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A New Strategy for Bearing Health Assessment with a Dynamic Interval Prediction Model
Bearing is the critical basic component of rotating machinery and its remaining life prediction is very important for mechanical equipment’s smooth and healthy operation. However, fast and accurate bearing life prediction has always been a difficult point in industry and academia. This paper propose...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10534407/ https://www.ncbi.nlm.nih.gov/pubmed/37765753 http://dx.doi.org/10.3390/s23187696 |
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author | Jiang, Lingli Sheng, Heshan Yang, Tongguang Tang, Hujiao Li, Xuejun Gao, Lianbin |
author_facet | Jiang, Lingli Sheng, Heshan Yang, Tongguang Tang, Hujiao Li, Xuejun Gao, Lianbin |
author_sort | Jiang, Lingli |
collection | PubMed |
description | Bearing is the critical basic component of rotating machinery and its remaining life prediction is very important for mechanical equipment’s smooth and healthy operation. However, fast and accurate bearing life prediction has always been a difficult point in industry and academia. This paper proposes a new strategy for bearing health assessment based on a model-driven dynamic interval prediction model. Firstly, the mapping proportion algorithm is used to determine whether the measured data are in the degradation stage. After finding the starting point of prediction, the improved annealing algorithm is used to determine the shortest data interval that can be used for accurate prediction. Then, based on the bearing degradation curve and the information fusion inverse health index, the health index is obtained from 36 general indexes in the time domain and frequency domain through screening, fusion, and inversion. Finally, the state space equation is constructed based on the Paris-DSSM formula and the particle filter is used to iterate the state space equation parameters with the minimum interval data to construct the life prediction model. The proposed method is verified by XJTU-SY rolling bearing life data. The results show that the prediction accuracy of the proposed strategy for the remaining life of the bearing can reach more than 90%. It is verified that the improved simulated annealing algorithm selects limited interval data, reconstructs health indicators based on bearing degradation curve and information fusion, and updates the Paris-DSSM state space equation through the particle filter algorithm. The bearing life prediction model constructed on this basis is accurate and effective. |
format | Online Article Text |
id | pubmed-10534407 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-105344072023-09-29 A New Strategy for Bearing Health Assessment with a Dynamic Interval Prediction Model Jiang, Lingli Sheng, Heshan Yang, Tongguang Tang, Hujiao Li, Xuejun Gao, Lianbin Sensors (Basel) Article Bearing is the critical basic component of rotating machinery and its remaining life prediction is very important for mechanical equipment’s smooth and healthy operation. However, fast and accurate bearing life prediction has always been a difficult point in industry and academia. This paper proposes a new strategy for bearing health assessment based on a model-driven dynamic interval prediction model. Firstly, the mapping proportion algorithm is used to determine whether the measured data are in the degradation stage. After finding the starting point of prediction, the improved annealing algorithm is used to determine the shortest data interval that can be used for accurate prediction. Then, based on the bearing degradation curve and the information fusion inverse health index, the health index is obtained from 36 general indexes in the time domain and frequency domain through screening, fusion, and inversion. Finally, the state space equation is constructed based on the Paris-DSSM formula and the particle filter is used to iterate the state space equation parameters with the minimum interval data to construct the life prediction model. The proposed method is verified by XJTU-SY rolling bearing life data. The results show that the prediction accuracy of the proposed strategy for the remaining life of the bearing can reach more than 90%. It is verified that the improved simulated annealing algorithm selects limited interval data, reconstructs health indicators based on bearing degradation curve and information fusion, and updates the Paris-DSSM state space equation through the particle filter algorithm. The bearing life prediction model constructed on this basis is accurate and effective. MDPI 2023-09-06 /pmc/articles/PMC10534407/ /pubmed/37765753 http://dx.doi.org/10.3390/s23187696 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Jiang, Lingli Sheng, Heshan Yang, Tongguang Tang, Hujiao Li, Xuejun Gao, Lianbin A New Strategy for Bearing Health Assessment with a Dynamic Interval Prediction Model |
title | A New Strategy for Bearing Health Assessment with a Dynamic Interval Prediction Model |
title_full | A New Strategy for Bearing Health Assessment with a Dynamic Interval Prediction Model |
title_fullStr | A New Strategy for Bearing Health Assessment with a Dynamic Interval Prediction Model |
title_full_unstemmed | A New Strategy for Bearing Health Assessment with a Dynamic Interval Prediction Model |
title_short | A New Strategy for Bearing Health Assessment with a Dynamic Interval Prediction Model |
title_sort | new strategy for bearing health assessment with a dynamic interval prediction model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10534407/ https://www.ncbi.nlm.nih.gov/pubmed/37765753 http://dx.doi.org/10.3390/s23187696 |
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