As a dynamic projection to latent structures(PLS)method with a good output prediction ability,dynamic inner PLS(DiPLS)is widely used in the prediction of key performance indi-cators.However,due to the oblique decompos...As a dynamic projection to latent structures(PLS)method with a good output prediction ability,dynamic inner PLS(DiPLS)is widely used in the prediction of key performance indi-cators.However,due to the oblique decomposition of the input space by DiPLS,there are false alarms in the actual industrial process during fault detection.To address the above problems,a dynamic modeling method based on autoregressive-dynamic inner total PLS(AR-DiTPLS)is proposed.The method first uses the regression relation matrix to decompose the input space orthogonally,which reduces useless information for the predic-tion output in the quality-related dynamic subspace.Then,a vector autoregressive model(VAR)is constructed for the predic-tion score to separate dynamic information and static informa-tion.Based on the VAR model,appropriate statistical indicators are further constructed for online monitoring,which reduces the occurrence of false alarms.The effectiveness of the method is verified by a Tennessee-Eastman industrial simulation process and a three-phase flow system.展开更多
Metal mineral resources play an indispensable role in the development of the national economy.Dynamic disasters in underground metal mines seriously threaten mining safety,which are major scientific and technological ...Metal mineral resources play an indispensable role in the development of the national economy.Dynamic disasters in underground metal mines seriously threaten mining safety,which are major scientific and technological problems to be solved urgently.In this article,the occurrence status and grand challenges of some typical dynamic disasters involving roof falling,spalling,collapse,large deformation,rockburst,surface subsidence,and water inrush in metal mines in China are systematically presented,the characteristics of mining-induced dynamic disasters are analyzed,the examples of dynamic disasters occurring in some metal mines in China are summarized,the occurrence mechanism,monitoring and early warning methods,and prevention and control techniques of these disasters are highlighted,and some new opinions,suggestions,and solutions are proposed simultaneously.Moreover,some shortcomings in current disaster research are pointed out,and the direction of efforts to improve the prevention and control level of dynamic disasters in China’s metal mines in the future is prospected.The integration of forward-looking key innovative theories and technologies in the abovementioned aspects will greatly enhance the cognitive level of disaster prevention and mitigation in China’s metal mining industry and achieve a significant shift from passive disaster relief to active disaster prevention.展开更多
Dynamic monitoring is an important basic work in the sustainable exploitation and utilization of geothermal resources.It offers basic data for geothermal management department to make out the plan of geothermal resour...Dynamic monitoring is an important basic work in the sustainable exploitation and utilization of geothermal resources.It offers basic data for geothermal management department to make out the plan of geothermal resources by collecting and analyzing the outdoor dynamic monitoring data. Tianjin is one of the earliest cities in exploiting and utilizing geothermal resources,and the展开更多
Social infrastructures such as dams are likely to be exposed to high risk of terrorist and military attacks,leading to increasing attentions on their vulnerability and catastrophic consequences under such events.This ...Social infrastructures such as dams are likely to be exposed to high risk of terrorist and military attacks,leading to increasing attentions on their vulnerability and catastrophic consequences under such events.This paper tries to develop advanced deep learning approaches for structural dynamic response prediction and dam health diagnosis.At first,the improved long short-term memory(LSTM)networks are proposed for data-driven structural dynamic response analysis with the data generated by a single degree of freedom(SDOF)and the finite numerical simulation,due to the unavailability of abundant practical structural response data of concrete gravity dam under blast events.Three kinds of LSTM-based models are discussed with the various cases of noise-contaminated signals,and the results prove that LSTM-based models have the potential for quick structural response estimation under blast loads.Furthermore,the damage indicators(i.e.,peak vibration velocity and domain frequency)are extracted from the predicted velocity histories,and their relationship with the dam damage status from the numerical simulation is established.This study provides a deep-learning based structural health monitoring(SHM)framework for quick assessment of dam experienced underwater explosions through blastinduced monitoring data.展开更多
Structural health monitoring is widely utilized in outdoor environments,especially under harsh conditions,which can introduce noise into the monitoring system.Therefore,designing an effective denoising strategy to enh...Structural health monitoring is widely utilized in outdoor environments,especially under harsh conditions,which can introduce noise into the monitoring system.Therefore,designing an effective denoising strategy to enhance the performance of guided wave damage detection in noisy environments is crucial.This paper introduces a local temporal principal component analysis(PCA)reconstruction approach for denoising guided waves prior to implementing unsupervised damage detection,achieved through novel autoencoder-based reconstruction.Experimental results demonstrate that the proposed denoising method significantly enhances damage detection performance when guided waves are contaminated by noise,with SNR values ranging from 10 to-5 dB.Following the implementation of the proposed denoising approach,the AUC score can elevate from 0.65 to 0.96 when dealing with guided waves corrputed by noise at a level of-5 dB.Additionally,the paper provides guidance on selecting the appropriate number of components used in the denoising PCA reconstruction,aiding in the optimization of the damage detection in noisy conditions.展开更多
基金supported by the National Natural Science Foundation of China(62273354,61673387,61833016).
文摘As a dynamic projection to latent structures(PLS)method with a good output prediction ability,dynamic inner PLS(DiPLS)is widely used in the prediction of key performance indi-cators.However,due to the oblique decomposition of the input space by DiPLS,there are false alarms in the actual industrial process during fault detection.To address the above problems,a dynamic modeling method based on autoregressive-dynamic inner total PLS(AR-DiTPLS)is proposed.The method first uses the regression relation matrix to decompose the input space orthogonally,which reduces useless information for the predic-tion output in the quality-related dynamic subspace.Then,a vector autoregressive model(VAR)is constructed for the predic-tion score to separate dynamic information and static informa-tion.Based on the VAR model,appropriate statistical indicators are further constructed for online monitoring,which reduces the occurrence of false alarms.The effectiveness of the method is verified by a Tennessee-Eastman industrial simulation process and a three-phase flow system.
基金Project(52204084)supported by the National Natural Science Foundation of ChinaProject(FRF-IDRY-GD22-002)supported by the Interdisciplinary Research Project for Young Teachers of USTB(Fundamental Research Funds for the Central Universities),China+2 种基金Project(QNXM20220009)supported by the Fundamental Research Funds for the Central Universities and the Youth Teacher International Exchange and Growth Program,ChinaProjects(2022YFC2905600,2022YFC3004601)supported by the National Key R&D Program of ChinaProject(2023XAGG0061)supported by the Science,Technology&Innovation Project of Xiongan New Area,China。
文摘Metal mineral resources play an indispensable role in the development of the national economy.Dynamic disasters in underground metal mines seriously threaten mining safety,which are major scientific and technological problems to be solved urgently.In this article,the occurrence status and grand challenges of some typical dynamic disasters involving roof falling,spalling,collapse,large deformation,rockburst,surface subsidence,and water inrush in metal mines in China are systematically presented,the characteristics of mining-induced dynamic disasters are analyzed,the examples of dynamic disasters occurring in some metal mines in China are summarized,the occurrence mechanism,monitoring and early warning methods,and prevention and control techniques of these disasters are highlighted,and some new opinions,suggestions,and solutions are proposed simultaneously.Moreover,some shortcomings in current disaster research are pointed out,and the direction of efforts to improve the prevention and control level of dynamic disasters in China’s metal mines in the future is prospected.The integration of forward-looking key innovative theories and technologies in the abovementioned aspects will greatly enhance the cognitive level of disaster prevention and mitigation in China’s metal mining industry and achieve a significant shift from passive disaster relief to active disaster prevention.
文摘Dynamic monitoring is an important basic work in the sustainable exploitation and utilization of geothermal resources.It offers basic data for geothermal management department to make out the plan of geothermal resources by collecting and analyzing the outdoor dynamic monitoring data. Tianjin is one of the earliest cities in exploiting and utilizing geothermal resources,and the
基金supported by a grant from the National Natural Science Foundation of China(Grant No.52109163 and 51979188).
文摘Social infrastructures such as dams are likely to be exposed to high risk of terrorist and military attacks,leading to increasing attentions on their vulnerability and catastrophic consequences under such events.This paper tries to develop advanced deep learning approaches for structural dynamic response prediction and dam health diagnosis.At first,the improved long short-term memory(LSTM)networks are proposed for data-driven structural dynamic response analysis with the data generated by a single degree of freedom(SDOF)and the finite numerical simulation,due to the unavailability of abundant practical structural response data of concrete gravity dam under blast events.Three kinds of LSTM-based models are discussed with the various cases of noise-contaminated signals,and the results prove that LSTM-based models have the potential for quick structural response estimation under blast loads.Furthermore,the damage indicators(i.e.,peak vibration velocity and domain frequency)are extracted from the predicted velocity histories,and their relationship with the dam damage status from the numerical simulation is established.This study provides a deep-learning based structural health monitoring(SHM)framework for quick assessment of dam experienced underwater explosions through blastinduced monitoring data.
基金National Science Foundation of Zhejiang under Contract(LY23E010001)。
文摘Structural health monitoring is widely utilized in outdoor environments,especially under harsh conditions,which can introduce noise into the monitoring system.Therefore,designing an effective denoising strategy to enhance the performance of guided wave damage detection in noisy environments is crucial.This paper introduces a local temporal principal component analysis(PCA)reconstruction approach for denoising guided waves prior to implementing unsupervised damage detection,achieved through novel autoencoder-based reconstruction.Experimental results demonstrate that the proposed denoising method significantly enhances damage detection performance when guided waves are contaminated by noise,with SNR values ranging from 10 to-5 dB.Following the implementation of the proposed denoising approach,the AUC score can elevate from 0.65 to 0.96 when dealing with guided waves corrputed by noise at a level of-5 dB.Additionally,the paper provides guidance on selecting the appropriate number of components used in the denoising PCA reconstruction,aiding in the optimization of the damage detection in noisy conditions.
