本文選題：故障時(shí)間定位　切入點(diǎn)：卡爾曼濾波　出處：《重慶理工大學(xué)》2015年碩士論文

【摘要】：橋梁結(jié)構(gòu)安全問題一直都被學(xué)術(shù)界和工程界高度重視,系統(tǒng)故障與傳感器故障的區(qū)分成為了近些年來(lái)研究熱點(diǎn)。傳統(tǒng)的傳感器故障診斷多是針對(duì)系統(tǒng)中的傳感器做故障空間定位,然而對(duì)于橋梁大型監(jiān)測(cè)系統(tǒng),傳感器故障發(fā)生到故障解決往往需要相當(dāng)長(zhǎng)的一段時(shí)間,為保證系統(tǒng)在此期間的正常運(yùn)作,對(duì)傳感器做故障時(shí)間定位以及故障數(shù)據(jù)重構(gòu)顯得意義深遠(yuǎn)。本文主要針對(duì)橋梁結(jié)構(gòu)健康監(jiān)測(cè)系統(tǒng)中的光電液位撓度傳感器,完成以下幾項(xiàng)研究:(1)卡爾曼濾波是目前應(yīng)用最廣泛的最優(yōu)估計(jì)理論,本文將卡爾曼濾波應(yīng)用到數(shù)據(jù)預(yù)處理,有效的抑制了數(shù)據(jù)中的噪聲誤差,分析表明,濾波之后的數(shù)據(jù)相關(guān)性增強(qiáng),這給后續(xù)研究高效精準(zhǔn)的故障定位方法提供了較好的支持。同時(shí)卡爾曼濾波是單步遞推估計(jì),這也為實(shí)時(shí)在線的高精度故障診斷提供了可能。(2)提出了基于滑動(dòng)時(shí)間窗相關(guān)性分析的故障時(shí)間定位方法。方法依據(jù)組內(nèi)傳感器之間較強(qiáng)的相關(guān)性,采用改進(jìn)的相關(guān)度模型,基于滑動(dòng)時(shí)間窗做相關(guān)性分析,以相關(guān)度量化值對(duì)故障進(jìn)行判定,從而對(duì)故障進(jìn)行時(shí)間定位分析。提出了基于數(shù)據(jù)標(biāo)準(zhǔn)化殘差分析的故障時(shí)間定位方法。方法依據(jù)組內(nèi)傳感器之間只存在幅值上的顯著差異,變化趨勢(shì)高度一致,基于數(shù)據(jù)標(biāo)準(zhǔn)化作殘差分析,以殘差偏離量化值對(duì)故障進(jìn)行判定,從而對(duì)故障進(jìn)行時(shí)間定位。運(yùn)用兩種時(shí)間定位方法,分別對(duì)工程中常見的四種故障類型做仿真模擬,分析兩種定位方法的有效性與精確度。實(shí)驗(yàn)驗(yàn)證,相關(guān)法對(duì)精度下降故障表現(xiàn)出明顯的優(yōu)勢(shì),而殘差法對(duì)常值故障、固定偏差、漂移故障的定位性能均優(yōu)于相關(guān)法。兩種方法結(jié)合使用,可達(dá)到更好的故障時(shí)間定位效果。(3)提出了自適應(yīng)殘差法的故障數(shù)據(jù)重構(gòu)方法。方法依據(jù)傳感器數(shù)據(jù)之間保持一致的變化趨勢(shì),基于標(biāo)準(zhǔn)化的殘差具有趨0性,以殘差值最小為目標(biāo)估計(jì)故障傳感器數(shù)據(jù),實(shí)現(xiàn)故障數(shù)據(jù)重構(gòu)。與經(jīng)典的數(shù)據(jù)重構(gòu)方法——RBF神經(jīng)網(wǎng)絡(luò)、多元回歸分析、最小二乘法,對(duì)重構(gòu)效果作對(duì)比分析,并對(duì)重構(gòu)殘差作量化對(duì)比分析。實(shí)驗(yàn)驗(yàn)證,針對(duì)本研究,本文所提的自適應(yīng)殘差的重構(gòu)效果是最優(yōu)的。
[Abstract]:The safety of bridge structure has always been attached great importance by the academic and engineering circles. The distinction between system fault and sensor fault has become a hot topic in recent years. The traditional sensor fault diagnosis is mostly aimed at the sensor in the system fault space location, but for the bridge large-scale monitoring system, Sensor failures often take quite a long time to resolve, and in order to ensure the normal operation of the system during this period, It is of great significance to locate the fault time and reconstruct the fault data for the sensor. This paper mainly focuses on the photoelectric liquid level deflection sensor in the bridge structure health monitoring system. Kalman filtering is the most widely used optimal estimation theory at present. In this paper, Kalman filter is applied to data preprocessing, which can effectively suppress the noise error in the data. The correlation of the data after filtering is enhanced, which provides a good support for the subsequent research on the efficient and accurate fault location method. At the same time, the Kalman filter is a single step recursive estimation. It also provides the possibility for real-time on-line high precision fault diagnosis. A fault time location method based on sliding time window correlation analysis is proposed. According to the strong correlation between sensors in the group, an improved correlation model is adopted. Based on the sliding time window, the correlation analysis is done, and the quantitative value of correlation degree is used to judge the fault. A fault time location method based on data standardized residuals analysis is put forward. The method is based on the significant difference in amplitude between sensors in the group, and the variation trend is highly consistent. Based on the data standardization, the residual error is analyzed, and the fault is determined by the quantization value of the residual deviation, and then the fault is located in time. The four common fault types in engineering are simulated by using two time localization methods, respectively. The effectiveness and accuracy of the two localization methods are analyzed. The experimental results show that the correlation method has obvious advantages on the precision decline fault, while the residual method has fixed deviation for the constant fault. The performance of drift fault location is better than that of correlation method. A fault data reconstruction method based on adaptive residuals method is proposed. The method is based on the uniform change trend of sensor data, and the standardized residual error has a tendency to zero. Taking the minimum residual value as the target to estimate the fault sensor data, the reconstruction of the fault data is realized, which is compared with the classical data reconstruction methods, such as RBF neural network, multivariate regression analysis and least square method. The experimental results show that the adaptive residuals proposed in this paper are the best.
【學(xué)位授予單位】：重慶理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U446

【引證文獻(xiàn)】

相關(guān)會(huì)議論文 前1條

1 李亞楠;段立;顧方勇;;基于支持向量機(jī)的傳感器故障診斷研究[A];艦船電子裝備維修理論與應(yīng)用——中國(guó)造船工程學(xué)會(huì)電子修理學(xué)組第四屆年會(huì)暨信息裝備保障研討會(huì)論文集[C];2005年

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本文編號(hào)：1682281