基于马田系统的海洋平台健康状态分析
发布时间：2018-10-17 21:21
【摘要】：海洋平台作为海上油田开发的重要设备,其兼有结构复杂和重量大的双重特点,这种大型工程结构物的健康状况是海上油田开发的关键所在。近几年,发生不少海洋平台由于疲劳破坏而引起垮塌的案例。对于海洋平台而言,其损伤在海洋平台结构的服役期间是不可避免的。海洋平台结构的健康监测与损伤诊断已成为刻不容缓的重要课题。采用振动测量和分析技术对海洋平台结构进行识别,既能对平台结构的完整性适时地做出总的评价,又能为系统识别提供必要的数据。现有的海洋平台健康分析方法主要是针对海洋平台结构或者力学角度分析,此类方法对于海洋平台大型机械作业长期累积作业状态分析很有成效,但实时的监控海洋平台健康状态分析,以及建立一个系统的评价方法来评价其实时作业环境的优劣问题,还没有解决。本文运用马田系统的理论方法,将一种新的多元数据定量决策的模式识别方法应用到海洋平台数据分析中。以“102号”海洋平台数据为例,建立了海洋平台健康状态分析系统,通过运用MATLAB、SPSS、MINITAB等数据处理分析软件对各个监测传感器获得监测量数据进行处理,将提取到的正常与异常状态分析样本进行分类,使用正交表筛选所建系统的特征变量,分析信噪比数据结果得到优化后的特征数据集合。最后,采用ROC曲线分析和费歇尔判别分析两种阈值确定方法进行马氏距离临界值的计算。通过对比两种方法的计算结果,确定出更准确的阈值范围。作为海洋平台健康状态分析的新方法,为后期未知样本提供一种模式识别与预测的新方法。
[Abstract]:As an important equipment for offshore oil field development, offshore platform has the dual characteristics of complex structure and heavy weight. The health condition of this large engineering structure is the key to offshore oil field development. In recent years, there have been many cases of offshore platform collapse due to fatigue damage. For the offshore platform, the damage is inevitable during the service of the offshore platform structure. The health monitoring and damage diagnosis of offshore platform structure has become an important task without delay. Using vibration measurement and analysis technology to identify offshore platform structure can not only evaluate the integrity of platform structure in good time but also provide necessary data for system identification. The existing health analysis methods of offshore platforms are mainly aimed at the structural or mechanical analysis of offshore platforms. This kind of method is very effective for long-term cumulative operation state analysis of large-scale mechanical operations of offshore platforms. However, the problems of real-time monitoring and monitoring of the health status of offshore platforms and the establishment of a systematic evaluation method to evaluate the merits and demerits of real-time operating environment have not been solved. In this paper, a new pattern recognition method of multivariate data quantitative decision is applied to the data analysis of offshore platform by using the theory method of Martian system. Taking the data of "102" offshore platform as an example, the health status analysis system of offshore platform is established. By using MATLAB,SPSS,MINITAB and other data processing and analysis software, the monitoring and measuring data obtained from each monitoring sensor are processed. The extracted normal and abnormal state analysis samples are classified, and the orthogonal table is used to filter the feature variables of the system, and the optimized feature data set is obtained by analyzing the results of SNR data. Finally, two threshold determination methods, ROC curve analysis and Fischer discriminant analysis, are used to calculate the critical value of Markov distance. By comparing the calculation results of the two methods, a more accurate threshold range is determined. As a new method of health state analysis for offshore platform, it provides a new method for pattern recognition and prediction for later unknown samples.
【学位授予单位】：大连海事大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TE95
本文编号：2278022
[Abstract]:As an important equipment for offshore oil field development, offshore platform has the dual characteristics of complex structure and heavy weight. The health condition of this large engineering structure is the key to offshore oil field development. In recent years, there have been many cases of offshore platform collapse due to fatigue damage. For the offshore platform, the damage is inevitable during the service of the offshore platform structure. The health monitoring and damage diagnosis of offshore platform structure has become an important task without delay. Using vibration measurement and analysis technology to identify offshore platform structure can not only evaluate the integrity of platform structure in good time but also provide necessary data for system identification. The existing health analysis methods of offshore platforms are mainly aimed at the structural or mechanical analysis of offshore platforms. This kind of method is very effective for long-term cumulative operation state analysis of large-scale mechanical operations of offshore platforms. However, the problems of real-time monitoring and monitoring of the health status of offshore platforms and the establishment of a systematic evaluation method to evaluate the merits and demerits of real-time operating environment have not been solved. In this paper, a new pattern recognition method of multivariate data quantitative decision is applied to the data analysis of offshore platform by using the theory method of Martian system. Taking the data of "102" offshore platform as an example, the health status analysis system of offshore platform is established. By using MATLAB,SPSS,MINITAB and other data processing and analysis software, the monitoring and measuring data obtained from each monitoring sensor are processed. The extracted normal and abnormal state analysis samples are classified, and the orthogonal table is used to filter the feature variables of the system, and the optimized feature data set is obtained by analyzing the results of SNR data. Finally, two threshold determination methods, ROC curve analysis and Fischer discriminant analysis, are used to calculate the critical value of Markov distance. By comparing the calculation results of the two methods, a more accurate threshold range is determined. As a new method of health state analysis for offshore platform, it provides a new method for pattern recognition and prediction for later unknown samples.
【学位授予单位】：大连海事大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TE95
【引证文献】
相关会议论文 前1条
1 曾江辉;曾凤章;陈嵩辉;;基于支持向量机的马田系统阈值确定方法研究[A];第三届中国质量学术论坛论文集[C];2008年
，本文编号：2278022
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