Multi-scale memory-enhanced method for predicting the remaining useful life of aircraft engines

Chen, Wenbai; Liu, Chang; Chen, Qili; Wu, Peiliang

doi:10.1007/s00521-022-07378-z

Cited by 12 publications

(4 citation statements)

References 33 publications

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“…The width of the 1D convolution kernel is the same as the sample, while the length and amount of convolution kernels are artificially set as hyperparameters. However, a single-length 1D convolutional kernel is not sufficient to extract the complex degradation features of the engine, so the Multi-Scale Convolutional Neural Network (MSCNN) is necessary [24]. The applied to feature extraction [23].…”

Section: Multi-scale Convolutional Neural Networkmentioning

confidence: 99%

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Remaining Useful Life Prediction for Aircraft Engines under High-Pressure Compressor Degradation Faults Based on FC-AMSLSTM

Peng,

Wang,

Liu

et al. 2024

Aerospace

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The healthy operation of aircraft engines is crucial for flight safety, and accurate Remaining Useful Life prediction is one of the core technologies involved in aircraft engine prognosis and health management. In recent years, deep learning-based predictive methods within data-driven approaches have shown promising performance. However, for engines experiencing a single fault, such as a High-Pressure Compressor fault, existing deep learning-based predictive methods often face accuracy challenges due to the coupling relationship between different fault modes in the training dataset that includes a mixture of multiple fault modes. In this paper, we propose the FC-AMSLSTM method, a novel approach for Remaining Useful Life prediction specifically targeting High-Pressure Compressor degradation faults. The proposed method effectively addresses the limitations of previous approaches by fault classification and decoupling fault modes from multiple operating conditions using a decline index. Then, attention mechanisms and multi-scale convolutional neural networks are employed to extract spatiotemporal features. The long short-term memory network is then utilized to model RUL estimation. The experiments are conducted using the Commercial Modular Aero-Propulsion System Simulation dataset provided by NASA. The results demonstrate that compared to other prediction models, the FC-AMSLSTM method effectively reduces RUL prediction error for HPC degradation faults under multiple operating conditions.

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Section: Multi-scale Convolutional Neural Networkmentioning

confidence: 99%

“…After that, the failure rate increases rapidly as the engine's running time increases. Following previous work [24], a piecewise RUL labeling method was used, as shown in Figure 9. During the early stage of engine operation, RUL of the engine keeps a constant value, and then linearly decline until complete failure.…”

Section: Data Preprocessingmentioning

confidence: 99%

Remaining Useful Life Prediction for Aircraft Engines under High-Pressure Compressor Degradation Faults Based on FC-AMSLSTM

Peng,

Wang,

Liu

et al. 2024

Aerospace

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“…Machine Learning (ML) is a popular data-driven approach that has been extensively used in predicting the RUL of rotating machines. Several studies, including [11][12][13][14], have employed well-known ML models such as Linear Regression (LR), Random Forest (RF), and Support Vector Machines (SVM) to forecast RUL. However, these methods have some significant drawbacks, such as suboptimal performance due to inflexible mathematical formulas and time-consuming computations for big input data.…”

Section: Introductionmentioning

confidence: 99%

Robust-MBDL: A Robust Multi-Branch Deep-Learning-Based Model for Remaining Useful Life Prediction of Rotating Machines

Tran,

Vu,

Pham

et al. 2024

Mathematics

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Predictive maintenance (PdM) is one of the most powerful maintenance techniques based on the estimation of the remaining useful life (RUL) of machines. Accurately estimating the RUL is crucial to ensure the effectiveness of PdM. However, current methods have limitations in fully exploring condition monitoring data, particularly vibration signals, for RUL estimation. To address these challenges, this research presents a novel Robust Multi-Branch Deep Learning (Robust-MBDL) model. Robust-MBDL stands out by leveraging diverse data sources, including raw vibration signals, time–frequency representations, and multiple feature domains. To achieve this, it adopts a specialized three-branch architecture inspired by efficient network designs. The model seamlessly integrates information from these branches using an advanced attention-based Bi-LSTM network. Furthermore, recognizing the importance of data quality, Robust-MBDL incorporates an unsupervised LSTM-Autoencoder for noise reduction in raw vibration data. This comprehensive approach not only overcomes the limitations of existing methods but also leads to superior performance. Experimental evaluations on benchmark datasets such as XJTU-SY and PRONOSTIA showcase Robust-MBDL’s efficacy, particularly in rotating machine health prognostics. These results underscore its potential for real-world applications, heralding a new era in predictive maintenance practices.

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“…Zhang et al [14] uses it to understand the distribution law of traffic data and the internal connections between data, excavate traffic characteristics in smart cities, and accurately predict the future traffic situation, so as to solve the problems of traffic congestion and route planning in smart cities. A multi-scale memory-enhanced prediction method is proposed to describe fully characteristics of the data [15]. Li [16] presents an in-depth study and analysis of the AC drive control simulation of a supercapacitor tram using a high-order neural network pattern discrimination algorithm.…”

mentioning

confidence: 99%

Special issue on 2021 international conference on machine learning and big data analytics for iot security and privacy

Jing-hua

2022

Neural Comput & Applic

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Multi-scale memory-enhanced method for predicting the remaining useful life of aircraft engines

Cited by 12 publications

References 33 publications

Remaining Useful Life Prediction for Aircraft Engines under High-Pressure Compressor Degradation Faults Based on FC-AMSLSTM

Remaining Useful Life Prediction for Aircraft Engines under High-Pressure Compressor Degradation Faults Based on FC-AMSLSTM

Robust-MBDL: A Robust Multi-Branch Deep-Learning-Based Model for Remaining Useful Life Prediction of Rotating Machines

Special issue on 2021 international conference on machine learning and big data analytics for iot security and privacy

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