Predictive analytics as a way to smart maintenance of hydraulic turbines. Georgievskaia, E. Procedia Structural Integrity, 28:836–842, January, 2020. ![Predictive analytics as a way to smart maintenance of hydraulic turbines [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Today, most energy companies face serious problems with the reliability and safety of large power equipment due to the long-term operation at off-design modes. This is especially true for hydraulic units that are traditionally used to ensure the required level of energy output and maintain the stability of a power grid due to their maneuverability. Off-design operational modes cause increased loads and stresses in the unit’s components, accelerate the growth of defects, stimulate premature failures, and can lead to a grave accident with large losses. Standard diagnostic systems for hydraulic units usually do not allow tracking hazardous defects such as fatigue cracks in the runner blades, guide vines, shaft, etc. The individuality of hydraulic units also excludes the use of any statistical methods to determine the time when the equipment will go to the limit state and its operation will become unreasonably dangerous. Therefore, the predictive analytics system is proposed as an additional approach to forecasting the appearance and growth of dangerous operational defects. The system realizes an analytical algorithm based on evaluating the fatigue strength of hydraulic unit elements under variable operating conditions and allows summing up damage from various external loads and in different time ranges. Input data for this predictive system is information about the actual operating time at every working mode and expected regime parameters for the upcoming period. The output data is information about actual and residual lifetime. All individual features are taken into account by the digital model which is a multidimensional matrix of equipment’s response to external influences. Data is generated in the cells for each unit’s elements with reference to the operating parameters and time scale. The proposed predictive analytics system allows not only to reduce the risk of accidents and unplanned shutdowns but it also is a way to smart maintenance of hydraulic turbines due to the development of the most effective, most reasonable, most lifetime-saving strategy of using the equipment.
@article{georgievskaia_predictive_2020,
series = {1st {Virtual} {European} {Conference} on {Fracture} - {VECF1}},
title = {Predictive analytics as a way to smart maintenance of hydraulic turbines},
volume = {28},
issn = {2452-3216},
url = {http://www.sciencedirect.com/science/article/pii/S2452321620306004},
doi = {10.1016/j.prostr.2020.10.098},
abstract = {Today, most energy companies face serious problems with the reliability and safety of large power equipment due to the long-term operation at off-design modes. This is especially true for hydraulic units that are traditionally used to ensure the required level of energy output and maintain the stability of a power grid due to their maneuverability. Off-design operational modes cause increased loads and stresses in the unit’s components, accelerate the growth of defects, stimulate premature failures, and can lead to a grave accident with large losses. Standard diagnostic systems for hydraulic units usually do not allow tracking hazardous defects such as fatigue cracks in the runner blades, guide vines, shaft, etc. The individuality of hydraulic units also excludes the use of any statistical methods to determine the time when the equipment will go to the limit state and its operation will become unreasonably dangerous. Therefore, the predictive analytics system is proposed as an additional approach to forecasting the appearance and growth of dangerous operational defects. The system realizes an analytical algorithm based on evaluating the fatigue strength of hydraulic unit elements under variable operating conditions and allows summing up damage from various external loads and in different time ranges. Input data for this predictive system is information about the actual operating time at every working mode and expected regime parameters for the upcoming period. The output data is information about actual and residual lifetime. All individual features are taken into account by the digital model which is a multidimensional matrix of equipment’s response to external influences. Data is generated in the cells for each unit’s elements with reference to the operating parameters and time scale. The proposed predictive analytics system allows not only to reduce the risk of accidents and unplanned shutdowns but it also is a way to smart maintenance of hydraulic turbines due to the development of the most effective, most reasonable, most lifetime-saving strategy of using the equipment.},
language = {en},
urldate = {2020-12-08},
journal = {Procedia Structural Integrity},
author = {Georgievskaia, Evgeniia},
month = jan,
year = {2020},
keywords = {crack, failure, hydraulic turbines, lifetime, predictive analytics, reliability, smart maintenance},
pages = {836--842},
}
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Off-design operational modes cause increased loads and stresses in the unit’s components, accelerate the growth of defects, stimulate premature failures, and can lead to a grave accident with large losses. Standard diagnostic systems for hydraulic units usually do not allow tracking hazardous defects such as fatigue cracks in the runner blades, guide vines, shaft, etc. The individuality of hydraulic units also excludes the use of any statistical methods to determine the time when the equipment will go to the limit state and its operation will become unreasonably dangerous. Therefore, the predictive analytics system is proposed as an additional approach to forecasting the appearance and growth of dangerous operational defects. The system realizes an analytical algorithm based on evaluating the fatigue strength of hydraulic unit elements under variable operating conditions and allows summing up damage from various external loads and in different time ranges. Input data for this predictive system is information about the actual operating time at every working mode and expected regime parameters for the upcoming period. The output data is information about actual and residual lifetime. All individual features are taken into account by the digital model which is a multidimensional matrix of equipment’s response to external influences. Data is generated in the cells for each unit’s elements with reference to the operating parameters and time scale. 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