發(fā)布時(shí)間：2018-01-16 20:42

  本文關(guān)鍵詞：基于PCA的流程工業(yè)故障診斷方法研究　出處：《西南科技大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 故障檢測(cè) 主元分析 局部保持投影 多模態(tài) 局部近鄰標(biāo)準(zhǔn)化

【摘要】：故障診斷對(duì)保證流程工業(yè)生產(chǎn)安全、提高產(chǎn)品質(zhì)量有著重要作用。本文針對(duì)實(shí)際流程工業(yè)過程中存在的數(shù)據(jù)信息提取、多模態(tài)、數(shù)據(jù)分布復(fù)雜等問題,對(duì)流程工業(yè)故障診斷方法進(jìn)行研究。具體包括:(1)針對(duì)傳統(tǒng)主元分析方法在數(shù)據(jù)降維中僅考慮數(shù)據(jù)全局結(jié)構(gòu)的問題,采用一種局部整體結(jié)構(gòu)保持投影(LGSPP)算法,使投影后的低維空間不僅和原始空間有相似的整體結(jié)構(gòu),而且保留了相似的局部結(jié)構(gòu)。將高維數(shù)據(jù)降維到低維空間后,構(gòu)造統(tǒng)計(jì)量和貝葉斯分類器對(duì)故障進(jìn)行檢測(cè)和辨識(shí);考慮到數(shù)據(jù)中存在的動(dòng)態(tài)問題,構(gòu)造含有前h個(gè)觀測(cè)的增廣矩陣,將其應(yīng)用到LGSPP算法中,改善了動(dòng)態(tài)工業(yè)過程的故障檢測(cè)效果。TE過程的仿真結(jié)果表明了算法的有效性。(2)從數(shù)據(jù)預(yù)處理的角度改善工業(yè)過程數(shù)據(jù)的多模態(tài)問題,在對(duì)數(shù)據(jù)進(jìn)行標(biāo)準(zhǔn)化處理時(shí)引入局部近鄰標(biāo)準(zhǔn)化策略,得到LNS-LGSPP算法。采用LNS對(duì)數(shù)據(jù)進(jìn)行標(biāo)準(zhǔn)化處理,去除不同變量量綱的影響以及多模態(tài)數(shù)據(jù)的多分布特征,將處理后的數(shù)據(jù)應(yīng)用到LGSPP算法中進(jìn)行故障檢測(cè)。數(shù)值仿真和TE過程的仿真結(jié)果表明了該算法在多模態(tài)過程監(jiān)控中的有效性。
[Abstract]:Fault diagnosis plays an important role in ensuring the production safety of process industry and improving product quality. This paper aims at the problems of data information extraction, multi-modal and complex data distribution in the actual process industry. This paper studies the fault diagnosis method of process industry, including: 1) the traditional principal component analysis method only considers the global structure of data in data dimensionality reduction. A local global structure preserving projection (LGSPP) algorithm is used to make the projected low-dimensional space not only have a global structure similar to the original space. After reducing the dimension of high-dimensional data to low-dimensional space, we construct statistics and Bayesian classifier to detect and identify faults. Considering the dynamic problem in the data, the augmented matrix with the first h observations is constructed and applied to the LGSPP algorithm. The simulation results show that the algorithm is effective and can improve the multi-modal problem of industrial process data from the point of view of data preprocessing. When the data is standardized, the local nearest neighbor standardization strategy is introduced, and the LNS-LGSPP algorithm is obtained. The LNS is used to standardize the data. The influence of different variables dimension and the multi-distribution characteristics of multi-modal data are removed. The processed data are applied to the LGSPP algorithm for fault detection. The numerical simulation and the simulation results of te process show that the algorithm is effective in multi-modal process monitoring.
【學(xué)位授予單位】：西南科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP277
，

本文編號(hào)：1434771