Health status assessment and prediction for pumped storage units using a novel health degradation index. Zhang, X., Jiang, Y., Li, C., & Zhang, J. Mechanical Systems and Signal Processing, 171:108910, May, 2022. ![Health status assessment and prediction for pumped storage units using a novel health degradation index [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex To improve the safety and stability of pumped storage units (PSUs), we propose a novel health degradation index (HDI) to achieve real-time health status assessment for PSUs. Based on the HDI, the health degradation trend is predicted by the combination of variational mode decomposition (VMD) and gated recurrent unit (GRU). Firstly, the complex fitting relationship between the operating parameters of the PSU and its shaft vibration is established based on health monitoring data by multi-head self-attentive neural network (MSNN), which is regarded as a health benchmark model (HBM). MSNN reveals the high-dimensional mutually coupled relationship between different factors influencing the vibration. Secondly, to well describe the uncertainty information inherent in the vibration, Gaussian cloud model (GCM) is used to describe the vibration from both quantitative and qualitative perspectives. Then, the HDI is defined by the Kullback-Leibler divergence between the observed GCM and the health GCM predicted by HBM. Finally, VMD is used to decompose the complex HDI series into some simplistic components, while GRU is used to predict separately on the components. The final results are obtained by combining the component prediction results. The proposed method is applied in a PSU in China. The experimental results as well as several comparative studies demonstrate its outstanding performance.
@article{zhang_health_2022,
title = {Health status assessment and prediction for pumped storage units using a novel health degradation index},
volume = {171},
issn = {0888-3270},
url = {https://www.sciencedirect.com/science/article/pii/S0888327022000978},
doi = {10.1016/j.ymssp.2022.108910},
abstract = {To improve the safety and stability of pumped storage units (PSUs), we propose a novel health degradation index (HDI) to achieve real-time health status assessment for PSUs. Based on the HDI, the health degradation trend is predicted by the combination of variational mode decomposition (VMD) and gated recurrent unit (GRU). Firstly, the complex fitting relationship between the operating parameters of the PSU and its shaft vibration is established based on health monitoring data by multi-head self-attentive neural network (MSNN), which is regarded as a health benchmark model (HBM). MSNN reveals the high-dimensional mutually coupled relationship between different factors influencing the vibration. Secondly, to well describe the uncertainty information inherent in the vibration, Gaussian cloud model (GCM) is used to describe the vibration from both quantitative and qualitative perspectives. Then, the HDI is defined by the Kullback-Leibler divergence between the observed GCM and the health GCM predicted by HBM. Finally, VMD is used to decompose the complex HDI series into some simplistic components, while GRU is used to predict separately on the components. The final results are obtained by combining the component prediction results. The proposed method is applied in a PSU in China. The experimental results as well as several comparative studies demonstrate its outstanding performance.},
language = {en},
urldate = {2022-02-15},
journal = {Mechanical Systems and Signal Processing},
author = {Zhang, Xiaoyuan and Jiang, Yajun and Li, Chaoshun and Zhang, Jinhao},
month = may,
year = {2022},
keywords = {Gated recurrent units, Gaussian cloud model, Health status assessment and prediction, Multi-head self-attentive mechanism, Pumped storage units, Variational mode decomposition},
pages = {108910},
}
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Firstly, the complex fitting relationship between the operating parameters of the PSU and its shaft vibration is established based on health monitoring data by multi-head self-attentive neural network (MSNN), which is regarded as a health benchmark model (HBM). MSNN reveals the high-dimensional mutually coupled relationship between different factors influencing the vibration. Secondly, to well describe the uncertainty information inherent in the vibration, Gaussian cloud model (GCM) is used to describe the vibration from both quantitative and qualitative perspectives. Then, the HDI is defined by the Kullback-Leibler divergence between the observed GCM and the health GCM predicted by HBM. Finally, VMD is used to decompose the complex HDI series into some simplistic components, while GRU is used to predict separately on the components. The final results are obtained by combining the component prediction results. The proposed method is applied in a PSU in China. 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