Adaptive Res-LSTM Attention-based Remaining Useful Lifetime Prognosis of Rolling Bearings
Predicting the Remaining Useful Lifetime (RUL) of bearings is crucial for the maintenance and reliability of rotating machinery. This paper presents a novel approach utilizing PRONOSTIA and XJTU-SY datasets for RUL prediction. The proposed methodology leverages Synchrosqueezing Wavelet Transform (SS...
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| Language: | English |
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The Prognostics and Health Management Society
2024-12-01
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| Series: | International Journal of Prognostics and Health Management |
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| Online Access: | https://papers.phmsociety.org/index.php/ijphm/article/view/4171 |
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| author | Boubker Najdi Mohammed Benbrahim Mohammed Nabil Kabbaj |
| author_facet | Boubker Najdi Mohammed Benbrahim Mohammed Nabil Kabbaj |
| author_sort | Boubker Najdi |
| collection | DOAJ |
| description | Predicting the Remaining Useful Lifetime (RUL) of bearings is crucial for the maintenance and reliability of rotating machinery. This paper presents a novel approach utilizing PRONOSTIA and XJTU-SY datasets for RUL prediction. The proposed methodology leverages Synchrosqueezing Wavelet Transform (SSWT) and Random Projection (RP) to extract significant features from vibration signals. These features are then fed into a Residual Network (ResNet) combined with a temporal attention layer, followed by a Long Short-Term Memory (LSTM) model, referred to as the Adaptive Residual Attention LSTM (ARAL), to assess the Health Indicator (HI) of the bearings. Notably, an exponential data labeling technique is employed instead of traditional linear labeling, enhancing the robustness of the HI assessment. Following the HI estimation, the three-sigma method is applied to identify the degradation starting point. Subsequently, Gaussian Process Regression (GPR) is utilized to predict the RUL from this point forward. The proposed method demonstrates superior performance compared to existing techniques, providing more accurate and reliable RUL predictions. Experimental results show that this integrated approach effectively captures the complex degradation patterns of bearings, making it a valuable tool for prognostics and health management in industrial applications. |
| format | Article |
| id | doaj-art-be6a813a4e7246b28d3be651a4a214b7 |
| institution | Kabale University |
| issn | 2153-2648 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | The Prognostics and Health Management Society |
| record_format | Article |
| series | International Journal of Prognostics and Health Management |
| spelling | doaj-art-be6a813a4e7246b28d3be651a4a214b72025-08-20T03:52:20ZengThe Prognostics and Health Management SocietyInternational Journal of Prognostics and Health Management2153-26482024-12-01161116https://doi.org/10.36001/ijphm.2025.v16i1.4171Adaptive Res-LSTM Attention-based Remaining Useful Lifetime Prognosis of Rolling BearingsBoubker Najdi0Mohammed Benbrahim1Mohammed Nabil Kabbaj2Systems Engineering, Modeling and Analysis Laboratory, Faculty of Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco.Systems Engineering, Modeling and Analysis Laboratory, Faculty of Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco.Systems Engineering, Modeling and Analysis Laboratory, Faculty of Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco.Predicting the Remaining Useful Lifetime (RUL) of bearings is crucial for the maintenance and reliability of rotating machinery. This paper presents a novel approach utilizing PRONOSTIA and XJTU-SY datasets for RUL prediction. The proposed methodology leverages Synchrosqueezing Wavelet Transform (SSWT) and Random Projection (RP) to extract significant features from vibration signals. These features are then fed into a Residual Network (ResNet) combined with a temporal attention layer, followed by a Long Short-Term Memory (LSTM) model, referred to as the Adaptive Residual Attention LSTM (ARAL), to assess the Health Indicator (HI) of the bearings. Notably, an exponential data labeling technique is employed instead of traditional linear labeling, enhancing the robustness of the HI assessment. Following the HI estimation, the three-sigma method is applied to identify the degradation starting point. Subsequently, Gaussian Process Regression (GPR) is utilized to predict the RUL from this point forward. The proposed method demonstrates superior performance compared to existing techniques, providing more accurate and reliable RUL predictions. Experimental results show that this integrated approach effectively captures the complex degradation patterns of bearings, making it a valuable tool for prognostics and health management in industrial applications.https://papers.phmsociety.org/index.php/ijphm/article/view/4171bearing rul prognosissignal processingdeep learningadaptive labelingpredictive maintenance |
| spellingShingle | Boubker Najdi Mohammed Benbrahim Mohammed Nabil Kabbaj Adaptive Res-LSTM Attention-based Remaining Useful Lifetime Prognosis of Rolling Bearings International Journal of Prognostics and Health Management bearing rul prognosis signal processing deep learning adaptive labeling predictive maintenance |
| title | Adaptive Res-LSTM Attention-based Remaining Useful Lifetime Prognosis of Rolling Bearings |
| title_full | Adaptive Res-LSTM Attention-based Remaining Useful Lifetime Prognosis of Rolling Bearings |
| title_fullStr | Adaptive Res-LSTM Attention-based Remaining Useful Lifetime Prognosis of Rolling Bearings |
| title_full_unstemmed | Adaptive Res-LSTM Attention-based Remaining Useful Lifetime Prognosis of Rolling Bearings |
| title_short | Adaptive Res-LSTM Attention-based Remaining Useful Lifetime Prognosis of Rolling Bearings |
| title_sort | adaptive res lstm attention based remaining useful lifetime prognosis of rolling bearings |
| topic | bearing rul prognosis signal processing deep learning adaptive labeling predictive maintenance |
| url | https://papers.phmsociety.org/index.php/ijphm/article/view/4171 |
| work_keys_str_mv | AT boubkernajdi adaptivereslstmattentionbasedremainingusefullifetimeprognosisofrollingbearings AT mohammedbenbrahim adaptivereslstmattentionbasedremainingusefullifetimeprognosisofrollingbearings AT mohammednabilkabbaj adaptivereslstmattentionbasedremainingusefullifetimeprognosisofrollingbearings |