A novel long-short term memory network approach for stress model updating for excavations in high stress environments

Morgenroth, Josephine; Kalenchuk, K.; Moreau–Verlaan, Lindsay; Perras, Matthew A.; Khan, Usman T.

doi:10.1080/17499518.2023.2182889

Cited by 6 publications

(2 citation statements)

References 49 publications

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“…RUL Prediction: Once the LSTM network is trained, it can be used to predict the remaining useful life of rolling bearings [21]. Given a new sequence of vibration data, the LSTM network processes the sequence through the trained network and generates a predicted RUL value based on the learned temporal patterns and dependencies.…”

Section: Long Short-term Memory Recurrent Neural Networkmentioning

confidence: 99%

Remaining Useful Life Prediction of Rolling Bearings Based on Multi-scale Permutation Entropy and SSA-LSTM

Wang,

Zhang,

Ren

et al. 2023

Preprint

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The performance of bearings plays a pivotal role in determining the dependability and security of rotating machinery. In intricate systems demanding exceptional reliability and safety, the ability to accurately forecast fault occurrences during operation holds profound significance. Such predictions serve as invaluable guides for crafting well-considered reliability strategies and executing maintenance practices aimed at enhancing reliability. In order to ensure the reliability of bearing operation, this article investigates the application of three advanced techniques—Maximum Correlation Kurtosis Deconvolution (MCKD), Multi-Scale Permutation Entropy (MPE), and Long Short-Term Memory (LSTM) recurrent neural networks—for the prediction of the remaining useful life (RUL) of rolling bearings. Each technique's principles, methodologies, and applications are comprehensively reviewed, offering insights into their respective strengths and limitations. Case studies and experimental evaluations are presented to assess their performance in RUL prediction. Findings reveal that MCKD enhances fault signatures, MPE captures complexity, and LSTM excels in modeling temporal patterns. The root mean square error of the prediction results is 0.007. The fusion of these techniques offers a comprehensive approach to RUL prediction, leveraging their unique attributes for more accurate and reliable predictions.

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Section: Long Short-term Memory Recurrent Neural Networkmentioning

confidence: 99%

Remaining Useful Life Prediction of Rolling Bearings Based on Multi-scale Permutation Entropy and SSA-LSTM

Wang,

Zhang,

Ren

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…RUL Prediction: Once the LSTM network is trained, it can be used to predict the remaining useful life of rolling bearings [44]. Given a new sequence of vibration data, the LSTM network processes the sequence through the trained network and generates a predicted RUL value based on the learned temporal patterns and dependencies.…”

Section: Issa-lstmmentioning

confidence: 99%

Remaining Useful Life Prediction of Rolling Bearings Based on Multi-scale Permutation Entropy and ISSA-LSTM

Wang,

Zhang,

Ren

et al. 2023

Entropy

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The performance of bearings plays a pivotal role in determining the dependability and security of rotating machinery. In intricate systems demanding exceptional reliability and safety, the ability to accurately forecast fault occurrences during operation holds profound significance. Such predictions serve as invaluable guides for crafting well-considered reliability strategies and executing maintenance practices aimed at enhancing reliability. In the real operational life of bearings, fault information often gets submerged within the noise. Furthermore, employing Long Short-Term Memory (LSTM) neural networks for time series prediction necessitates the configuration of appropriate parameters. Manual parameter selection is often a time-consuming process and demands substantial prior knowledge. In order to ensure the reliability of bearing operation, this article investigates the application of three advanced techniques—Maximum Correlation Kurtosis Deconvolution (MCKD), Multi-Scale Permutation Entropy (MPE), and Long Short-Term Memory (LSTM) recurrent neural networks—for the prediction of the remaining useful life (RUL) of rolling bearings. The improved sparrow search algorithm (ISSA) is employed for configuring parameters in the Long Short-Term Memory (LSTM) network. Each technique’s principles, methodologies, and applications are comprehensively reviewed, offering insights into their respective strengths and limitations. Case studies and experimental evaluations are presented to assess their performance in RUL prediction. Findings reveal that MCKD enhances fault signatures, MPE captures complexity, and LSTM excels in modeling temporal patterns. The root mean square error of the prediction results is 0.007. The fusion of these techniques offers a comprehensive approach to RUL prediction, leveraging their unique attributes for more accurate and reliable predictions.

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Special issue on “Machine learning and AI in geotechnics”

Phoon

Zhang

Cao

2023

Georisk: Assessment and Management of Risk for Engineered Syste

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A novel long-short term memory network approach for stress model updating for excavations in high stress environments

Cited by 6 publications

References 49 publications

Remaining Useful Life Prediction of Rolling Bearings Based on Multi-scale Permutation Entropy and SSA-LSTM

Remaining Useful Life Prediction of Rolling Bearings Based on Multi-scale Permutation Entropy and SSA-LSTM

Remaining Useful Life Prediction of Rolling Bearings Based on Multi-scale Permutation Entropy and ISSA-LSTM

Special issue on “Machine learning and AI in geotechnics”

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