Screening similar historical fault-free candidate data would greatly affect the effectiveness of fault detection results based on principal component analysis(PCA).In order to find out the candidate data,this study co...Screening similar historical fault-free candidate data would greatly affect the effectiveness of fault detection results based on principal component analysis(PCA).In order to find out the candidate data,this study compares unweighted and weighted similarity factors(SFs),which measure the similarity of the principal component subspace corresponding to the first k main components of two datasets.The fault detection employs the principal component subspace corresponding to the current measured data and the historical fault-free data.From the historical fault-free database,the load parameters are employed to locate the candidate data similar to the current operating data.Fault detection method for air conditioning systems is based on principal component.The results show that the weighted principal component SF can improve the effects of the fault-free detection and the fault detection.Compared with the unweighted SF,the average fault-free detection rate of the weighted SF is 17.33%higher than that of the unweighted,and the average fault detection rate is 7.51%higher than unweighted.展开更多
The Self-Similar Crack Expansion (SSCE) method is proposed to evaluate stress intensity factors at crack tips, whereby stress intensity factors of a crack can be determined by the crack opening displacement over the c...The Self-Similar Crack Expansion (SSCE) method is proposed to evaluate stress intensity factors at crack tips, whereby stress intensity factors of a crack can be determined by the crack opening displacement over the crack, not just by the local displacement around the crack tip. The crack expansion rate is estimated by taking advantage of the crack self-similarity. Therefore, the accuracy of the calculation is improved. The singular integrals on crack tip elements are also analyzed and are precisely evaluated in terms of a special integral analysis. Combination of these two techniques greatly increases the accuracy in estimating the stress distribution around the crack tip. A variety of two-dimensional cracks, such as subsurface cracks, edge cracks, and their interactions are calculated in terms of the self-similar expansion rate. Solutions are satisfied with errors less than 0.5% as compared with the analytical solutions. Based on the calculations of the crack interactions, a theory for crack interactions is proposed such that for a group of aligned cracks the summation of the square of SIFs at the right tips of cracks is always equal to that at the left tips of cracks. This theory was proved by the mehtod of Self-Similar Crack Expansion in this paper.展开更多
为研究灰尘对光伏发电性能的影响,通过搭建的实验台采集清洁与污染光伏组串每天的发电数据,同时监测气象数据,分析积灰及天气对光伏组件发电性能的影响。结果表明,冬季PM2.5质量浓度的上升和春季沙尘暴天气的频发使得光伏组件表面灰尘...为研究灰尘对光伏发电性能的影响,通过搭建的实验台采集清洁与污染光伏组串每天的发电数据,同时监测气象数据,分析积灰及天气对光伏组件发电性能的影响。结果表明,冬季PM2.5质量浓度的上升和春季沙尘暴天气的频发使得光伏组件表面灰尘积累较多,累计发电量损失增长较快,而夏季由于降水增加,灰尘难以积聚在光伏组件上,累计发电量损失增长缓慢。此外,利用DTW(dynamic time warping)算法来寻找相似日。首先通过熵值法计算出各气象参数的权重,然后按日期逆序逐个计算出每个历史日各个气象参数对应的DTW值,再乘以其权重并相加得到历史日的综合DTW值。通过比较各历史日的综合DTW值,选出与当前日最接近的气象相似日。在避开极端天气的情况下,选择数据集中的一部分作为验证集,并对寻找相似日的判据进行优化,选取每天09:00—15:00的数据分为3个时间段进行分析,并设定平均太阳辐照度不小于600 W/m2的条件。优化后,预测模型的评价指标决定系数为0.83,均方根误差为0.22,预测效果显著提升。最后利用该算法为光伏电站制定清洗策略,经过累计发电量损失与清洗成本的对比,确定在长期不降雨情况下,电站应每28天进行一次清洗。展开更多
基金Research Project of China Ship Development and Design Center。
文摘Screening similar historical fault-free candidate data would greatly affect the effectiveness of fault detection results based on principal component analysis(PCA).In order to find out the candidate data,this study compares unweighted and weighted similarity factors(SFs),which measure the similarity of the principal component subspace corresponding to the first k main components of two datasets.The fault detection employs the principal component subspace corresponding to the current measured data and the historical fault-free data.From the historical fault-free database,the load parameters are employed to locate the candidate data similar to the current operating data.Fault detection method for air conditioning systems is based on principal component.The results show that the weighted principal component SF can improve the effects of the fault-free detection and the fault detection.Compared with the unweighted SF,the average fault-free detection rate of the weighted SF is 17.33%higher than that of the unweighted,and the average fault detection rate is 7.51%higher than unweighted.
文摘The Self-Similar Crack Expansion (SSCE) method is proposed to evaluate stress intensity factors at crack tips, whereby stress intensity factors of a crack can be determined by the crack opening displacement over the crack, not just by the local displacement around the crack tip. The crack expansion rate is estimated by taking advantage of the crack self-similarity. Therefore, the accuracy of the calculation is improved. The singular integrals on crack tip elements are also analyzed and are precisely evaluated in terms of a special integral analysis. Combination of these two techniques greatly increases the accuracy in estimating the stress distribution around the crack tip. A variety of two-dimensional cracks, such as subsurface cracks, edge cracks, and their interactions are calculated in terms of the self-similar expansion rate. Solutions are satisfied with errors less than 0.5% as compared with the analytical solutions. Based on the calculations of the crack interactions, a theory for crack interactions is proposed such that for a group of aligned cracks the summation of the square of SIFs at the right tips of cracks is always equal to that at the left tips of cracks. This theory was proved by the mehtod of Self-Similar Crack Expansion in this paper.
文摘为研究灰尘对光伏发电性能的影响,通过搭建的实验台采集清洁与污染光伏组串每天的发电数据,同时监测气象数据,分析积灰及天气对光伏组件发电性能的影响。结果表明,冬季PM2.5质量浓度的上升和春季沙尘暴天气的频发使得光伏组件表面灰尘积累较多,累计发电量损失增长较快,而夏季由于降水增加,灰尘难以积聚在光伏组件上,累计发电量损失增长缓慢。此外,利用DTW(dynamic time warping)算法来寻找相似日。首先通过熵值法计算出各气象参数的权重,然后按日期逆序逐个计算出每个历史日各个气象参数对应的DTW值,再乘以其权重并相加得到历史日的综合DTW值。通过比较各历史日的综合DTW值,选出与当前日最接近的气象相似日。在避开极端天气的情况下,选择数据集中的一部分作为验证集,并对寻找相似日的判据进行优化,选取每天09:00—15:00的数据分为3个时间段进行分析,并设定平均太阳辐照度不小于600 W/m2的条件。优化后,预测模型的评价指标决定系数为0.83,均方根误差为0.22,预测效果显著提升。最后利用该算法为光伏电站制定清洗策略,经过累计发电量损失与清洗成本的对比,确定在长期不降雨情况下,电站应每28天进行一次清洗。
