Similarity-Based Remaining Useful Lifetime Prediction Method Considering Epistemic Uncertainty
Measuring the similarity between two trajectories is fundamental and essential for the similarity-based remaining useful life (RUL) prediction. Most previous methods do not adequately account for the epistemic uncertainty caused by asynchronous sampling, while others have strong assumption constrain...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/23/9535 |
| _version_ | 1797399595722997760 |
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| author | Wenbo Wu Tianji Zou Lu Zhang Ke Wang Xuzhi Li |
| author_facet | Wenbo Wu Tianji Zou Lu Zhang Ke Wang Xuzhi Li |
| author_sort | Wenbo Wu |
| collection | DOAJ |
| description | Measuring the similarity between two trajectories is fundamental and essential for the similarity-based remaining useful life (RUL) prediction. Most previous methods do not adequately account for the epistemic uncertainty caused by asynchronous sampling, while others have strong assumption constraints, such as limiting the positional deviation of sampling points to a fixed threshold, which biases the results considerably. To address the issue, an uncertain ellipse model based on the uncertain theory is proposed to model the location of sampling points as an observation drawn from an uncertain distribution. Based on this, we propose a novel and effective similarity measure metric for any two degradation trajectories. Then, the Stacked Denoising Autoencoder (SDA) model is proposed for RUL prediction, in which the models can be first trained on the most similar degradation data and then fine-tuned by the target dataset. Experimental results show that the predictive performance of the new method is superior to prior methods based on edit distance on real sequence (EDR), longest common subsequence (LCSS), or dynamic time warping (DTW) and is more robust at different sampling rates. |
| first_indexed | 2024-03-09T01:42:25Z |
| format | Article |
| id | doaj.art-c2f2e7171d6f4bb99993bd5c0575ae31 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T01:42:25Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c2f2e7171d6f4bb99993bd5c0575ae312023-12-08T15:26:20ZengMDPI AGSensors1424-82202023-11-012323953510.3390/s23239535Similarity-Based Remaining Useful Lifetime Prediction Method Considering Epistemic UncertaintyWenbo Wu0Tianji Zou1Lu Zhang2Ke Wang3Xuzhi Li4University of Chinese Academy of Sciences, Beijing 101408, ChinaUniversity of Chinese Academy of Sciences, Beijing 101408, ChinaUniversity of Chinese Academy of Sciences, Beijing 101408, ChinaUniversity of Chinese Academy of Sciences, Beijing 101408, ChinaUniversity of Chinese Academy of Sciences, Beijing 101408, ChinaMeasuring the similarity between two trajectories is fundamental and essential for the similarity-based remaining useful life (RUL) prediction. Most previous methods do not adequately account for the epistemic uncertainty caused by asynchronous sampling, while others have strong assumption constraints, such as limiting the positional deviation of sampling points to a fixed threshold, which biases the results considerably. To address the issue, an uncertain ellipse model based on the uncertain theory is proposed to model the location of sampling points as an observation drawn from an uncertain distribution. Based on this, we propose a novel and effective similarity measure metric for any two degradation trajectories. Then, the Stacked Denoising Autoencoder (SDA) model is proposed for RUL prediction, in which the models can be first trained on the most similar degradation data and then fine-tuned by the target dataset. Experimental results show that the predictive performance of the new method is superior to prior methods based on edit distance on real sequence (EDR), longest common subsequence (LCSS), or dynamic time warping (DTW) and is more robust at different sampling rates.https://www.mdpi.com/1424-8220/23/23/9535remaining useful lifetimeepistemic uncertaintyuncertainty theorysimilarity measuredegradation trajectories |
| spellingShingle | Wenbo Wu Tianji Zou Lu Zhang Ke Wang Xuzhi Li Similarity-Based Remaining Useful Lifetime Prediction Method Considering Epistemic Uncertainty Sensors remaining useful lifetime epistemic uncertainty uncertainty theory similarity measure degradation trajectories |
| title | Similarity-Based Remaining Useful Lifetime Prediction Method Considering Epistemic Uncertainty |
| title_full | Similarity-Based Remaining Useful Lifetime Prediction Method Considering Epistemic Uncertainty |
| title_fullStr | Similarity-Based Remaining Useful Lifetime Prediction Method Considering Epistemic Uncertainty |
| title_full_unstemmed | Similarity-Based Remaining Useful Lifetime Prediction Method Considering Epistemic Uncertainty |
| title_short | Similarity-Based Remaining Useful Lifetime Prediction Method Considering Epistemic Uncertainty |
| title_sort | similarity based remaining useful lifetime prediction method considering epistemic uncertainty |
| topic | remaining useful lifetime epistemic uncertainty uncertainty theory similarity measure degradation trajectories |
| url | https://www.mdpi.com/1424-8220/23/23/9535 |
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