Unsupervised Convolutional Transformer Autoencoder for Robust Health Indicator Construction and RUL Prediction in Rotating Machinery
Prognostics for rotating machinery, particularly bearings, encounter significant challenges in constructing reliable health indicators (HIs) that accurately reflect degradation trajectories, thereby enabling precise remaining useful life (RUL) predictions. This article proposes a novel integrated ap...
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| Abstract | Prognostics for rotating machinery, particularly bearings, encounter significant challenges in constructing reliable health indicators (HIs) that accurately reflect degradation trajectories, thereby enabling precise remaining useful life (RUL) predictions. This article proposes a novel integrated approach for predicting the RUL of bearings without manual feature engineering. Specifically, a sequential autoencoder integrating a convolutional neural network (CNN) and vision Transformer (Vi-T) is employed to capture the local spatial patterns and global temporal correlations of time-domain vibration signals. The Wasserstein distance is introduced to quantify the divergence between healthy and degraded signal embeddings, resulting in a robust HI metric. Subsequently, the derived HI is fed into a CNN-bidirectional long short-term memory-regressor with Monte Carlo dropout to provide RUL predictions and Bayesian uncertainty estimates. Experimental results from the Xi’an Jiao-Tong University bearing dataset demonstrate that the proposed method surpasses conventional techniques in HI construction and RUL prediction accuracy, demonstrating its efficacy for complex industrial systems with minimal data preprocessing. |
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| AbstractList | Prognostics for rotating machinery, particularly bearings, encounter significant challenges in constructing reliable health indicators (HIs) that accurately reflect degradation trajectories, thereby enabling precise remaining useful life (RUL) predictions. This article proposes a novel integrated approach for predicting the RUL of bearings without manual feature engineering. Specifically, a sequential autoencoder integrating a convolutional neural network (CNN) and vision Transformer (Vi-T) is employed to capture the local spatial patterns and global temporal correlations of time-domain vibration signals. The Wasserstein distance is introduced to quantify the divergence between healthy and degraded signal embeddings, resulting in a robust HI metric. Subsequently, the derived HI is fed into a CNN-bidirectional long short-term memory-regressor with Monte Carlo dropout to provide RUL predictions and Bayesian uncertainty estimates. Experimental results from the Xi’an Jiao-Tong University bearing dataset demonstrate that the proposed method surpasses conventional techniques in HI construction and RUL prediction accuracy, demonstrating its efficacy for complex industrial systems with minimal data preprocessing. |
| Audience | Academic |
| Author | Dahal, Amrit Huang, Hong-Zhong Huang, Tudi Huang, Cheng-Geng Khanal, Smaran Niazi, Sajawal Gul |
| Author_xml | – sequence: 1 givenname: Amrit orcidid: 0009-0004-2016-7390 surname: Dahal fullname: Dahal, Amrit – sequence: 2 givenname: Hong-Zhong orcidid: 0000-0003-4478-8349 surname: Huang fullname: Huang, Hong-Zhong – sequence: 3 givenname: Cheng-Geng surname: Huang fullname: Huang, Cheng-Geng – sequence: 4 givenname: Tudi surname: Huang fullname: Huang, Tudi – sequence: 5 givenname: Smaran surname: Khanal fullname: Khanal, Smaran – sequence: 6 givenname: Sajawal Gul orcidid: 0000-0003-2144-9563 surname: Niazi fullname: Niazi, Sajawal Gul |
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| Cites_doi | 10.1016/j.ress.2024.110014 10.3390/e25050798 10.1016/j.jmapro.2024.07.002 10.1016/j.ymssp.2023.110888 10.1016/j.ress.2025.111722 10.1016/j.neucom.2017.02.045 10.1109/JIOT.2024.3433460 10.1016/j.ress.2024.110225 10.1109/ACCESS.2023.3234286 10.1016/j.engappai.2023.106817 10.1109/ICPHM.2019.8819405 10.1016/j.measurement.2022.112108 10.1016/j.ress.2022.108758 10.1109/DDCLS58216.2023.10166306 10.1016/j.ymssp.2017.11.016 10.1016/j.ress.2018.11.011 10.1109/JSEN.2025.3548675 10.20944/preprints202011.0591.v1 10.1016/j.asoc.2020.106119 10.1016/j.ress.2023.109776 10.1016/j.ymssp.2022.109628 10.1109/JIOT.2024.3361533 10.1109/TIM.2025.3556193 10.21595/jve.2016.16910 10.1109/TII.2022.3169465 10.1016/j.ress.2024.110116 10.1088/3050-2454/adbaf7 10.1016/j.ymssp.2024.111122 10.1016/j.jmsy.2021.03.012 10.1016/j.ymssp.2021.108573 10.1109/TR.2011.2182221 10.1016/j.cie.2023.108999 10.1016/j.aei.2022.101708 10.1016/j.engappai.2022.105582 10.1109/TIE.2015.2455055 10.1109/TR.2016.2570568 10.1088/1361-6501/ace072 |
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| SubjectTerms | Algorithms Artificial intelligence Bearings Datasets Electric transformers Entropy Forecasts and trends Health health indicator hybrid convolutional vision transformer Machinery Neural networks Predictive maintenance Principal components analysis remaining useful life Signal processing Time series unsupervised learning Vibration |
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| Title | Unsupervised Convolutional Transformer Autoencoder for Robust Health Indicator Construction and RUL Prediction in Rotating Machinery |
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