Belt conveyors are extensively utilized in mining and power industries.In a typical coal mine conveyor system,coal is transported over distances exceeding 2 km,involving more than 20000 idlers,which far exceeds a reas...Belt conveyors are extensively utilized in mining and power industries.In a typical coal mine conveyor system,coal is transported over distances exceeding 2 km,involving more than 20000 idlers,which far exceeds a reasonable manual inspection capacity.Given that idlers typically have a lifespan of 1-2 years,there is an urgent need for a rapid,cost-effective,and intelligent safety monitoring system.However,current embedded systems face prohibitive replacement costs,while conventional monitoring technologies suffer from inefficiency at low rotational speeds and lack systematic structural optimization frameworks for diverse idler types and parameters.To address these challenges,this paper introduces an integrated,on-site detachable self-powered idler condition monitoring system(ICMS).This system combines energy harvesting based on the magnetic modulation technology with wireless condition monitoring capabilities.Specifically,it develops a data-driven model integrating convolutional neural networks(CNNs) with genetic algorithms(GAs).The conventional testing results show that the data-driven model not only significantly accelerates the parameter response time,but also achieves a prediction accuracy of 92.95%.The in-situ experiments conducted in coal mines demonstrate the system's reliability and monitoring functionality under both no-load and fullload conditions.This research provides an innovative self-powered condition monitoring solution and develops an efficient data-driven model,offering feasible online monitoring approaches for smart mine construction.展开更多
This paper deals with the co-existence of mixed aleatory and epistemic uncertainties in a wind turbine geared system for more reliable and robust vibration analyses.To this end,the regression-based polynomial chaos ex...This paper deals with the co-existence of mixed aleatory and epistemic uncertainties in a wind turbine geared system for more reliable and robust vibration analyses.To this end,the regression-based polynomial chaos expansion(PCE)is used to track aleatory uncertainties,and the polynomial surrogate approach(PSA)is developed to treat the epistemic uncertainties.This non-intrusive dual-layer framework shares the same collocation pool,which is extracted from the Legendre series.Moreover,the regression technique has been implemented in both layers to enhance calculation efficiency.Numerical validation is carried out to show the effectiveness of the proposed method.New vibration behaviors of the geared transmission system are observed,and the mechanism behind is discussed in detail.The findings of this paper will contribute to the insightful understanding of such wind turbine geared systems under hybrid uncertainties and are beneficial for the condition monitoring.展开更多
基金supported by the National Natural Science Foundation of China(Nos.12172248,12302022,12021002,and 12132010)the Tianjin Research Program of Application Foundation and Advanced Technology of China(No.23JCZDJC00950)。
文摘Belt conveyors are extensively utilized in mining and power industries.In a typical coal mine conveyor system,coal is transported over distances exceeding 2 km,involving more than 20000 idlers,which far exceeds a reasonable manual inspection capacity.Given that idlers typically have a lifespan of 1-2 years,there is an urgent need for a rapid,cost-effective,and intelligent safety monitoring system.However,current embedded systems face prohibitive replacement costs,while conventional monitoring technologies suffer from inefficiency at low rotational speeds and lack systematic structural optimization frameworks for diverse idler types and parameters.To address these challenges,this paper introduces an integrated,on-site detachable self-powered idler condition monitoring system(ICMS).This system combines energy harvesting based on the magnetic modulation technology with wireless condition monitoring capabilities.Specifically,it develops a data-driven model integrating convolutional neural networks(CNNs) with genetic algorithms(GAs).The conventional testing results show that the data-driven model not only significantly accelerates the parameter response time,but also achieves a prediction accuracy of 92.95%.The in-situ experiments conducted in coal mines demonstrate the system's reliability and monitoring functionality under both no-load and fullload conditions.This research provides an innovative self-powered condition monitoring solution and develops an efficient data-driven model,offering feasible online monitoring approaches for smart mine construction.
基金Project supported by the National Natural Science Foundation of China(Nos.12072263 and 11972295)the Fundamental Research Funds for the Central Universities(No.G2021KY0601)。
文摘This paper deals with the co-existence of mixed aleatory and epistemic uncertainties in a wind turbine geared system for more reliable and robust vibration analyses.To this end,the regression-based polynomial chaos expansion(PCE)is used to track aleatory uncertainties,and the polynomial surrogate approach(PSA)is developed to treat the epistemic uncertainties.This non-intrusive dual-layer framework shares the same collocation pool,which is extracted from the Legendre series.Moreover,the regression technique has been implemented in both layers to enhance calculation efficiency.Numerical validation is carried out to show the effectiveness of the proposed method.New vibration behaviors of the geared transmission system are observed,and the mechanism behind is discussed in detail.The findings of this paper will contribute to the insightful understanding of such wind turbine geared systems under hybrid uncertainties and are beneficial for the condition monitoring.
