摘要
随着信息科学技术的快速发展,国防和国民经济各个领域的大型机械系统、工程系统和自动化系统复杂性与自动化程度不断提高,人们迫切需要提高整个系统的可靠性、可维修性和安全性。由于系统在运行过程中是一个相互影响的统一整体,其不同设备之间存在着密切的联系且同一设备不同部分之间也含有紧密的耦合,使得发生的故障难以准确的判断和定位,若不能将其及时诊断并排除就会导致设备甚至整个系统不能有效正常运行,严重的故障会造成灾难性事故。由于计算机技术及各种智能仪表在工业过程中的广泛应用和不断发展,反映系统运行状况的海量过程数据被采集并存储下来。如何利用这些积累的海量离、在线运行数据,在难以建立起系统机理模型进行故障诊断的情况下,通过分析数据特征及其内在规律实现对工业过程及其相关设备的故障诊断,最终到达提高产品质量、保证生产安全、减少经济损失的目的,成为近年来工程和学术界的研究热点之一。
为此,本文从大型自动化系统的运行状态特点和动态非平稳过程的诊断需求分析出发,利用相应的数学理论并结合工程实际,系统深入地开展了数据驱动的多元统计故障诊断研究,为进一步完善基于数据驱动的故障诊断理论体系作出一定程度的贡献,以期实现数据驱动的故障诊断在实际中的良好应用。通过采用现有数据驱动的故障诊断体系中的各种方法以及本文所提出的一些新方法,以6135D柴油机在漏气故障下的海量数据作为诊断的数据样本,分别对该数据样本进行了数据实验。数据实验的结果表明,本文所开展的各项研究工作及在此基础上所取得的一些创新性成果,在具有较为广泛的应用前景的同时,还具有较为重要的科学研究意义。本文所进行的主要研究工作如下:
①通过将含有恒偏差、缓变、突变和和高频正弦等几种类型故障的染噪信号在各个尺度下进行小波分解并提取出相应的细节特征,详细地分析了动态非平稳过程数据的多尺度特征,得到了故障特征在数据空间中的分布规律。针对小波滤波中所出现的边缘效应造成滤波后信号所产生的毛刺和尖峰,结合Toeplitz矩阵和Gram-Schmidt正交化算法设计了边缘校正滤波器,从而减轻边缘效应对信号滤波造成的不良影响。
②分析了小波变换检测信号奇异性的相关原理。为克服传统基于小波变换的信号奇异性检测方法不能克服噪声的影响,而预先对信号降噪的同时又容易造成部分故障信息的丢失,提出了一种基于多尺度积的故障奇异性检测与奇异点定位方法。该方法充分利用了小波变换多尺度积在有效增强信号的细节特征的同时又能有效抑制噪声影响的特性。
③针对传统PCA因模型固定、尺度单一不能准确反映动态非平稳过程数据的时变和多尺度等统计特性的缺陷,通过在线多尺度滤波(OLMS),结合递归主元分析(APCA)和多尺度主元分析(MSPCA),并在已设计的边缘校正滤波器基础上,提出了基于在线多尺度滤波的多元统计故障诊断方法,实现了多元统计方法对过程数据的实时监控。
④针对动态非平稳过程数据的时变性和多尺度性导致故障诊断准确率下降及故障准确定位难以实现的不足,提出滑动窗口多尺度主元分析( Moving Window Multi-scale Principal Component Analysis,MW-MSPCA),通过小波阈值消噪解决统计模型偏离与数据相关性降低之间的矛盾,并在各个尺度上利用滑动窗口主元分析实现模型更新,然后借助三维贡献图描述反映过程行为变化的各独立过程变量对统计过程的贡献程度,进而对故障准确定位。
⑤选取6135D型柴油机作为研究对象,分析其故障机理和缸盖处振动信号特点,以该柴油机在漏气故障下的测量数据作为样本,分别采用传统主元分析(PCA)、传统多尺度主元分析(MSPCA)、直接对小波系数进行阈值去噪的改进多尺度主元分析(AMSPCA)、在线多尺度滤波的多尺度主元分析(OLMS-R-MSPCA)、基于滑动窗口的多尺度主元分析(MW-MSPCA)5种算法分别进行了数据实验,并在提出定量分析算法准确率的基础上,按照定量规则对实验结果进行定量分析,最终的分析结果证明了本文所提方法的有效性和可行性。
With the functions gradually improved and the structures increasingly complicated, the large-scale automation systems are the types of important technical equipment being used extensively in national defense and national economy realms, which safety and reliability become crucial.It is quite difficult to establish an accurate physical model being used to fault diagnosis for such systems in which there are strong nonlinear on their condition behaviors and serious state coupling among the different parts and being subject to the non-gauss noise and various indetermination factors.
Since the non-contact measurement technology and the soft measurement technology and a variety of intelligent instruments in the wide spread of industrial processes are excellently evolving, the massive process data which reflecting the operational status of systems is collected and stored. However, there is a "data rich, information plaque lack of "embarrassing situation in industrial process. In the case of the fault diagnosis model with mechanism is difficult to established for systems, how to use the accumulated mass data from the online or outline operation process, digging deeply out the data characteristics and the inherent laws in the running on the systems process for the related equipment fault diagnosis, and finally to achieve the purpose of improving product quality and ensuring production safety and reducing economic losses, which has become an issue needed to urgent address in aerospace, chemical, manufacturing, transportation and logistics areas, but also is one of the hot focus in recent years of engineering and academic areas.
Therefore, this article is completed by analyzing dynamic non-stationary characteristics from large automated system running and aiming at needs of the diagnostic process. To achieve the purpose of enhancing accuracy and reliability in the based data-driven fault diagnosis technical, this article use of existing measurement techniques and measurement tools for obtaining the mass process data. A deeply researching on data-driven multivariate statistical fault diagnosis carried out data-driven multivariate statistical fault diagnosis by using the appropriate combination of corresponding mathematical theory and engineering practice. The work is good for the further development of the present imperfect data-driven fault diagnosis the theoretical system and achieving data-driven fault diagnosis in the practice of good applications to provide a degree of technical support and the corresponding theoretical guidance. By using the various methods existing in the present data-driven fault diagnosis system and the proposed new ways in this article, the massive data of 6135D diesel engine under the leak fault is sampled and 6 data-driven fault diagnosis methods are respectively exploited in the data samples. Data experimental results show that the various studies carried out and some of the innovative achievements made in this article are significant in both theory and application. This major research work carried out by the following:
①Using the wavelet decomposition in various scales to extract detailed features of a noise signal containing the constant bias, graded, mutation and high frequency sinusoidal failure, detailed analyzing the multi-scale features of the dynamic non-stationary data and the fault characteristics distribution law of the data is obtained. Wavelet filtering for the edge effect appears in the result produced by the filtered signal glitches and spikes, with Toeplitz matrices and the Gram-Schmidt orthogonalization algorithm designed the edge of the orthogonal filter, thereby reducing the edge effect on the adverse impact of signal filtering.
②The relevant priciples of signal singularity detection are analyzed in this paper. In order to solve the problem that the wavelet-singular point detection is more sensitive to the noise and signal denoising will lose some improtant fault information, a new method of sigular point is proposed based the multiscale products of wavlet which can sharpen the important features of signal while weakening noise. Therefore, multiscale products can distinguish edge structures from noise more effectively.
③Aiming at multi-scaling and time-varying of the dynamic non-stationary process data the analysis on multi-scale characteristics of the signal is studied and the edge correcting filter is designed. Using online multi-scale filtering (OLMS) ideology, combined with the recursive principal component analysis (APCA) and multi-scale principal component analysis (MSPCA), multi-scale line filtering based on multivariate statistical fault diagnosis is proposed.
④Proposes an online moving window multi-scale principal component analysis(MW-MSPCA) datadrivenbased fault diagnosis method for tracking the non-stationary dynamics of the process which contains time-varyingand multi-scale data. In this data-driven diagnosis technique, wave threshold denoising is used to solve the conflict betweenthe statistical model deviation and data correlation decreasing;the statistical models is updated by using moving windowprincipal component analysis in various scales;the contributions of individual process variables to the process behaviorchanges is illustrated in a 3-dimensional contribution chart, which determines the root cause failure ;and a quantitative evaluation mechanism is also given to evaluating the accuracy in such algorithms.
⑤Select 6135D diesel engine as an object of study, to analyze the failure mechanism and the signal features at cylinder head vibration, making the measurement data under the engine failure in the leak as an experimental data sample, implementing applied research by employing respectively 6 kinds of algorithms, such as the traditional principal component analysis(PCA),based on-line multi-scale filtering multi-scale Principal Component analysis(OLMS-R-MSPCA), and the experimental results show that the proposed method is effective and feasible.
引文
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