A Machine Learning and Feature Engineering Approach for the Prediction of the Uncontrolled Re-Entry of Space Objects

Salmaso, Francesco; Trisolini, Mirko; Colombo, Camilla

doi:10.3390/aerospace10030297

Cited by 6 publications

(4 citation statements)

References 25 publications

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“…Additionally, the method's reliance on accurate and comprehensive training data may limit its effectiveness in dynamic and evolving space environments. Another research group (Salmaso et al, 2023) proposed a deep learning model for predicting the re-entry of uncontrolled objects in Low Earth Orbit (LEO) based on a modi ed Sequence-to-Sequence architecture. Trained on average altitude pro les from Two-Line Element (TLE) data of over 400 bodies, the model introduces novel input features, including a drag-like coe cient (𝐵 * ), average solar index, and area-to-mass ratio.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Space Debris detection and monitoring using a hybrid Bi-LSTM- CNN and Bayesian Optimization

Priyadarshini

2024

Preprint

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Monitoring space debris is vital for ensuring the safety of space missions and satellite operations amid the increasing number of satellites and spacecraft in orbit. The study addresses this challenge by proposing a novel approach based on a hybrid Bi-LSTM-CNN architecture optimized using Bayesian Optimization. Through extensive analysis utilizing machine learning and deep learning techniques, the study develops a robust space debris detection system capable of classifying both the object type and Radar Cross Section (RCS) size. The proposed method outperforms existing approaches by demonstrating superior performance across multiple evaluation metrics, including accuracy, precision, recall, and F1 score. Moreover, the study considers the practical aspect of training time, ensuring efficiency in real-time applications. Empirical validation on real-world datasets confirms the effectiveness and efficiency of the hybrid model in accurately detecting and predicting space debris types. Overall, this research significantly advances space debris monitoring capabilities, mitigating risks associated with space exploration and satellite operations, and offers comprehensive insights into potential hazards and optimizing mitigation strategies.

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Section: Related Workmentioning

confidence: 99%

Enhanced Space Debris detection and monitoring using a hybrid Bi-LSTM- CNN and Bayesian Optimization

Priyadarshini

2024

Preprint

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“…Certain sensors, for instance, exhibit constant measurements throughout the entire life cycle. To mitigate computational complexity, we adopt the approach outlined in [40], selectively incorporating data from 14 sensors (sensors 2, 3,4,7,8,9,11,12,13,14,15,17,20,21) into our training process. Recognizing the disparate numerical ranges resulting from distinct sensor measurements, we also employ a min-max normalization technique by using the following formula:…”

Section: C-mapss Dataset and Preprocessingmentioning

confidence: 99%

“…As data volume and computing capabilities continue to expand, artificial intelligence and machine learning (AI/ML) have found success in applications across various domains, including cyber security [9,10], geology [11,12], aerospace engineering [13,14], and transportation [15,16]. In parallel, the focus of research on data-driven approaches for RUL estimation is in the process of transitioning from conventional statistical-based probabilistic techniques to AI/ML methods.…”

Section: Introductionmentioning

confidence: 99%

A Bidirectional Long Short-Term Memory Autoencoder Transformer for Remaining Useful Life Estimation

Fan,

Li,

Chang

2023

Mathematics

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Estimating the remaining useful life (RUL) of aircraft engines holds a pivotal role in enhancing safety, optimizing operations, and promoting sustainability, thus being a crucial component of modern aviation management. Precise RUL predictions offer valuable insights into an engine’s condition, enabling informed decisions regarding maintenance and crew scheduling. In this context, we propose a novel RUL prediction approach in this paper, harnessing the power of bi-directional LSTM and Transformer architectures, known for their success in sequence modeling, such as natural languages. We adopt the encoder part of the full Transformer as the backbone of our framework, integrating it with a self-supervised denoising autoencoder that utilizes bidirectional LSTM for improved feature extraction. Within our framework, a sequence of multivariate time-series sensor measurements serves as the input, initially processed by the bidirectional LSTM autoencoder to extract essential features. Subsequently, these feature values are fed into our Transformer encoder backbone for RUL prediction. Notably, our approach simultaneously trains the autoencoder and Transformer encoder, different from the naive sequential training method. Through a series of numerical experiments carried out on the C-MAPSS datasets, we demonstrate that the efficacy of our proposed models either surpasses or stands on par with that of other existing methods.

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“…Certain sensors, for instance, exhibit constant measurements throughout the entire life cycle. mitigate computational complexity, we adopt the approach outlined in [35], selectively incorporating data from 14 sensors (sensors 2, 3,4,7,8,9,11,12,13,14,15,17,20,21) into our training process.…”

Section: C-mapss Dataset and Preprocessingmentioning

confidence: 99%

Bidirectional LSTM Autoencoder Transformer for Remaining Useful Life Estimation

Fan,

Li,

Chang

2023

Preprint

View full text Add to dashboard Cite

Estimating the Remaining Useful Life (RUL) of aircraft engines holds a pivotal role in enhancing safety, optimizing operations, and promoting sustainability, thus being a crucial component of modern aviation management. Precise RUL predictions offer valuable insights into an engine’s condition, enabling informed decisions regarding maintenance and crew scheduling. In this context, we propose a novel RUL prediction approach in this paper, harnessing the power of Bi-directional LSTM and Transformer architectures, known for their success in sequence modeling, such as natural languages. We adopt the encoder part of the full Transformer as the backbone of our framework, integrating it with a self-supervised denoising autoencoder that utilizes Bidirectional LSTM for improved feature extraction. Within our framework, a sequence of multivariate time series sensor measurements serves as the input, initially processed by the Bidirectional LSTM autoencoder to extract essential features. Subsequently, these feature values are fed into our Transformer encoder backbone for RUL prediction. Notably, our approach simultaneously trains the autoencoder and Transformer encoder, different from the naive sequential training method. Through a series of numerical experiments carried out on the C-MAPSS datasets, we demonstrate that the efficacy of our proposed models either surpasses or stands on par with that of other existing methods.

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A Machine Learning and Feature Engineering Approach for the Prediction of the Uncontrolled Re-Entry of Space Objects

Cited by 6 publications

References 25 publications

Enhanced Space Debris detection and monitoring using a hybrid Bi-LSTM- CNN and Bayesian Optimization

Enhanced Space Debris detection and monitoring using a hybrid Bi-LSTM- CNN and Bayesian Optimization

A Bidirectional Long Short-Term Memory Autoencoder Transformer for Remaining Useful Life Estimation

Bidirectional LSTM Autoencoder Transformer for Remaining Useful Life Estimation

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