A deep-sea mining riser is a crucial component of the system used to lift seafloor mineral resources to the vessel.It is prone to damage and failure because of harsh environmental conditions and internal fluid erosion...A deep-sea mining riser is a crucial component of the system used to lift seafloor mineral resources to the vessel.It is prone to damage and failure because of harsh environmental conditions and internal fluid erosion.Furthermore,damage can impact the response characteristics of the riser,but varying environmental loadings easily mask it.Thus,distin-guishing between riser damage and environmental effects poses a considerable challenge.To address this issue,a cantilevered model is created for a deep-sea mining riser via the concentrated mass method,and a time-domain analytical strategy is developed.The vortex-induced vibration(VIV)response characteristics of the riser are initially examined,considering various damage conditions and flow velocities.The study results revealed four primary observations:(a)effective tension can serve as a reliable indicator for identifying damage at lower velocities;(b)there are noticeable differences in displacement between the healthy and damaged risers in the in-line direction rather than the cross-flow direction;(c)frequency characteristics can more effectively distinguish the damage conditions at high flow velocities,with the mean square frequency and frequency variance being more effective than the centroid frequency and root variance frequency;(d)displacement differences are more sensitive to damage occurring near the top and bottom of the riser,while both velocity variations and structural damage can influence displacements,especially in regions between modal nodes.The vibrational behavior and damage indicators are clarified for structural health monitoring of deep-sea mining risers during lifting operations.展开更多
A deep-sea riser is a crucial component of the mining system used to lift seafloor mineral resources to the vessel.Even minor damage to the riser can lead to substantial financial losses,environmental impacts,and safe...A deep-sea riser is a crucial component of the mining system used to lift seafloor mineral resources to the vessel.Even minor damage to the riser can lead to substantial financial losses,environmental impacts,and safety hazards.However,identifying modal parameters for structural health monitoring remains a major challenge due to its large deformations and flexibility.Vibration signal-based methods are essential for detecting damage and enabling timely maintenance to minimize losses.However,accurately extracting features from one-dimensional(1D)signals is often hindered by various environmental factors and measurement noises.To address this challenge,a novel approach based on a residual convolutional auto-encoder(RCAE)is proposed for detecting damage in deep-sea mining risers,incorporating a data fusion strategy.First,principal component analysis(PCA)is applied to reduce environmental fluctuations and fuse multisensor strain readings.Subsequently,a 1D-RCAE is used to extract damage-sensitive features(DSFs)from the fused dataset.A Mahalanobis distance indicator is established to compare the DSFs of the testing and healthy risers.The specific threshold for these distances is determined using the 3σcriterion,which is employed to assess whether damage has occurred in the testing riser.The effectiveness and robustness of the proposed approach are verified through numerical simulations of a 500-m riser and experimental tests on a 6-m riser.Moreover,the impact of contaminated noise and environmental fluctuations is examined.Results show that the proposed PCA-1D-RCAE approach can effectively detect damage and is resilient to measurement noise and environmental fluctuations.The accuracy exceeds 98%under noise-free conditions and remains above 90%even with 10 dB noise.This novel approach has the potential to establish a new standard for evaluating the health and integrity of risers during mining operations,thereby reducing the high costs and risks associated with failures.Maintenance activities can be scheduled more efficiently by enabling early and accurate detection of riser damage,minimizing downtime and avoiding catastrophic failures.展开更多
基金financially supported by the National Key Research and Development Program of China(Grant No.2023YFC2811600)the National Natural Science Foundation of China(Grant Nos.52301349 and 52088102)+1 种基金the Qingdao Post-Doctorate Science Fund(No.QDBSH20220202070)the Major Scientific and Technological Innovation Project of Shandong Province(Grant No.2019JZZY010820).
文摘A deep-sea mining riser is a crucial component of the system used to lift seafloor mineral resources to the vessel.It is prone to damage and failure because of harsh environmental conditions and internal fluid erosion.Furthermore,damage can impact the response characteristics of the riser,but varying environmental loadings easily mask it.Thus,distin-guishing between riser damage and environmental effects poses a considerable challenge.To address this issue,a cantilevered model is created for a deep-sea mining riser via the concentrated mass method,and a time-domain analytical strategy is developed.The vortex-induced vibration(VIV)response characteristics of the riser are initially examined,considering various damage conditions and flow velocities.The study results revealed four primary observations:(a)effective tension can serve as a reliable indicator for identifying damage at lower velocities;(b)there are noticeable differences in displacement between the healthy and damaged risers in the in-line direction rather than the cross-flow direction;(c)frequency characteristics can more effectively distinguish the damage conditions at high flow velocities,with the mean square frequency and frequency variance being more effective than the centroid frequency and root variance frequency;(d)displacement differences are more sensitive to damage occurring near the top and bottom of the riser,while both velocity variations and structural damage can influence displacements,especially in regions between modal nodes.The vibrational behavior and damage indicators are clarified for structural health monitoring of deep-sea mining risers during lifting operations.
基金the National Key Research and Development Program of China(No.2023 YFC2811600)the National Natural Science Foundation of China(Nos.52301349,52088102)+1 种基金the Major Science and Technology Innovation Program of Qingdao(No.223-3-hygg-10-hy)the Qingdao Science Foundation for Post-doctoral Scientists(Nos.QDBSH20220202070,QDBSH20220201015)。
文摘A deep-sea riser is a crucial component of the mining system used to lift seafloor mineral resources to the vessel.Even minor damage to the riser can lead to substantial financial losses,environmental impacts,and safety hazards.However,identifying modal parameters for structural health monitoring remains a major challenge due to its large deformations and flexibility.Vibration signal-based methods are essential for detecting damage and enabling timely maintenance to minimize losses.However,accurately extracting features from one-dimensional(1D)signals is often hindered by various environmental factors and measurement noises.To address this challenge,a novel approach based on a residual convolutional auto-encoder(RCAE)is proposed for detecting damage in deep-sea mining risers,incorporating a data fusion strategy.First,principal component analysis(PCA)is applied to reduce environmental fluctuations and fuse multisensor strain readings.Subsequently,a 1D-RCAE is used to extract damage-sensitive features(DSFs)from the fused dataset.A Mahalanobis distance indicator is established to compare the DSFs of the testing and healthy risers.The specific threshold for these distances is determined using the 3σcriterion,which is employed to assess whether damage has occurred in the testing riser.The effectiveness and robustness of the proposed approach are verified through numerical simulations of a 500-m riser and experimental tests on a 6-m riser.Moreover,the impact of contaminated noise and environmental fluctuations is examined.Results show that the proposed PCA-1D-RCAE approach can effectively detect damage and is resilient to measurement noise and environmental fluctuations.The accuracy exceeds 98%under noise-free conditions and remains above 90%even with 10 dB noise.This novel approach has the potential to establish a new standard for evaluating the health and integrity of risers during mining operations,thereby reducing the high costs and risks associated with failures.Maintenance activities can be scheduled more efficiently by enabling early and accurate detection of riser damage,minimizing downtime and avoiding catastrophic failures.
