Correlation analysis of aeroengine operation monitoring using deep learning

Xie, Changjun; Zhang, Peng; Yan, Zhi

doi:10.1007/s00500-020-05166-2

Cited by 6 publications

(2 citation statements)

References 27 publications

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“…Therefore, to predict the remaining useful life (RUL) of aeroengines more accurately, a shift from shallow machine learning models to more advanced deep learning models is necessary (Zhao et al, 2023).In recent years, deep learning-based life prediction models have achieved remarkable results, demonstrating high prediction accuracy and efficiency, including Convolutional Neural Networks (CNN) (Zhang et al, 2024), Autoencoders (AE) (Remadna et al, 2023), Deep Belief Networks (DBN) (Peng et al, 2020), and other methods. Xie et al (2021) utilized an ensemble network of deep learning to predict the RUL of aero-engines and fitted key parameters using the Backpropagation (BP) algorithm. Li et al (2018a) proposed a novel data-driven method using a deep convolutional neural network (DCNN) to effectively extract critical features, thus achieving accuracy in RUL prediction.…”

Section: Introductionmentioning

confidence: 99%

Prediction of remaining useful life of aero-engines based on CNN-LSTM- Attention

Deng,

Zhou

2024

Preprint

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Predicting an aircraft engine's remaining life with accuracy is crucial for maintaining both financial stability and aviation safety. The capacity to analyze spatiotemporal data enhances machine RUL's predictive performance. Nevertheless, the majority of spatiotemporal information processing models now in use lack the ability to extract adaptive features in addition to having complicated topologies. In light of this, the paper suggests a deep learning-based approach to forecast an engine's remaining life. This method has the potential to enhance data feature recognition and, as a result, boost model prediction accuracy. First, we standardize the input features and compute the aircraft engine dataset test set's Remaining Useful Life (RUL) using CMAPSS. After extracting characteristics from the input data using a CNN (Convolutional Neural Network), the extracted data is fed into an LSTM model (Long Short Term Memory) and attention (multi head attention) is added to forecast how long the aircraft engine would last. Finally, ablation study and comparison model experiments were used to evaluate and compare the suggested aircraft engine model. the research results show that the CNN-LSTM-Attention model performs well predictively on the dataset and its Root Mean Square Error (RMSE) of 15.306, 12.913, 12.507, and 13.902 in FD001, FD002, FD003, and FD004, respectively,. In comparison to other models, our proposed model has the best prediction accuracy on the CMPASS dataset, indicating strong reliability and accuracy.

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Section: Introductionmentioning

confidence: 99%

Prediction of remaining useful life of aero-engines based on CNN-LSTM- Attention

Deng,

Zhou

2024

Preprint

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“…Among these accidents, aeroengine wear failure is a significant cause [ 2 , 3 ]. As we know, with the development of big data, 5G technology, and artificial intelligence, intelligent aeroengine has become an inevitable trend in the field of aeroengine manufacturing and maintenance [ 4 ]. The development of intelligent aeroengines is closely related to the intellectualization of the compressor, turbine, lubricating oil system, and other essential parts of the aeroengine, which all contain a large number of supporting components, such as bearings and gears.…”

Section: Introductionmentioning

confidence: 99%

A Lubricating Oil Condition Monitoring System Based on Wear Particle Kinematic Analysis in Microfluid for Intelligent Aeroengine

Liu

Zuo

et al. 2021

Micromachines

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Lubricating oil monitoring technology is a commonly used method in aeroengine condition monitoring, which includes particle counting technology, as well as spectral and ferrography technology in offline monitoring. However, these technologies only analyze the characteristics of wear particles and rely on physical and chemical analysis techniques to monitor the oil quality. In order to further advance offline monitoring technology, this paper explores the potential role of differences in wear particle kinematic characteristics in recognizing changes in wear particle diameter and oil viscosity. Firstly, a kinematic force analysis of the wear particles in the microfluid was carried out. Accordingly, a microfluidic channel conducive to observing the movement characteristics of particles was designed. Then, the wear particle kinematic analysis system (WKAS) was designed and fabricated. Secondly, a real-time tracking velocity measurement algorithm was developed by using the Gaussian mixture model (GMM) and the blob-tracking algorithm. Lastly, the WKAS was applied to a pin–disc tester, and the experimental results show that there is a corresponding relationship between the velocity of the particles and their diameter and the oil viscosity. Therefore, WKAS provides a new research idea for intelligent aeroengine lubricating oil monitoring technology. Future work is needed to establish a quantitative relationship between wear particle velocity and particle diameter, density, and oil viscosity.

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Health Assessment and RUL Prediction of Aeroengine Under Multiple Working Conditions

Zhang

2023

Lecture Notes in Electrical Engineering

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Correlation analysis of aeroengine operation monitoring using deep learning

Cited by 6 publications

References 27 publications

Prediction of remaining useful life of aero-engines based on CNN-LSTM- Attention

Prediction of remaining useful life of aero-engines based on CNN-LSTM- Attention

A Lubricating Oil Condition Monitoring System Based on Wear Particle Kinematic Analysis in Microfluid for Intelligent Aeroengine

Health Assessment and RUL Prediction of Aeroengine Under Multiple Working Conditions

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