Prediction of Tool Remaining Useful Life Based on NHPP-WPHM

Zhang, Yingzhi; Guo, Guiming; Yang, Fang; Zheng, Yubin; Zhai, Fenli

doi:10.3390/math11081837

Cited by 4 publications

(3 citation statements)

References 22 publications

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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%

“…Lv et al [6] introduced a predictive maintenance strategy tailored for multi-component systems, combining data and model fusion through particle filtering and degradation distribution modeling to enhance RUL predictions. Zhang et al [7] integrated a non-homogeneous Poisson process and a Weibull proportional hazard model, along with intrinsic failure rate modeling, to predict RUL. While these conventional statistical-based RUL prediction methods have shown effectiveness, they often rely on prior knowledge of the system's underlying physics, posing limitations when such information is incomplete or absent.…”

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.

show abstract

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

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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.

show abstract