Multicomponent decomposition of a time-varying acoustic Doppler signal generated by a passing railway vehicle using Complex Shifted Morlet Wavelets

Complex Shifted Morlet Wavelets (CSMW) present a number of advantages, since the concept of shifting the Morlet wavelet in the frequency domain allow the simultaneous optimal selection of both the wavelet center frequency and the wavelet bandwidth. According to the proposed method, a cluster of CSMW wavelets is used, covering appropriate ranges in the frequency domain. Then, instead of directly processing the instantaneous frequency of each CSMW, an invariance approach is used to indirectly recover the individual harmonic components of the signal. This invariance approach is based actually on the same rotational approach, using the same matrix properties, which consists the core of the well known ESPRIT algorithm. Moreover, the DESFRI (DEtection of Source Frequencies via Rotational Invariance) approach is introduced to support the proposed CSMW method to semi-automated selection of the center frequency of the applied Morlet window. This approach is based on the singular values that are extracted as an intermediate product of the proposed decomposition process. By the application of the method in a multi-component synthetic signal a way to select the critical parameters of the Morlet wavelet, is investigated. The method is further tested on a time-varying acoustic Doppler signal generated by a passing railway vehicle, indicating promising results for the estimation of the variable instantaneous frequency and the multi-component decomposition of it.     

Potential Benefits of Coastal Ocean Observing Systems to Alaskan Commercial Fisheries

In this article we attempt to illustrate the potential benefits to Alaskan commercial fisheries expected from enhancements to the Alaska Ocean Observing System (AOOS) through changes in fishery management strategies. In particular we show how the use of improved AOOS data in research, stock assessment, and ultimately fisheries management has the potential to result in significant benefits in the Bering Sea and Gulf of Alaska groundfish and Kodiak king crab fisheries. We show through a case study approach that information such as might be provided by an enhanced AOOS could conceivably contribute over $600 million in additional annual revenue in Alaska's groundfish fishery. In addition we estimate that had the information from such a system been available in the 1970s and 1980s the Kodiak king crab stock collapse could have been avoided and $60 million in annual revenues generated. Benefit estimates (as measured by revenue increases) are based on the assumptions that when better data is delivered those data will be integrated into stock assessment models; when better data are integrated into the models the new data will actually improve the reliability of the models; and when the reliability of the models is improved predictions will be accepted by managers or industry members.     

基于小波降噪的经验模式分解方法研究

通过正常工况下的转子位移信号,分析不同幅值噪声对经验模式分解(EMD)分解的影响。对于幅值较小的随机噪声,EMD分解可以自适应地将主要特征信息从噪声中分解出来。而对于幅值较大的噪声,小波降噪的EMD分解则可以有效避免模态间能量泄漏,从而得到准确的分析结果。     

船舶波浪弯矩短期预报研究

采用两种方法（一是利用WALCS软件，基于三维频域线性水动力理论；二是利用3小时波浪弯矩有义值MS，根据极值I型理论）进行船舶波浪弯矩短期预报，并分别计算得到16艘船舶（6艘散货船、10艘油船）的短期波浪弯矩。经比较发现，根据极值I型理论得到的短期波浪弯矩与WALCS软件短期波浪弯矩偏差较小，在工程实践中具有一定参考价值。     

基于G(1,1)模型的物流产业增加值预测研究

物流产业增加值是指物流产业在一定时期内通过物流活动为社会提供的最终成果的货币表现,是物流业发展的核心指标之一。本文以灰色系统理论为基础,根据2006~2011年全国物流产业增加值数据,建立了单因子系统云模型G(1,1)。通过对模型拟合和预测精度的检验,证明该模型能满足预测要求。该研究为物流产业发展决策提供了一种新的预测方法。     

基于EMD柴油机气阀声发射诊断技术试验研究

以4120SG柴油机为研究对象,通过故障模拟试验测试了柴油机在气阀正常状态、气阀漏气状态下的缸盖声发射信号,对燃烧段声发射信号进行经验模态分解(Experience Mode Decomposition,EMD),计算了各个本征模分量(Intrinsic Mode Function,IMF)的能量分布及EMD能量熵。研究表明声发射信号能量主要集中在前3个IMF分量内,随着气阀漏气故障严重程度的增加,代表最高频IMF1分量能量相对增加,而能量熵H减少。通过自定义特征参数,提高了声发射应用于柴油机气阀漏气诊断方法的灵敏度。     

风暴条件下自升式平台桩腿强度对节距敏感性分析研究

文章以自升式平台桁架式桩腿优化设计技术研究为目标,完成风暴条件下桩腿强度对节距敏感性分析。通过风洞试验技术获取风载荷数据;基于STOKES五阶波理论和MORISON方程,采用DNV开发的SESAM软件WAJAC模块分析获取波流载荷数据;通过特征值分析得到平台自振周期和一阶偏移值,进而获取计及DAF效应的惯性载荷和P-△效应的二次惯性矩。基于上述载荷计算结果,重点对不同节距的桩腿结构进行强度校核,给出目标平台桩腿节距优选值,形成桩腿强度对节距敏感性分析技术,为平台核心部件的自主设计提供技术参考。     

基于自适应奇异值标准谱和EMD的柴油机故障诊断

针对柴油机多发故障,提出了自适应奇异值标准谱和经验模态分解(Empirical Mode Decomposition,EMD)相结合的故障诊断模型。通过计算平均最近邻域发散度和奇异值标准谱的方法自适应地选择奇异值分解的嵌入维数和重构阶数,提高了奇异值分解降噪的精度。对降噪后的信号进行EMD分解,并利用调整余弦相似度标准提取反映信号真实特征的主固有模态函数(Intrinsic Mode Function,IMF),进而提取故障特征参数。将此模型应用于F3L912柴油机进气门漏气、单缸失火和多缸失火等故障的诊断,通过提取峭度和过零率作为故障特征,获得了较高的故障分类准确率。     

混凝土耐久性监测传感器的研制及应用

研制了可预埋于混凝土结构中,能够实时监测混凝土中钢筋腐蚀电位、腐蚀速率、氯离子浓度和p H值的耐久性监测传感器,并对传感器的性能进行研究。研究结果表明:在测试的时间范围内,传感器能够较好地反映混凝土中氯离子浓度、p H值、钢筋腐蚀电位和腐蚀速率的变化趋势,可以用于监测混凝土结构的耐久性健康状况。     

钓浜渔港防波堤修复加固新技术

钓浜渔港是温岭市中心渔港的重要组成部分,其防波堤建成后历经"卡努"、"麦莎"等超强台风正面袭击,护面结构严重受损,存在着安全隐患,修复工程技术难点是处置数千个人工块体。先进行波浪断面模型试验再确定施工设计图,并采用分解坡面块体形成垫层的新技术,解决了修复工程技术难点。工程完成后,钓浜渔港抗灾能力已提高到百年一遇。