temporal events, such as diagnosis prediction [1]- [5]; mortality prediction [6]- [9]; risk prediction [10]- [13]; and medication recommendation [14], [15]. A common supervised training approach to utilize EHR data for temporal event prediction is to use previous records as features and the records of next admissions as labels.…”

Section: Introductionmentioning

confidence: 99%

Self-Supervised Graph Learning With Hyperbolic Embedding for Temporal Health Event Prediction

Lü

Reddy

Ning

2023

IEEE Trans. Cybern.

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Electronic health records (EHRs) have been heavily used in modern healthcare systems for recording patients' admission information to health facilities. Many data-driven approaches employ temporal features in EHR for predicting specific diseases, readmission times, and diagnoses of patients. However, most existing predictive models cannot fully utilize EHR data, due to an inherent lack of labels in supervised training for some temporal events. Moreover, it is hard for the existing methods to simultaneously provide generic and personalized interpretability. To address these challenges, we propose Sherbet, a self-supervised graph learning framework with hyperbolic embeddings for temporal health event prediction. We first propose a hyperbolic embedding method with information flow to pretrain medical code representations in a hierarchical structure. We incorporate these pretrained representations into a graph neural network (GNN) to detect disease complications and design a multilevel attention method to compute the contributions of particular diseases and admissions, thus enhancing personalized interpretability. We present a new hierarchy-enhanced historical prediction proxy task in our self-supervised learning framework to fully utilize EHR data and exploit medical domain knowledge. We conduct a comprehensive set of experiments on widely used publicly available EHR datasets to verify the effectiveness of our model. Our results demonstrate the proposed model's strengths in both predictive tasks and interpretable abilities.

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“…In the data-driven method [4][5][6], because the aeroengine RUL prediction is essentially a time series forecasting problem, and the Recurrent Neural Network (RNN) is good at capturing time information and has apparent advantages in time series forecasting, long and short-term memory Network(LSTM) is a type of RNN, which is widely used in the field of RUL prediction [7]. Nevertheless, LSTM still has shortcomings in RUL prediction.…”

Section: Introductionmentioning

confidence: 99%

Transformer Model for Remaining Useful Life Prediction of Aeroengine

Yang

2022

J. Phys.: Conf. Ser.

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Accurate aeroengine remaining useful life (RUL) prediction plays a vital role in ensuring safe operation and reducing maintenance losses. In order to improve the accuracy of aeroengine RUL prediction, an aeroengine RUL prediction method based on the Transformer model is proposed, which gives greater weight to the characteristics of important time steps through self attention mechanism, and solves the memory degradation problem caused by too long sequence in engine RUL prediction, and excavates the complex mapping relationship between input features and aeroengine RUL. Experiments on the C-MAPSS data set show that the Transformer model can better predict the aeroengine RUL based on the aeroengine degradation data. Compared with the long short term memory network model, the root mean square error of the two sub data sets is reduced by 6.57% and 5.63% respectively.

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A Data-Driven Aero-Engine Degradation Prognostic Strategy

Cited by 85 publications

References 36 publications

Aircraft Engine Prognostics Based on Informative Sensor Selection and Adaptive Degradation Modeling with Functional Principal Component Analysis

Aircraft Engine Prognostics Based on Informative Sensor Selection and Adaptive Degradation Modeling with Functional Principal Component Analysis

Self-Supervised Graph Learning With Hyperbolic Embedding for Temporal Health Event Prediction

Transformer Model for Remaining Useful Life Prediction of Aeroengine

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