Remaining Useful Life Prediction for Circuit Breaker Based on Opening-Related Vibration Signal and SA-CNN-GRU

Sun, Shuguang; Wei, Shuo; Wang, Jingqin; Xu, Sheng; Liu, Jinfa; Gao, Hui

doi:10.1109/jsen.2022.3214608

Cited by 16 publications

(3 citation statements)

References 24 publications

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“…This could include using a network (e.g., LSTM) to preprocess the data for subsequent processes or networks (e.g., Wiener process) [94], or using different networks in parallel. For example, a CNN-GRU [224], CNN-LSTM [121], TCN-LSTM [225], or LSTM-PF [188] architecture can combine the feature extraction capabilities of the CNN and the temporal modeling capabilities of the GRU or LSTM to create a superior deep learning architecture [98,126]. Since CNNs are designed to handle local, short-term dependencies, they can benefit from other methods that deal with long-term dependencies [50].…”

Section: ) Collaborative Architecturesmentioning

confidence: 99%

Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review

Ramezani¹,

Cummins²,

Killen³

et al. 2023

Preprint

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<ul> <li>This work summarizes the state-of-the-art data-driven methods for prediction of the Remaining Useful Life<br> (RUL)<br> </li> <li>It discusses challenges and open problems faced in PdM<br> </li> <li>This study presents a discussion on the new problems that need to be considered towards the Industry 4.0 goals<br> </li> <li>We propose the future direction for each challenge discussed in this article</li> </ul>

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Section: ) Collaborative Architecturesmentioning

confidence: 99%

Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review

Ramezani¹,

Cummins²,

Killen³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…This could include using a network (e.g., LSTM) to preprocess the data for subsequent processes or networks (e.g., Wiener process) [109], or using different networks in parallel. For example, a CNN-GRU [243], CNN-LSTM [136], TCN-LSTM [244], or LSTM-PF [204] architecture can combine the feature extraction capabilities of the CNN and the temporal modeling capabilities of the GRU or LSTM to create a superior deep learning architecture [113,141]. Since CNNs are designed to handle local, short-term dependencies, they can benefit from other methods that deal with long-term dependencies [50].…”

Section: ) Collaborative Architecturesmentioning

confidence: 99%

Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review

et al. 2023

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Early detection of faulty patterns and timely scheduling of maintenance events can minimize risk to the underlying processes and increase a system's lifespan, reliability, and availability. Two main data-driven approaches are used in the literature to determine the Remaining Useful Life (RUL): direct calculation from raw data and indirect analysis by revealing the transitions from one latent state to another and highlighting degradation in a system's Health Indices. The present study discusses the state-of-theart data-driven methods introduced for RUL prediction in predictive maintenance, by looking at their capabilities, scalability, performance, and weaknesses. We will also discuss the challenges faced with the current approaches and the future directions to tackle the current limitations.

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“…This could include using a network (e.g., LSTM) to pre-process the data for subsequent processes or networks (e.g., Wiener process) [109], or using different networks in parallel. For example, a CNN-GRU [243], CNN-LSTM [136], TCN-LSTM [244], or LSTM-PF [204] architecture can combine the feature extraction capabilities of the CNN and the temporal modeling capabilities of the GRU or LSTM to create a superior deep learning architecture [113], [141]. Since CNNs are designed to handle local, short-term dependencies, they can benefit from other methods that deal with long-term dependencies [50].…”

Section: ) Collaborative Architecturesmentioning

confidence: 99%

Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review

Ramezani¹,

Cummins²,

Killen³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Remaining Useful Life Prediction for Circuit Breaker Based on Opening-Related Vibration Signal and SA-CNN-GRU

Cited by 16 publications

References 24 publications

Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review

Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review

Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review

Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review

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