【摘要】：在過程監(jiān)測中,隨著對(duì)系統(tǒng)穩(wěn)定性與安全性需求的增加,故障檢測與診斷也越發(fā)重要。及時(shí)、有效、準(zhǔn)確的檢出故障對(duì)工業(yè)過程的經(jīng)濟(jì)性與安全性都有非常大的意義,及時(shí)檢出故障,則有助于降低維修成本,并且避免長期故障引起的設(shè)備損壞,若不能及時(shí)檢出故障,不僅在經(jīng)濟(jì)方面造成損失,嚴(yán)重的甚至?xí){現(xiàn)場工作人員的生命安全。 作為一種基于數(shù)據(jù)驅(qū)動(dòng)的故障診斷方法主元分析分析方法,需要穩(wěn)態(tài)工況下的數(shù)據(jù)進(jìn)行建模。傳統(tǒng)主元分析方法,其T2統(tǒng)計(jì)量與SPE統(tǒng)計(jì)量控制限均為固定的,然而,在過渡工況下,使用固定的控制限,會(huì)引發(fā)大量的誤報(bào),這嚴(yán)重影響主元分析的檢測性能。同時(shí),從系統(tǒng)獲取的數(shù)據(jù)含有大量的噪聲,導(dǎo)致主元分析檢測性能下降。 本文首先介紹了故障診斷的基礎(chǔ)知識(shí)及故障分類、故障診斷方法及其分類,深入研究了主元分析的故障診斷機(jī)理及方法。針對(duì)主元分析在過渡工況中誤報(bào)率較高的問題,研究了T2統(tǒng)計(jì)量方差自適應(yīng)控制限。然而,采用方差自適應(yīng)控制限增加了故障的漏報(bào)率,因此,本文就T2統(tǒng)計(jì)量方差自適應(yīng)控制限與EWMA濾波展開研究,將方差自適應(yīng)控制限的主元分析方法首次用于火電廠故障診斷中,有效地降低了主元分析T2統(tǒng)計(jì)量在過渡工況中的誤報(bào)情況；并且方差自適應(yīng)控制限與EWMA濾波相結(jié)合,在不增加誤報(bào)的情況下有效地提高了主元分析檢出微小故障的能力。 主要研究工作如下： 1、對(duì)故障診斷基礎(chǔ)進(jìn)行調(diào)研,研究了故障的分類及故障診斷類型的分類。本文介紹了兩類故障診斷方法：1)基于模型的故障診斷方法；2)基于數(shù)據(jù)的故障診斷方法,并分析對(duì)比了其方法的優(yōu)缺點(diǎn)。 2、深入研究了經(jīng)典主元分析方法,為了解決經(jīng)典主元分析誤報(bào)的問題,研究了統(tǒng)計(jì)量用自適應(yīng)控制限的方法,并且深入研究了主元分析與EWMA濾波相結(jié)合的方法,提出方差自適應(yīng)控制限與EWMA相結(jié)合的故障診斷方法,在降低誤報(bào)率的同時(shí),提高了主元分析檢出微小故障的性能。 3、采用數(shù)值仿真實(shí)驗(yàn)驗(yàn)證方差自適應(yīng)控制限與EWMA相結(jié)合的方式可以有效降低誤報(bào),同時(shí)提高了主元分析檢出微小故障的性能,并且將這種方法用于火電廠燃燒故障診斷過程中。
[Abstract]:In process monitoring, with the increasing demand for system stability and security, fault detection and diagnosis become more and more important. Timely, effective and accurate detection of faults is of great significance to the economy and safety of industrial processes. Timely detection of faults will help to reduce maintenance costs and avoid equipment damage caused by long-term failures. If failure is detected in time, it will not only cause economic losses, but also threaten the safety of workers. As a data-driven principal component analysis method for fault diagnosis, it is necessary to model the data under steady condition. The control limits of T2 and SPE statistics of traditional principal component analysis methods are fixed. However, under transient conditions, the use of fixed control limits will lead to a large number of false positives, which seriously affect the detection performance of principal component analysis. At the same time, the data obtained from the system contain a lot of noise, which leads to the degradation of the performance of principal component analysis (PCA) detection. In this paper, the basic knowledge and classification of fault diagnosis, the method and classification of fault diagnosis are introduced, and the principle and method of fault diagnosis based on principal component analysis (PCA) are studied. Aiming at the problem of high false alarm rate of PCA in transient condition, the adaptive control limit of variance of T2 statistic is studied. However, the error rate is increased by using variance adaptive control limit. Therefore, the T2 statistic variance adaptive control limit and EWMA filter are studied in this paper. The principal component analysis method of variance adaptive control limit is applied to fault diagnosis of thermal power plant for the first time, which effectively reduces the misinformation of T2 statistic in transient condition. And the combination of variance adaptive control limit and EWMA filter can effectively improve the ability of principal component analysis (PCA) to detect small faults without adding false positives. The main research work is as follows: 1. The basic of fault diagnosis is investigated, and the classification of fault diagnosis and fault diagnosis is studied. This paper introduces two kinds of fault diagnosis methods: (1) model-based fault diagnosis and (2) data-based fault diagnosis, and analyzes and compares their advantages and disadvantages. 2. The classical principal component analysis method is deeply studied. In order to solve the problem of false positives in classical principal component analysis, the method of adaptive control limit is studied, and the method of combining principal component analysis with EWMA filter is studied. A fault diagnosis method based on variance adaptive control limit and EWMA is proposed, which can reduce the false alarm rate and improve the performance of principal component analysis (PCA) to detect small faults. 3. Numerical simulation experiments show that the combination of variance adaptive control limit and EWMA can effectively reduce false positives and improve the performance of principal component analysis (PCA) to detect small faults. And this method is used in the process of combustion fault diagnosis in thermal power plant.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM62

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