基于AMSAA模型以研发阶段收集的同类或相似产品故障数据为核心,创新性地引入离散系数最小化原则作为优化标准,用于指导拟合优度检验统计量的选取。在整合平均值与方差等多元信息后,构建了一种全新的时间环境折合系数求解算法,旨在对原...基于AMSAA模型以研发阶段收集的同类或相似产品故障数据为核心,创新性地引入离散系数最小化原则作为优化标准,用于指导拟合优度检验统计量的选取。在整合平均值与方差等多元信息后,构建了一种全新的时间环境折合系数求解算法,旨在对原始故障数据进行有效折算,进而精准估算模型各项参数。对所得折合系数进行求解,并在10种不同置信度设定下,逐一计算产品设计成型时平均无故障工作时间(Mean Time Between Failure,MTBF)的单侧置信下限。实例研究表明,当置信区间位于0.9~0.99之间时,无论同一置信水平如何,该方法始终能得出优于现有文献的结果,即采用该方法对航天产品可靠性增长进行预测,其准确度显著提升。此外,在不同置信水平下,由改进方法求解的时间环境折合系数值并没有改变,即每个试验项目的环境应力和真实环境应力之间的数量关系并没有因为置信度的增加而变化,也从另一个角度证明了该改进方法更接近于工程实践。展开更多
The coupling model of major influence factors such state affecting the chloride diffusion process in concrete is as environmental relative humidity, load-induced crack and stress discussed. The probability distributio...The coupling model of major influence factors such state affecting the chloride diffusion process in concrete is as environmental relative humidity, load-induced crack and stress discussed. The probability distributions of the critical chloride concentration Cc, the chloride diffusion coefficient D, and the surface chloride concentration Cs were determined based on the collected natural exposure data. And the estimation of probability of corrosion initiation considering the coupling effects of influence factors is presented. It is found that the relative humidity and curing time are the most effective factors affecting the probability of corrosion initiation before and after 10 years of exposure time. At the same exposure time, the influence of load-induced crack and stress state on the probability of corrosion initiation is obvious, in which the effect of crack is the most one展开更多
Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control.In solar greenhouses,temperature,humidity,and light intensity are crucial environmental parame...Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control.In solar greenhouses,temperature,humidity,and light intensity are crucial environmental parameters.The monitoring platform collected data on the internal environment of the solar greenhouse for one year,including temperature,humidity,and light intensity.Additionally,meteorological data,comprising outdoor temperature,outdoor humidity,and outdoor light intensity,was gathered during the same time frame.The characteristics and interrelationships among these parameters were investigated by a thorough analysis.The analysis revealed that environmental parameters in solar greenhouses displayed characteristics such as temporal variability,non-linearity,and periodicity.These parameters exhibited complex coupling relationships.Notably,these characteristics and coupling relationships exhibited pronounced seasonal variations.The multi-parameter multi-step prediction model for solar greenhouse(MPMS-SGH)was introduced,aiming to accurately predict three key greenhouse environmental parameters,and the model had certain seasonal adaptability.MPMS-SGH was structured with multiple layers,including an input layer,a preprocessing layer,a feature extraction layer,and a prediction layer.The input layer was used to generate the original sequence matrix,which included indoor temperature,indoor humidity,indoor light intensity,as well as outdoor temperature and outdoor light intensity.Then the preprocessing layer normalized,decomposed,and positionally encoded the original sequence matrix.In the feature extraction layer,the time attention mechanism and frequency attention mechanism were used to extract features from the trend component and the seasonal component,respectively.Finally,the prediction layer used a multi-layer perceptron to perform multi-step prediction of indoor environmental parameters(i.e.temperature,humidity,and light intensity).The parameter selection experiment evaluated the predictive performance of MPMS-SGH on input and output sequences of different lengths.The results indicated that with a constant output sequence length,the prediction accuracy of MPMS-SGH was firstly increased and then decreased with the increase of input sequence length.Specifically,when the input sequence length was 100,MPMS-SGH had the highest prediction accuracy,with RMSE of 0.22℃,0.28%,and 250lx for temperature,humidity,and light intensity,respectively.When the length of the input sequence remained constant,as the length of the output sequence increased,the accuracy of the model in predicting the three environmental parameters was continuously decreased.When the length of the output sequence exceeded 45,the prediction accuracy of MPMS-SGH was significantly decreased.In order to achieve the best balance between model size and performance,the input sequence length of MPMS-SGH was set to be 100,while the output sequence length was set to be 35.To assess MPMS-SGH’s performance,comparative experiments with four prediction models were conducted:SVR,STL-SVR,LSTM,and STL-LSTM.The results demonstrated that MPMS-SGH surpassed all other models,achieving RMSE of 0.15℃for temperature,0.38%for humidity,and 260lx for light intensity.Additionally,sequence decomposition can contribute to enhancing MPMS-SGH’s prediction performance.To further evaluate MPMS-SGH’s capabilities,its prediction accuracy was tested across different seasons for greenhouse environmental parameters.MPMS-SGH had the highest accuracy in predicting indoor temperature and the lowest accuracy in predicting humidity.And the accuracy of MPMS-SGH in predicting environmental parameters of the solar greenhouse fluctuated with seasons.MPMS-SGH had the highest accuracy in predicting the temperature inside the greenhouse on sunny days in spring(R^(2)=0.91),the highest accuracy in predicting the humidity inside the greenhouse on sunny days in winter(R^(2)=0.83),and the highest accuracy in predicting the light intensity inside the greenhouse on cloudy days in autumm(R^(2)=0.89).MPMS-SGH had the lowest accuracy in predicting three environmental parameters in a sunny summer greenhouse.展开更多
文摘基于AMSAA模型以研发阶段收集的同类或相似产品故障数据为核心,创新性地引入离散系数最小化原则作为优化标准,用于指导拟合优度检验统计量的选取。在整合平均值与方差等多元信息后,构建了一种全新的时间环境折合系数求解算法,旨在对原始故障数据进行有效折算,进而精准估算模型各项参数。对所得折合系数进行求解,并在10种不同置信度设定下,逐一计算产品设计成型时平均无故障工作时间(Mean Time Between Failure,MTBF)的单侧置信下限。实例研究表明,当置信区间位于0.9~0.99之间时,无论同一置信水平如何,该方法始终能得出优于现有文献的结果,即采用该方法对航天产品可靠性增长进行预测,其准确度显著提升。此外,在不同置信水平下,由改进方法求解的时间环境折合系数值并没有改变,即每个试验项目的环境应力和真实环境应力之间的数量关系并没有因为置信度的增加而变化,也从另一个角度证明了该改进方法更接近于工程实践。
基金Project(50925829) supported by the National Science Fund for Distinguished Young Scholars of ChinaProject(50908148) supported by the National Natural Science Foundation of ChinaProjects(2009-K4-23,2010-11-33) supported by the Research of Ministry of Housing and Urban Rural Development of China
文摘The coupling model of major influence factors such state affecting the chloride diffusion process in concrete is as environmental relative humidity, load-induced crack and stress discussed. The probability distributions of the critical chloride concentration Cc, the chloride diffusion coefficient D, and the surface chloride concentration Cs were determined based on the collected natural exposure data. And the estimation of probability of corrosion initiation considering the coupling effects of influence factors is presented. It is found that the relative humidity and curing time are the most effective factors affecting the probability of corrosion initiation before and after 10 years of exposure time. At the same exposure time, the influence of load-induced crack and stress state on the probability of corrosion initiation is obvious, in which the effect of crack is the most one
文摘Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control.In solar greenhouses,temperature,humidity,and light intensity are crucial environmental parameters.The monitoring platform collected data on the internal environment of the solar greenhouse for one year,including temperature,humidity,and light intensity.Additionally,meteorological data,comprising outdoor temperature,outdoor humidity,and outdoor light intensity,was gathered during the same time frame.The characteristics and interrelationships among these parameters were investigated by a thorough analysis.The analysis revealed that environmental parameters in solar greenhouses displayed characteristics such as temporal variability,non-linearity,and periodicity.These parameters exhibited complex coupling relationships.Notably,these characteristics and coupling relationships exhibited pronounced seasonal variations.The multi-parameter multi-step prediction model for solar greenhouse(MPMS-SGH)was introduced,aiming to accurately predict three key greenhouse environmental parameters,and the model had certain seasonal adaptability.MPMS-SGH was structured with multiple layers,including an input layer,a preprocessing layer,a feature extraction layer,and a prediction layer.The input layer was used to generate the original sequence matrix,which included indoor temperature,indoor humidity,indoor light intensity,as well as outdoor temperature and outdoor light intensity.Then the preprocessing layer normalized,decomposed,and positionally encoded the original sequence matrix.In the feature extraction layer,the time attention mechanism and frequency attention mechanism were used to extract features from the trend component and the seasonal component,respectively.Finally,the prediction layer used a multi-layer perceptron to perform multi-step prediction of indoor environmental parameters(i.e.temperature,humidity,and light intensity).The parameter selection experiment evaluated the predictive performance of MPMS-SGH on input and output sequences of different lengths.The results indicated that with a constant output sequence length,the prediction accuracy of MPMS-SGH was firstly increased and then decreased with the increase of input sequence length.Specifically,when the input sequence length was 100,MPMS-SGH had the highest prediction accuracy,with RMSE of 0.22℃,0.28%,and 250lx for temperature,humidity,and light intensity,respectively.When the length of the input sequence remained constant,as the length of the output sequence increased,the accuracy of the model in predicting the three environmental parameters was continuously decreased.When the length of the output sequence exceeded 45,the prediction accuracy of MPMS-SGH was significantly decreased.In order to achieve the best balance between model size and performance,the input sequence length of MPMS-SGH was set to be 100,while the output sequence length was set to be 35.To assess MPMS-SGH’s performance,comparative experiments with four prediction models were conducted:SVR,STL-SVR,LSTM,and STL-LSTM.The results demonstrated that MPMS-SGH surpassed all other models,achieving RMSE of 0.15℃for temperature,0.38%for humidity,and 260lx for light intensity.Additionally,sequence decomposition can contribute to enhancing MPMS-SGH’s prediction performance.To further evaluate MPMS-SGH’s capabilities,its prediction accuracy was tested across different seasons for greenhouse environmental parameters.MPMS-SGH had the highest accuracy in predicting indoor temperature and the lowest accuracy in predicting humidity.And the accuracy of MPMS-SGH in predicting environmental parameters of the solar greenhouse fluctuated with seasons.MPMS-SGH had the highest accuracy in predicting the temperature inside the greenhouse on sunny days in spring(R^(2)=0.91),the highest accuracy in predicting the humidity inside the greenhouse on sunny days in winter(R^(2)=0.83),and the highest accuracy in predicting the light intensity inside the greenhouse on cloudy days in autumm(R^(2)=0.89).MPMS-SGH had the lowest accuracy in predicting three environmental parameters in a sunny summer greenhouse.
