Computing River Discharge Using Water Surface Elevation Based on Deep Learning Networks

Liu, Wei; Zou, Peng; Jiang, Dingguo; Quan, Xiufeng; Dai, Huichao

doi:10.3390/w15213759

Cited by 2 publications

(3 citation statements)

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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%

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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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Section: C-mapss Dataset and Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Fan,

Li,

Chang

2023

Mathematics

View full text Add to dashboard Cite

show abstract

“…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.

show abstract

Computing River Discharge Using Water Surface Elevation Based on Deep Learning Networks

Cited by 2 publications

References 40 publications

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

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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