【作者】 林丽；

【导师】 赵德有；

【作者基本信息】 大连理工大学 ， 船舶与海洋结构物设计制造， 2009， 博士

【摘要】 近海油气的开发主要使用固定式海洋平台,最常用的固定式海洋平台是导管架式海洋平台,管结点裂纹破坏问题对导管架式海洋平台来讲是一个公认的设计问题,恶劣的海洋环境,有时会使导管架式海洋平台的结点出现裂纹断裂,早期诊断出结点裂纹是导管架式海洋平台的关键问题。因此,运用声发射技术对海洋平台进行动态监测具有重要的现实意义。声发射技术的关键是从声发射信号中提取特征,信号分析和处理是特征提取最常用的方法。由于声发射信号是非平稳非线性信号,因此有必要选择恰当的适合于非平稳非线性信号分析的信号处理方法。由于时频分析方法能同时提供声发射信号的时域和频域信息,因而人们广泛进行了研究。但常用的时频分析方法如窗口傅里叶变换和小波变换等都有各自的局限性。近年来,一种适合于处理非平稳信号的时频分析方法局域波法被提出来以后,经验证在很多方面的应用效果都优于其它的信号处理方法。本文在国家自然科学基金项目的资助下,提出将局域波法引入到声发射特征提取中。将局域波用于分析导管架海洋平台结构模型的声发射信号,以获得声发射信号的时频特征和频率能量分布。通过局域波分解将声发射信号分解为一组本征模函数分量(IMF),对每一个IMF分量进行希尔伯特变换获得信号能量随时间和频率的变化;由局域波时频谱得到边际谱,反映声发射信号的能量频率分布特征。分析了导管架海洋平台结构模型模拟声发射信号的特征,运用局域波分析方法监测到导管架海洋平台结构模型裂纹声发射信号的出现。试验表明,局域波法可以有效地捕捉到导管架海洋平台结构裂纹的声发射信号,在声发射信号处理领域将会有广阔的应用前景。本文提出了一种新的结构裂纹声发射信号特征提取方法——近似熵法,近似熵是一种最近新发展起来的度量序列复杂性的统计方法。介绍了近似熵的概念及性质,并对仿真声发射信号和预制裂纹钢管在逐渐加载作用下的裂纹扩展声发射信号进行了近似熵计算分析,结果表明,近似熵在表征声发射信号的复杂性方面有明显的效果,从而为声发射信号分析提供了一种很有效的新方法。提出将近期发展的局域波法和近似熵法相结合应用于声发射信号的特征提取中。首先,将声发射信号进行局域波分解,得到自适应的本征模函数分量,然后对各本征模函数分量计算近似熵,描述各本征模函数分量的复杂程度,监测声发射信号的发生和发展,量化声发射信号的特征。通过预制裂纹钢管逐渐加载试验,分析计算了钢管裂纹声发射信号的各本征模函数分量的近似熵,表明局域波法与近似熵相结合的方法可以有效地提取声发射信号的特征,从而为声发射信号特征提取提供了一种新的方法。提出将近期发展的局域波法和神经网络相结合应用于声发射信号特征提取识别中。首先,将海洋平台结构声发射信号进行局域波分解,得到自适应的本征模函数分量,然后从各本征模函数分量中提取能量特征参数作为神经网络的输入参数来识别海洋平台结构的声发射信号。通过对导管架海洋平台结构模型声发射信号的试验数据分析表明,以局域波法提取各频带能量作为特征参数的神经网络方法可以准确、有效地识别导管架海洋平台结构模型声发射信号。从而为海洋平台结构声发射信号特征提取识别提供了一种新的方法。为管理大量导管架海洋平台结构声发射信号试验数据,运用识别算法对声发射信号进行定性识别研究,提出了建立以开放式数据库为支持,基于局域波法的导管架海洋平台结构声发射信号识别系统平台。采用PowerBuilder和Matlab等编程工具,结合SQL Server数据库技术,通过多种接口设计方法实现了导管架海洋平台结构声发射信号的数据、识别算法等的有机结合。通过试验证明,该识别平台操作简便,具有较强的实用价值,对导管架海洋平台结构声发射信号的科学研究试验数据和实际监测数据的管理及识别提供了便利。更多还原

【Abstract】 Fixed offshore platforms are widely employed in the offshore oil-gas exploration and jacket offshore platforms are the common ones of fixed offshore platforms. Tubular joint fatigue failures have been commonly regarded as the design problem for jacket offshore platforms. When a fatigue fracture occurs in the node of jacket offshore platforms, an early diagnosis is the key in hostile ocean environments.Therefore it is extremely significant to detect the crack of offshore platforms using the acoustic emission (AE) technique. As we all know, extracting features is the key of the AE technique. To extract features effectively, signal processing-based methods are widely used today. Due to the fact that most of the AE signals present non-stationary and nonlinear properties, it is essential to choose appropriate signal processing methods that are suitable for non-stationary and nonlinear signals to extract AE signals features.The time-frequency analysis methods are widely studied in AE signals processing because they can provide both time and frequency domain information of a signal simultaneously. However, the time-frequency analysis methods such as windowed Fourier transforms(WFT) and wavelet transform have their own limitations. Recently, a novelty for non-stationary signals named as Local Wave Analysis (LWA), has been put forward and confirmed to be superior to the other signal processing methods in many applications. Supported by National Natural Science Foundation, this dissertation introduces LWA into AE signals processing, whose aim is to extract feature of AE signals by using LWA. In this paper we show the possibility of using local-wave to analyze the time-frequency feature of the acoustic emission signals produced by the crack in the offshore structure model.In the investigation, we used a local wave decomposition technique, allowing time series of acoustic emission signal being decomposed into a small number of intrinsic mode function components (IMF). Under the Hilbert transformation process, IMF can be translated into an expression called Hilbert spectra, which exhibits the amplitude-frequency-time distribution of the data. The marginal spectra, which present the energy-frequency distribution of the data, were obtained by integrating the Hilbert spectra with time. The feature of the offshore structure simulation acoustic emission signals could be extracted by applying local wave analysis. The characteristics of the crack acoustic emission signals in the offshore structure, was found which indicated the acoustic emission occurrence by using the local-wave analyzing. Consequently, the experimental results show that the proposed approach is able to effectively capture the significant information reflecting the acoustic emission in the offshore structure, and thus has good potential in the field of acoustic emission signal feature extraction.This thesis presents a new approach to characterize the acoustic emission signals of the structure cracking in the process of loading based on the Approximate Entropy (ApEn), which is a statistical measure that quantifies the regularity of a time series. The conception and nature are introduced. Successful application has been achieved to analyze the simulating acoustic emission signals and the acoustic emission signals produced by the crack in the steel tube. The results show that ApEn has obvious high ability to quantify the complexity of signals, thereby providing a new effective tool for the acoustic emission signals processing.A new approach through combining the recently developed Local Wave method with the Approximate Entropy to characterize the acoustic emission signals was studied in the thesis. Firstly, local wave method is used to decompose the acoustic emission signal into a number of intrinsic mode functions (IMFs), and then calculate the ApEn of IMFs to describe their complexity, detect the occurrence and the development and quantify the characteristic of the acoustic emission signals. The effectiveness of the proposed methods has been demonstrated by using the acoustic emission signals from the steel tube cracking during a quasi-static loadings test. The experimental results show that the proposed approach can effectively capture the significant information reflecting the acoustic emission, and thus has good potential in the field of acoustic emission signal feature extraction.This dissertation presents a new approach, which is combined the recently developed Local Wave method with the neural network to characterize and identify the acoustic emission signals of offshore structures. Local wave method is used to decompose the acoustic emission signals of offshore structures into a number of intrinsic mode functions, and then energy feature parameter extracted from IMFs could be served as input parameter of neural networks to identify the acoustic emission signals of offshore structures. The experimental analysis results from the acoustic emission signals of offshore structures model show that the approach of neural network based on local wave extracting energy parameter as feature can effectively recognise the offshore structures AE signals, and thus providing a new effective tool for the acoustic emission signal feature extraction identification of offshore structures.This paper presents an identification platform of acoustic emission signals of offshore structures supported by the open country-wide interconnected database and based on local wave method, which can manage a lot of acoustic emission signal experimental data of offshore structures and study the nature of the acoustic emission signals by multi-recognition arithmetic. By using the program tools such as PowerBuilder and Matlab, in combination with database technique, the data and algorithm of acoustic emission signals of offshore structures were combined by designing several interfaces. The results show that the identification platform has powerful functions with easy operation, and has more practical values. And the system can offer convenience for managing and identifying the experimental data and the practical data of acoustic emission signals of offshore structures.更多还原