为了能够准确地判定系统的时间动态特性,提出一种基于设备运行状态信息的实时可靠度算法。该算法从设备的状态特征指标出发,结合贝叶斯(Bayes)方法和KM(Kaplan-Meier)评估器思想,无需对设备失效概率密度函数(Probability Density Functi...为了能够准确地判定系统的时间动态特性,提出一种基于设备运行状态信息的实时可靠度算法。该算法从设备的状态特征指标出发,结合贝叶斯(Bayes)方法和KM(Kaplan-Meier)评估器思想,无需对设备失效概率密度函数(Probability Density Function,PDF)进行估计,也不会因样本太少而引起大的估计误差。以上述方法计算所得到的可靠度作为刀具实际可靠度,对比分析支持向量机(Support Vector Machine,SVM)与反馈神经网络(Back Propagation Neural Network,BPNN)两种方法在不同样本条件下的预测精度。结果表明在大样本条件下,两模型都具有较高的预测精度和较小的预测误差;小样本条件下,BPNN方法预测误差过大而达不到预测的功能,而SVM方法仍能保持较高的预测精度和较小的预测误差,与BPNN相比具有明显的优势。展开更多
Bridge health monitoring （BHM） has become increasingly significant in the life-cycle of the structure such as maintenance, repair and rehabilitation. It is necessary to use BHM information efficiently to assess the ...Bridge health monitoring （BHM） has become increasingly significant in the life-cycle of the structure such as maintenance, repair and rehabilitation. It is necessary to use BHM information efficiently to assess the working conditions of the bridge. The main objective of this study is to develop an effective method and establish a framework for the real-time reliability assessment based on BHM acceleration information. The first-passage probability and its further development have been proposed to as- sess the reliability probability. The first-passage probability shows the probability of that a scalar process exceeds a designated threshold during a given time interval. The advantage of the proposed method is the assessment of the real-time reliability probability based on the monitoring information during an assessment reference period. Furthermore, the velocity data and displacement data are calculated from the acceleration monitoring data using the relationships between their power spectral density （PSD） functions. The real-time reliability assessment of Donghai Bridge, which is the first large scale cross-sea bridge in China, demonstrates that the proposed method is efficient and effective.展开更多
基金supported by the National Basic Research Program of China(“973”Project)(Grant No.2013CB036305)Ministry of Transport of the People’s Republic of China(Grant No.2015318J38230)National Science and Technology Support Plan(Grant No.2012BAJ11B01)
文摘Bridge health monitoring （BHM） has become increasingly significant in the life-cycle of the structure such as maintenance, repair and rehabilitation. It is necessary to use BHM information efficiently to assess the working conditions of the bridge. The main objective of this study is to develop an effective method and establish a framework for the real-time reliability assessment based on BHM acceleration information. The first-passage probability and its further development have been proposed to as- sess the reliability probability. The first-passage probability shows the probability of that a scalar process exceeds a designated threshold during a given time interval. The advantage of the proposed method is the assessment of the real-time reliability probability based on the monitoring information during an assessment reference period. Furthermore, the velocity data and displacement data are calculated from the acceleration monitoring data using the relationships between their power spectral density （PSD） functions. The real-time reliability assessment of Donghai Bridge, which is the first large scale cross-sea bridge in China, demonstrates that the proposed method is efficient and effective.
