A new time-frequency analysis method based on single mode function decomposition for offshore wind turbines

The Hilbert-Huang transform (HHT) has been widely applied and recognised as a powerful time-frequency analysis method for nonlinear and non-stationary signals in numerous engineering fields. One of its major challenges is that the HHT is frequently subject to mode mixing in the processing of practical signals such as those of offshore wind turbines, as the frequencies of offshore wind turbines are typically close and contaminated by noise. To address this issue, this paper proposes a new time-frequency analysis method based on single mode function (SMF) decomposition to overcome the mode mixing problem in the structural health monitoring (SHM) of offshore wind turbines. In this approach, the structural vibration signal is first decomposed into a set of window components using complex exponential decomposition. A state-space model is introduced in the signal decomposition to improve the numerical stability of the decomposition, and then a novel window-alignment strategy, named energy gridding, is proposed and the signals are constructed in the corresponding gridding. Furthermore, energy recollection is implemented in each gridding, and the reassembling of these components yields an SMF that is comparable to the intrinsic mode function (IMF) of the HHT, but with a significant improvement in terms of mode mixing. Four case studies are conducted to evaluate the performance of the proposed method. The first case attempts to detect three different frequencies in a simulated time-invariant signal. The second case attempts to test a synthesised signal with segmental time-varying frequencies (each segment contains three different frequencies components). The analysis results in these two cases indicate that mode mixing can be reduced by the proposed method. Furthermore, a synthesised signal with slowly varying frequencies is used. These analysis results demonstrate the effective suppression of non-relevant frequency components using SMF decomposition. In the third case, the experimental data from vortex-induced vibration (VIV) experiments sponsored by the Norwegian Deepwater Programme (NDP) are used to evaluate the proposed SMF decomposition for vibration mode identification. In the final case, field data acquired from an offshore wind turbine foundation and offshore wind turbine are analysed. The mode identification results obtained using SMF decomposition are compared with those produced by the HHT. The comparison demonstrates superior performance of the proposed method in identifying the vibration modes of the VIV experimental and field data.     

多源激励谱反演振荡研究

多源激励谱反演属于逆问题范畴,反演解在系统共振频率及低频段会出现振荡.文章以数值仿真与理论相结合的方法,开展了矩形板四种方案的虚拟实验来研究多源激励谱反演振荡问题.首先进行了板的模态分析和响应分析,数值解与理论解吻合良好,验证了方法的可行性.然后分别以直接法、截断奇异值分解法(TSVD)、Tikhonov正则法对板的多源激励谱进行反演.结果表明:(1)多源激励谱反演时,保证响应点数量大于激励点数量可有效抑制反演解在共振频率处的振荡;(2)采用TSVD法、Tikhonov正则法可有效抑制反演解的低频振荡.最后,用反演的激励谱重构接收点的响应谱,重构解与理想值吻合较好,验证了虚拟实验及方法的可信性.     

基于经验模态分解的大型轮船锚机齿轮箱故障诊断

经验模态分解法(EMD)是一种针对信号变化的自适应处理方法。针对某大型轮船锚机齿轮箱,首先通过测量不同转速和位置的振动信号预估故障源;其次对采集出的原始故障信号进行处理,提取故障特征,即通过EMD将原始信号分解为若干个IMF分量,不同的IMF分量包含不同频段所对应的能量,然后再对IMF分量进行包络分析和谱分析,最终便可确定故障源,并对其进行修复。通过测试比较,验证了EMD理论方法的正确性和有效性。     

VIV response of a flexible cylinder with varied coverage by buoyancy elements and helical strakes

Many significant engineering challenges have emerged as the petroleum industry has moved their field development and production activities into increasingly deeper water depths. The design of deepwater marine risers presents the combined challenges to minimize top tensioning requirements, mitigate any flow-induced vibrations, and if possible to increase the expected fatigue life of these slender structural members. As part of the design process to achieve these goals external buoyancy modules and strakes have been employed. To gain insight into the complex multi-mode response behavior a recent experimental study was performed and the analysis of selected data sets is presented. In the experiments a horizontal cylinder with a length to diameter ratio of 263 was fitted with a variety of strake and buoyancy element configurations. The models were towed at uniform speeds ranging from 0.4 to 2.0 m/s and fiber optic strain gages were used to measure both in-line and cross-flow strain response. The resulting time series information was processed utilizing the method of time domain decomposition formulated for strain data input and the introduction of modal assurance criterion to resolve the modal strain information that included frequency, mode shape, and critical damping ratio information. The pre-tensioned cylinder without appendages was used as a base case and the results were basically consistent with expectations. In the case of 0.8 m/s low-tension test, multiple closely spaced non-overlapping peaks were observed in both in-line and cross-flow directions and were identified as being of the same mode with mode shapes distorted away from purely sinusoidal behavior. The test data for the 100% coverage by helical stakes demonstrated the effectiveness of that suppression device over the range of current velocities investigated. The most interesting case was that of a staggered combination of helical strakes and buoyancy element whose total for each type of coverage was equal. This effective asymmetric VIV suppression approach is presented and discussed in detail.     

基于工程分解的船体生产物流管理研究

船舶建造的物流特点表现为:物流量大、周期长且具有动态性。零件生产能力不足、理料效率低下和配送困难是国内造船企业在实施精益造船、建立现代造船模式的过程中普遍遇到的船体结构建造问题。通过对某船厂的生产布局进行分析,运用工程分解的理论对船体生产物流管理模式进行深入探讨,并提出新的结构流向设计原则,以期提高管理的精细化与数字化,最终形成规范的物流管理体系。     

基于机器学习的舰船机电装备故障诊断

随着信息技术的发展,以机器学习、模式识别为代表的人工智能技术在故障诊断领域逐步得到应用。通过对振动噪声信号的采集,利用时频分析技术对信号进行分解,并提取故障特征参数,再利用机器学习或模式识别技术对信号进行判别分类,可以实现舰船机电装备的智能诊断。为了验证该方法,选择经验模态分解方法进行信号分解,采用支持向量机进行诊断分类。通过实验表明,该方法有着较高的诊断精度,故障诊断率达到了96.7%,可以对舰船机电装备常见故障进行准确的智能诊断。     

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为促进我国低碳交通运输体系建设,研究交通运输结构优化的碳减排有效性.在分析交通运输结构对其碳排放影响机制的基础上,基于1989-2009年数据,结合静态因素分解与动态冲击响应技术,量化研究20年来我国交通运输结构变化对碳排放的影响,以及结构优化冲击下,交通运输碳排放和结构优化自身的响应方向、程度与持续期数.根据实证结果,分别讨论了结构因素对交通运输碳排放的短期和长期影响,结构因素与其他交通运输碳排放主要影响因素的差异,以及结构因素与交通运输碳排放两者间的动态互动机理,为相关研究和政策制定提供实证参考.     

基于EMD方法的我国主要海港货物吞吐量变化规律研究

利用经验模态分解（EMD）方法对近10年来我国沿海主要港口货物吞吐量变化的内在规律进行了研究．结果表明：（1）EMD方法能有效的揭示港口货物吞吐量变化的一些内在规律,如吞吐量变化的季度、半年、年和多年周期;（2）进入21世纪以来,我国港口吞吐量增长势头强劲,但2009年后明显受到金融危机的影响．     

基于改进局域波法的柴油机振动信号分析

提出了一种基于奇异值分解降噪的柴油机振动信号局域波分解方法,首先对原始振动信号进行相空间重构和奇异值分解,然后根据分解的奇异值降噪,接着利用经验模式分解法提取降噪后振动信号的基本模式分量,最后作出时频曲线和边界谱曲线。对滤波前和滤波后的柴油机振动信号进行了局域波分析,分析结果表明,奇异值分解能够有效地提高信噪比,突出原始振动信号的特征,使得降噪后的振动信号分解出的基本模式分量和时频曲线以及边界谱曲线具有更明确的物理意义,有利于对柴油机进行精确诊断。     

铁路集装箱运输动态服务网络设计及Benders分解算法

充分考虑箱流的中转方案,研究铁路集装箱运输动态服务网络的设计方法。以总成本最小为优化目标,构建了铁路集装箱运输动态服务网络设计的线性规划模型。根据模型特点,采用Benders算法进行求解,将问题分解为服务网络设计的主问题及箱流分配的子问题,通过计算子问题的对偶模型不断产生主问题的割平面,由此进行迭代求解。为克服算法收敛速度慢的缺点,在主问题模型中添加有效不等式,使主问题更加紧致。以北京、郑州等集装箱办理站构建的运输网络为例,验证了模型和算法的有效性。算例结果表明,对于求解大规模的集装箱运输动态服务网络设计问题,改进后的算法运行46 s得到优化解,GAP为1.56%,未改进的Benders算法运行相同时间后,GAP为45.17%,改进策略的运用有效提高了计算效率; 所得服务网络的总成本比所有箱流均采用直达运输模式服务网络的总成本减少了20%;与现有集装箱班列开行方案相比,优化后的班列发车时段、开行频率在满足运输需求的同时,保证了各组箱流能在规定运到期限内送至目的站。