Intelligent prognostics of bearings based on bidirectional long short-term memory and wavelet packet decomposition

Habbouche, Houssem; Benkedjouh, Tarak; Zerhouni, Noureddine

doi:10.1007/s00170-021-06814-z

Cited by 21 publications

(13 citation statements)

References 42 publications

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“…Research results have shown that valid results can be obtained when BI-LSTM is applied to the life expectancy model of manufacturing equipment [9]. In addition, another study proved that BI-LSTM is suitable for predicting the damage tendency of bearings [10].…”

Section: Introductionmentioning

confidence: 88%

Design and Implementation of an Explainable Bidirectional LSTM Model Based on Transition System Approach for Cooperative AI-Workers

et al. 2022

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Recently, interest in the Cyber-Physical System (CPS) has been increasing in the manufacturing industry environment. Various manufacturing intelligence studies are being conducted to enable faster decision-making through various reliable indicators collected from the manufacturing process. Artificial intelligence (AI) and Machine Learning (ML) have advanced enough to give various possibilities of predicting manufacturing time, which can help implement CPS in manufacturing environments, but it is difficult to secure reliability because it is difficult to understand how AI works, and although it can offer good results, it is often not applied to industries. In this paper, Bidirectional Long Short Term Memory (BI-LSTM) is used to predict process execution time, which is an indicator that can be used as a basis for CPS in the manufacturing process, and the Shapley Additive Explanations (SHAP) algorithm is used to explain how artificial intelligence works. The experimental results of this paper, applying manufacturing data, prove that the results derived from SHAP are effective for workers and AI to collaborate.

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Section: Introductionmentioning

confidence: 88%

Design and Implementation of an Explainable Bidirectional LSTM Model Based on Transition System Approach for Cooperative AI-Workers

et al. 2022

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“…Bian et al provided architectural analysis of multiple setups for their stacked BiLSTM to predict the health state [42]. In a similar approach, Habbouche et al used a stacked BiLSTM for RUL prediction [97]. However, they used wavelet packet decomposition to break the data into different frequencies, as those would hold the degradation patterns.…”

Section: ) Bidirectional Long Short-term Memorymentioning

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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“…Bian et al provided architectural analysis of multiple setups for their stacked BiLSTM to predict the health state [42]. In a similar approach, Habbouche et al used a stacked BiLSTM for RUL prediction [112]. However, they used wavelet packet decomposition to break the data into different frequencies, as those would hold the degradation patterns.…”

Section: ) Bidirectional Long Short-term Memorymentioning

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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Intelligent prognostics of bearings based on bidirectional long short-term memory and wavelet packet decomposition

Cited by 21 publications

References 42 publications

Design and Implementation of an Explainable Bidirectional LSTM Model Based on Transition System Approach for Cooperative AI-Workers

Design and Implementation of an Explainable Bidirectional LSTM Model Based on Transition System Approach for Cooperative AI-Workers

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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