在复杂的交通枢纽结构设计中因管道线路的铺设需要在钢筋混凝土(RC)梁腹板进行开洞,为研究双开洞RC梁的疲劳性能,以荷载水平比、荷载作用洞口位置、疲劳次数为变化参数设计了6根双开洞RC梁,并对其进行反复荷载作用下的疲劳试验。研究了...在复杂的交通枢纽结构设计中因管道线路的铺设需要在钢筋混凝土(RC)梁腹板进行开洞,为研究双开洞RC梁的疲劳性能,以荷载水平比、荷载作用洞口位置、疲劳次数为变化参数设计了6根双开洞RC梁,并对其进行反复荷载作用下的疲劳试验。研究了疲劳加载过程中双开洞RC梁内的受力状态和破坏模式,讨论了各变化参数对双开洞RC梁疲劳性能的影响规律,提出了变形协调模型,并基于该模型揭示了双开洞RC梁在疲劳荷载作用下的损伤演化规律和破坏机理。研究表明:循环荷载作用下,双开洞RC梁疲劳破坏形态为洞口的上下梁斜向裂缝和洞口角部裂缝;荷载上限为0.7 P u和荷载位置作用在洞口中部的试件发生疲劳破坏,双开洞RC梁在0.5 P u荷载水平下具有至少41万次的疲劳寿命;疲劳试验后剩余极限承载力、抗弯刚度和延性随着荷载水平的增大发生退化;当荷载位于洞口中部时试件疲劳后的剩余性能更差;疲劳周期增大至41万次对剩余极限弯矩影响在7%左右,剩余抗弯刚度和延性退化25%左右。基于CEB-FIP 2010提出了适用于双开洞RC梁的疲劳变形预测方法,误差在7%以内。展开更多
针对航空发动机结构复杂、性能退化参数众多、寿命预测精度低等问题,提出了一种基于退化特征相似性的寿命预测方法。首先通过基于Relief算法的退化特征筛选、基于主成分分析(principal component analysis,PCA)的特征提取和基于核函数...针对航空发动机结构复杂、性能退化参数众多、寿命预测精度低等问题,提出了一种基于退化特征相似性的寿命预测方法。首先通过基于Relief算法的退化特征筛选、基于主成分分析(principal component analysis,PCA)的特征提取和基于核函数的特征平滑,提取低维正交多变量退化特征;然后进行特征的相似性匹配,寻找与当前样本特征片段最相似的一组历史样本中的特征片段集合,将这些片段对应的RUL信息融合并采用密度加权方法得到当前样本的寿命预测估计值;最后通过美国国家航空航天局(national aeronautics and space administration,NASA)提供的航空涡轮扇发动机仿真数据集验证了该方法的有效性,其寿命预测性能高于现有几种代表性方法。展开更多
Remaining useful life（RUL） prognostics is a fundamental premise to perform conditionbased maintenance（CBM） for a system subject to performance degradation. Over the past decades,research has been conducted in RUL ...Remaining useful life（RUL） prognostics is a fundamental premise to perform conditionbased maintenance（CBM） for a system subject to performance degradation. Over the past decades,research has been conducted in RUL prognostics for aeroengine. However, most of the prognostics technologies and methods simply base on single parameter, making it hard to demonstrate the specific characteristics of its degradation. To solve such problems, this paper proposes a novel approach to predict RUL by means of superstatistics and information fusion. The performance degradation evolution of the engine is modeled by fusing multiple monitoring parameters, which manifest non-stationary characteristics while degrading. With the obtained degradation curve,prognostics model can be established by state-space method, and then RUL can be estimated when the time-varying parameters of the model are predicted and updated through Kalman filtering algorithm. By this method, the non-stationary degradation of each parameter is represented, and multiple monitoring parameters are incorporated, both contributing to the final prognostics. A case study shows that this approach enables satisfactory prediction evolution and achieves a markedly better prognosis of RUL.展开更多
文摘在复杂的交通枢纽结构设计中因管道线路的铺设需要在钢筋混凝土(RC)梁腹板进行开洞,为研究双开洞RC梁的疲劳性能,以荷载水平比、荷载作用洞口位置、疲劳次数为变化参数设计了6根双开洞RC梁,并对其进行反复荷载作用下的疲劳试验。研究了疲劳加载过程中双开洞RC梁内的受力状态和破坏模式,讨论了各变化参数对双开洞RC梁疲劳性能的影响规律,提出了变形协调模型,并基于该模型揭示了双开洞RC梁在疲劳荷载作用下的损伤演化规律和破坏机理。研究表明:循环荷载作用下,双开洞RC梁疲劳破坏形态为洞口的上下梁斜向裂缝和洞口角部裂缝;荷载上限为0.7 P u和荷载位置作用在洞口中部的试件发生疲劳破坏,双开洞RC梁在0.5 P u荷载水平下具有至少41万次的疲劳寿命;疲劳试验后剩余极限承载力、抗弯刚度和延性随着荷载水平的增大发生退化;当荷载位于洞口中部时试件疲劳后的剩余性能更差;疲劳周期增大至41万次对剩余极限弯矩影响在7%左右,剩余抗弯刚度和延性退化25%左右。基于CEB-FIP 2010提出了适用于双开洞RC梁的疲劳变形预测方法,误差在7%以内。
文摘针对航空发动机结构复杂、性能退化参数众多、寿命预测精度低等问题,提出了一种基于退化特征相似性的寿命预测方法。首先通过基于Relief算法的退化特征筛选、基于主成分分析(principal component analysis,PCA)的特征提取和基于核函数的特征平滑,提取低维正交多变量退化特征;然后进行特征的相似性匹配,寻找与当前样本特征片段最相似的一组历史样本中的特征片段集合,将这些片段对应的RUL信息融合并采用密度加权方法得到当前样本的寿命预测估计值;最后通过美国国家航空航天局(national aeronautics and space administration,NASA)提供的航空涡轮扇发动机仿真数据集验证了该方法的有效性,其寿命预测性能高于现有几种代表性方法。
基金co-supported by the State Key Program of National Natural Science of China (No. 61232002)the Joint Funds of the National Natural Science Foundation of China (No. 60939003)+3 种基金China Postdoctoral Science Foundation (Nos. 2012M521081, 2013T60537)the Fundamental Research Funds for the Central Universities of China (No. NS2014066)Postdoctoral Science Foundation of Jiangsu Province of China (No. 1301107C)Philosophy and Social Science Research Projects in Colleges and Universities in Jiangsu of China (No. 2014SJD041)
文摘Remaining useful life（RUL） prognostics is a fundamental premise to perform conditionbased maintenance（CBM） for a system subject to performance degradation. Over the past decades,research has been conducted in RUL prognostics for aeroengine. However, most of the prognostics technologies and methods simply base on single parameter, making it hard to demonstrate the specific characteristics of its degradation. To solve such problems, this paper proposes a novel approach to predict RUL by means of superstatistics and information fusion. The performance degradation evolution of the engine is modeled by fusing multiple monitoring parameters, which manifest non-stationary characteristics while degrading. With the obtained degradation curve,prognostics model can be established by state-space method, and then RUL can be estimated when the time-varying parameters of the model are predicted and updated through Kalman filtering algorithm. By this method, the non-stationary degradation of each parameter is represented, and multiple monitoring parameters are incorporated, both contributing to the final prognostics. A case study shows that this approach enables satisfactory prediction evolution and achieves a markedly better prognosis of RUL.
