【摘要】：安全是軌道交通永恒的主題,尤其是在軌道交通事業(yè)集中建設(shè)和高速發(fā)展的全盛時期,安全問題更是全社會關(guān)注的焦點。軌道交通列車的正常服役是保障軌道交通系統(tǒng)安全高效運營的必要條件,而軌道交通列車能否正常運行直接取決于其中的運行安全關(guān)鍵設(shè)備的服役狀態(tài)。但是,我國現(xiàn)有的軌道交通列車及其運行安全關(guān)鍵裝備在線服役狀態(tài)的辨識、預(yù)測、診斷和控制技術(shù)和手段遠遠不能滿足軌道交通系統(tǒng)動態(tài)化系統(tǒng)化的主動安全保障需求。針對這一重大問題,急需研究并提出系統(tǒng)化的列車運行安全關(guān)鍵設(shè)備服役狀態(tài)辨識和預(yù)測的方法。鑒于此,本文在進行了如下研究工作： 1.在了解分析列車關(guān)鍵設(shè)備狀態(tài)監(jiān)測、安全域估計理論和方法等相關(guān)的國內(nèi)外研究現(xiàn)狀的基礎(chǔ)上,參考借鑒相關(guān)領(lǐng)域的已有成果,系統(tǒng)地提出了基于安全域理論的列車運行安全關(guān)鍵設(shè)備服役狀態(tài)辨識方法。闡述了安全域的基本概念和內(nèi)涵,說明了基于安全域估計進行狀態(tài)辨識的基本原理；提出了基于安全域估計理論的狀態(tài)辨識方法框架,給出了方法實施的通用步驟,并針對方法實現(xiàn)中的邊界估計這一關(guān)鍵技術(shù)問題,根據(jù)研究對象是否便于建立數(shù)據(jù)模型提出了兩條并行的技術(shù)路線：基于模型的邊界估計技術(shù)和數(shù)據(jù)驅(qū)動的邊界估計技術(shù)；對于本文使用的數(shù)據(jù)驅(qū)動的邊界估計技術(shù)實現(xiàn),提出了采用支持向量機的方法,并根據(jù)狀態(tài)辨識需求給出了二分類的和多分類的支持向量機算法。 2.在基于安全域估計理論的狀態(tài)辨識方法框架的基礎(chǔ)上,提出了一種面向?qū)崟r狀態(tài)特征的安全域狀態(tài)辨識方法。該方法在狀態(tài)特征提取方面,首先采用了較新穎的局部均值分解方法將數(shù)字信號分解為多個分量,然后計算了信號分量的直接時域特征以及基于能量和熵的兩類實時狀態(tài)特征指標；以列車滾動軸承作為實例,分別利用不同工況環(huán)境下的數(shù)據(jù)對算法的辨識精度、魯棒性和實時性進行了全面測試,實驗結(jié)果表明基于實時狀態(tài)特征的狀態(tài)辨識方法的計算效率很高,而辨識精度和抗干擾性能方面表現(xiàn)一般。 3.從基于數(shù)據(jù)的統(tǒng)計分布特性提取狀態(tài)特征方面考慮,提出了面向統(tǒng)計狀態(tài)特征的安全域狀態(tài)辨識方法。首先清晰地給出了基于統(tǒng)計狀態(tài)特征提取和支持向量機的狀態(tài)辨識方法,詳細敘述了其實現(xiàn)步驟；然后,針對方法步驟中的統(tǒng)計狀態(tài)特征提取問題,細致地闡述了基于主成分分析的統(tǒng)計狀態(tài)特征提取方法；仍以列車滾動軸承作為實例,通過不同工況環(huán)境下的多組實驗仿真,測試了方法的辨識精度、魯棒性和實時性,實驗結(jié)果表明基于統(tǒng)計狀態(tài)特征的狀態(tài)辨識方法具有極高的辨識精度和優(yōu)越的魯棒性,但該方法計算負擔(dān)大執(zhí)行效率低,實時性方面表現(xiàn)一般；最后簡要介紹了作者參與的國家863重點項目中關(guān)于安全域狀態(tài)辨識方法的現(xiàn)場應(yīng)用系統(tǒng)的設(shè)計工作。 4.基于列車運行安全關(guān)鍵設(shè)備服役狀態(tài)辨識方法的研究結(jié)果,進一步對基于狀態(tài)監(jiān)測的剩余壽命預(yù)測問題進行了探討。敘述了幾類常用的基于狀態(tài)監(jiān)測的剩余壽命預(yù)測方法,給出了各類方法的基本原理、優(yōu)缺點和適應(yīng)環(huán)境；詳細討論了能夠同時融合可靠性信息和狀態(tài)監(jiān)測信息的比例風(fēng)險模型,對基于比例風(fēng)險模型的剩余壽命預(yù)測方法進行了詳細闡述；基于滾動軸承的全壽命振動數(shù)據(jù),進行了試驗仿真,結(jié)果表明,相對于僅依靠狀態(tài)監(jiān)測信息的剩余壽命預(yù)測方法來說,本文提出的基于統(tǒng)計狀態(tài)特征的比例風(fēng)險模型能夠精確地預(yù)測設(shè)備的剩余壽命。
[Abstract]:Safety is the eternal theme of rail transit, especially in the heyday of centralized construction and rapid development of rail transit. Safety is the focus of attention of the whole society. The normal service of rail transit trains is the necessary condition to ensure the safe and efficient operation of rail transit system, and the normal operation of rail transit trains is directly decided by the normal operation of rail transit trains. However, the existing on-line identification, prediction, diagnosis and control technologies and means of rail transit trains and their operational safety critical equipment in China are far from meeting the requirements of active safety assurance for the dynamic and systematic rail transit system. It is necessary to study and put forward a systematic method for identifying and predicting the service state of key equipment for train operation safety.
1. On the basis of understanding and analyzing the status monitoring of train key equipments, the theory and method of safety domain estimation, and referring to the achievements in related fields, a method of identifying the service status of train key equipments based on safety domain theory is proposed systematically. The basic principle of state identification based on security domain estimation is explained, the framework of state identification method based on security domain estimation theory is proposed, and the general steps of implementation of the method are given. There are two parallel technical routes: model-based boundary estimation and data-driven boundary estimation. For the implementation of data-driven boundary estimation, a support vector machine (SVM) method is proposed, and two-class and multi-class SVM algorithms are given according to the requirements of state identification.
2. Based on the framework of the state identification method based on the theory of security domain estimation, a real-time state feature-oriented security domain state identification method is proposed. Direct time domain feature and two kinds of real-time state feature indexes based on energy and entropy are used to test the identification accuracy, robustness and real-time performance of the algorithm using the data of different working conditions respectively. The experimental results show that the state identification method based on real-time state feature is effective. The rate is very high, but the identification accuracy and anti-jamming performance are general.
3. Considering the extraction of state features based on statistical distribution characteristics of data, a new method of state identification in security region for statistical state features is proposed. The problem of state feature extraction is discussed in detail, and the statistical state feature extraction method based on principal component analysis (PCA) is elaborated. Taking train rolling bearing as an example, the identification accuracy, robustness and real-time performance of the method are tested through a series of experimental simulations under different working conditions. The experimental results show that the state identification method based on statistical state feature is effective. The method has high identification accuracy and superior robustness, but its computational burden is heavy and its execution efficiency is low, and its real-time performance is general.
4. Based on the research results of the identification method of the service state of the key equipment for train operation safety, the problem of residual life prediction based on condition monitoring is further discussed. Proportional risk model which can fuse reliability information and condition monitoring information at the same time is proposed, and residual life prediction method based on proportional risk model is expounded in detail; full-life vibration data of rolling bearing is tested and simulated, and the results show that the residual life prediction method based on condition monitoring information is better than that based on condition monitoring information only. For example, the proposed proportional hazard model based on statistical state features can accurately predict the residual life of equipment.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：U298.1

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