摘要
系统长时间高负荷持续运转、复杂的环境及许多无法预料的原因,使得对象常会出现各种类型的故障。同时,随着大型自动化系统的功能日渐完善化、结构日趋复杂化,对其建立较为精确的模型也变得越来越困难。为此,本文以提高对系统监控性能为目的,以能获得的海量数据为基础,利用相应的数学理论并结合工程实际,建立了几种新的故障检测与诊断方法。进一步发展和完善了基于数据驱动的异常监控理论体系,为其在实际中的应用打下了更加扎实的理论基础。本文开展的研究工作及所取得的创新性成果,不仅具有广泛应用前景,而且有重要的科学意义。
论文以多变量统计特征提取为主线,利用小波分析、空间投影、矩阵谱分解、相关分析等数学理论或方法,开展基于数据驱动的多尺度故障检测、知识导引的数据驱动多故障诊断和故障传播等内容的研究。主要研究工作如下:
1.基于矩阵的谱分解、信号的多尺度变换及谱的多尺度表示,提出一种拟多尺度主元分析方法,从机理上分析了多尺度检测方法优于传统单尺度检测方法的内在原因。并建立了一种拟多尺度相对主元分析异常检测算法。
2.针对主元分析所呈现的模式复合问题,引入指定元分析(DCA)方法,建立了DCA的空间投影框架,完善和发展了DCA理论。提出了一种逐步DCA分析算法,并开展了其在指定模式非正交情况下的多故障诊断问题研究。
3.针对微小故障诊断、未知类型故障诊断问题,分别建立了基于DCA的多级微小故障诊断算法和扩展DCA未知故障诊断算法。
4.提出了一种基于DCA的故障传播关系分析方法。通过输入/输出指定元间的相关性分析,确定故障传播关系矩阵用以追溯故障根源。建立了输入/输出指定元回归模型,实现对输出子系统故障影响强度的预测。
5.开展了上述部分方法在船舶主柴油机故障检测与诊断中的应用研究。
Due to many reasons of system operation condition, large scale system is always bothered by different kind of faults. It is highly required to improve the performance of abnormal monitoring, which can maintain the safe and economy operation of automatic system. Since large scale systems are becoming more complicated and more automatic, it is difficult to establish an accurate physical model that is useful for abnormal monitoring. Using huge amount of data that can be obtained by DCS, this thesis focuses on establishing some new fault detection and diagnosis methods. Some theory research is carried out in the first to provide theory foundation for data-driven abnormal monitoring. The main innovations of this thesis are significant in both theory and application.
Involving around multivariate statistical information extraction, some mathematical tools, such as wavelet analysis, space projection theory, spectral decomposition of matrix, correlation analysis etc., are used to develop the research on 3 aspects: multi-scale abnormal detection, knowledge guided data driven fault diagnosis and fault propagation. The main contribution of this paper follows as:
1. Using spectral decomposition of a matrix and multi-scale representation of spectral as well as multi-scale transform of a signal, a quasi multi-scale PCA (MSPCA) method is proposed to analyze the reason why multi-scale detection method does well than single scale method. Combing with relative PCA, a quasi MSRPCA algorithm is also developed for abnormal detection.
2. Designated component analysis (DCA) is introduced to solve the pattern compounding problem of PCA. A DCA projection frame, which is the theory foundation for DCA based multiple fault diagnosis, is established in the first. For the case when designated pattern are not orthogonal to each other, a progressive DCA analysis method is developed for the generally application of DCA based multiple faults diagnosis.
3. A multi-level small faults diagnosis method under the DCA projection frame is established for small fault diagnosis. In addition, an extended DCA algorithm is developed to avoid the shortage that DCA is only validated for faults defined in advance.
4. A DCA based fault propagation analysis method is proposed. It is proved in the thesis that correlation between Input/Output designated component(DC) can be used to guide the determination of fault propagation matrix. In addition, a DC regress model is established to predict the imperil level of the root fault.
5. Some of the above mentioned methods are used for fault detection and diagnosis of an ocean ship's main diesel engine.
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