Attention-based LSTM for Remaining Useful Life Estimation of Aircraft Engines

Boujamza, Abdeltif; Elhaq, Saâd Lissane

doi:10.1016/j.ifacol.2022.07.353

Cited by 20 publications

(5 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to evaluate the prediction accuracy of different methods, this paper uses three evaluation metrics that are widely used in the field of aircraft engine RUL prediction [14]. One is RMSE (Root Mean Square Error), another is Score, and the third is AS (Average Score).…”

Section: Evaluation Metricsmentioning

confidence: 99%

See 1 more Smart Citation

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

Peng,

Wang,

Liu

et al. 2024

Aerospace

View full text Add to dashboard Cite

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.

show abstract

Section: Evaluation Metricsmentioning

confidence: 99%

“…Boujamza et al [14] proposed a method for the prediction of RUL based on attention and LSTM. Liu et al [15] proposed a double attention-based data-driven framework for aircraft engine RUL prognostics.…”

Section: Introductionmentioning

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

View full text Add to dashboard Cite

show abstract

“…Various RUL prediction models for aviation engines are reviewed, evaluated, and their performance is contrasted with a suggested Long-Short Term Memory (LSTM) method based on a data-driven machine learning approach. The results collected demonstrate that the modified LSTM approach with Attention mechanism enhances and gives higher performance for RUL prediction for aircraft engines [35]. Long Short-Term Memory (LSTM) neural networks are being used in another study to achieve good diagnosis and prediction performance in the presence of complex procedures, hybrid errors, and significant noise.…”

Section: Literature Reviewmentioning

confidence: 99%

An Investigation of Exhaust Gas Temperature of Aircraft Engine Using LSTM

Ullah¹,

Li²,

Khan

et al. 2023

IEEE Access

View full text Add to dashboard Cite

A significant obstacle to creating efficient machine health monitoring systems is estimating performance degradation in dynamic systems, like aero plane engines. In exceedingly complex systems with many components, states, and parameters, conventional model-based and data-driven methods fall short of producing satisfactory results. While traditional methods had several drawbacks, deep learning has emerged as a viable computational tool for dynamic system prediction. In order to track system deterioration and estimate the EGT, a novel technique based on the Long Short-Term Memory (LSTM) network, (an architecture created to find the hidden patterns hidden in time series data) is provided in this research. The health monitoring information of aircraft turbofan engines is used to assess the effectiveness of the proposed strategy. As a result of this network's ability to recognize the input data as a real-time series, the output in the following step can be predicted. Results of the suggested study show a significant ability to anticipate the output in the following time step. Additionally, the proposed model has a shorter learning curve and is more accurate.

show abstract

“…Reference [69] conducts a comprehensive analysis and assessment of different prognostic models for aviation engines' RUL and seeks to compare the effectiveness of these models with an LSTM technique, which utilizes a data-driven machine learning approach. This paper utilizes the C-MAPSS datasets to assess the performance and outcomes of each technique.…”

Section: Recurrent Neural Network (Rnn) and Long Short-term Memory (L...mentioning

confidence: 99%

Prognostic and Health Management of Critical Aircraft Systems and Components: An Overview

Fu,

Avdelidis

2023

Sensors

View full text Add to dashboard Cite

Prognostic and health management (PHM) plays a vital role in ensuring the safety and reliability of aircraft systems. The process entails the proactive surveillance and evaluation of the state and functional effectiveness of crucial subsystems. The principal aim of PHM is to predict the remaining useful life (RUL) of subsystems and proactively mitigate future breakdowns in order to minimize consequences. The achievement of this objective is helped by employing predictive modeling techniques and doing real-time data analysis. The incorporation of prognostic methodologies is of utmost importance in the execution of condition-based maintenance (CBM), a strategic approach that emphasizes the prioritization of repairing components that have experienced quantifiable damage. Multiple methodologies are employed to support the advancement of prognostics for aviation systems, encompassing physics-based modeling, data-driven techniques, and hybrid prognosis. These methodologies enable the prediction and mitigation of failures by identifying relevant health indicators. Despite the promising outcomes in the aviation sector pertaining to the implementation of PHM, there exists a deficiency in the research concerning the efficient integration of hybrid PHM applications. The primary aim of this paper is to provide a thorough analysis of the current state of research advancements in prognostics for aircraft systems, with a specific focus on prominent algorithms and their practical applications and challenges. The paper concludes by providing a detailed analysis of prospective directions for future research within the field.

show abstract

Attention-based LSTM for Remaining Useful Life Estimation of Aircraft Engines

Cited by 20 publications

References 20 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

An Investigation of Exhaust Gas Temperature of Aircraft Engine Using LSTM

Prognostic and Health Management of Critical Aircraft Systems and Components: An Overview

Contact Info

Product

Resources

About