【摘要】：作為Dempster-Shafer(DS)證據(jù)理論的一種重要分支,近年來(lái)被提出的證據(jù)推理(Evidence Reasoning,ER)規(guī)則及其融合方法中,定義了證據(jù)可靠性和重要性的概念并明確了兩者的區(qū)別,這對(duì)于證據(jù)的獲取及性能評(píng)價(jià)十分重要。此外,基于正交和定理給出的證據(jù)推理規(guī)則,相比于傳統(tǒng)的Dempster證據(jù)組合規(guī)則,提供了一種更為嚴(yán)密的概率推理過(guò)程,其重新詮釋了貝葉斯推理在辨識(shí)框架密集上的推廣。本文開(kāi)展基于證據(jù)推理的分類決策故障診斷方法研究,主要工作包括:(1)基于證據(jù)推理的電機(jī)轉(zhuǎn)子系統(tǒng)故障診斷方法。首先,利用似然函數(shù)歸一化的方法,從故障樣本變化區(qū)間的投點(diǎn)結(jié)果中獲得各故障特征的診斷證據(jù);結(jié)合傳感器本身固有誤差以及各樣本區(qū)間的診斷證據(jù)對(duì)各故障模式的診斷能力,計(jì)算診斷證據(jù)的可靠性因子;構(gòu)建基于歐式距離度量的雙目標(biāo)優(yōu)化模型,獲得診斷證據(jù)的最優(yōu)權(quán)重值;利用ER融合規(guī)則合并考慮了可靠性和權(quán)重的診斷證據(jù),并根據(jù)融合結(jié)果進(jìn)行診斷決策。最后,在電機(jī)轉(zhuǎn)子系統(tǒng)故障診斷實(shí)驗(yàn)中,該方法表現(xiàn)出了較好的診斷性能。(2)基于證據(jù)推理的軌道高低不平順故障診斷方法。由軌道高低不平順引起的異常振動(dòng)輕則引起行車質(zhì)量下降,重則導(dǎo)致列車脫軌。利用有效的狀態(tài)監(jiān)測(cè)方法對(duì)軌道高低不平順故障進(jìn)行檢測(cè)和診斷,是保證列車行車質(zhì)量和行車穩(wěn)定性的前提。為此,建立了一種基于ER規(guī)則的推理模型,通過(guò)融合車載傳感器采集的加速度數(shù)據(jù)推理出軌道高低不平順?lè)档墓烙?jì)值。通過(guò)與經(jīng)典神經(jīng)網(wǎng)絡(luò)方法在完備與不完備測(cè)量數(shù)據(jù)環(huán)境下的典型實(shí)驗(yàn)對(duì)比,驗(yàn)證了方法的有效性。(3)基于證據(jù)推理的廣義分類器設(shè)計(jì)方法。經(jīng)上述面向設(shè)備故障診斷應(yīng)用的ER規(guī)則推理方法研究,可知故障診斷本質(zhì)上是一種基于多源屬性信息的分類決策問(wèn)題。因此,進(jìn)一步提出基于證據(jù)推理的廣義分類器設(shè)計(jì)方法,以期將ER規(guī)則推廣到解決一般意義上的分類問(wèn)題。首先從某屬性訓(xùn)練數(shù)據(jù)中獲得證據(jù);根據(jù)屬性的分類能力確定屬性及其證據(jù)的可靠性,并基于初始參數(shù)構(gòu)建分類器;基于序列線性規(guī)劃(SLP)訓(xùn)練分類器的參數(shù);根據(jù)各屬性證據(jù)融合結(jié)果進(jìn)行分類決策。最后,選擇5種國(guó)際上通用的基準(zhǔn)數(shù)據(jù)集,將ER分類器與其它6種經(jīng)典分類器方法進(jìn)行對(duì)比試驗(yàn),從而說(shuō)明ER分類器的有效性和普適性。
[Abstract]:As an important branch of Dempster-Shafer (DS) evidence theory, in recent years, Evidence Reasoning,ER rules and their fusion methods have defined the concepts of reliability and importance of evidence and made clear the difference between them. This is important for obtaining evidence and evaluating performance. In addition, the rule of evidence reasoning based on orthogonal sum theorem provides a more rigorous process of probabilistic reasoning than the traditional Dempster rule of evidence combination, which reinterprets the generalization of Bayesian reasoning in the dense identification framework. In this paper, the classification decision fault diagnosis method based on evidence reasoning is studied. The main work includes: (1) the fault diagnosis method of motor rotor system based on evidence reasoning. Firstly, using the method of likelihood function normalization, the diagnosis evidence of each fault feature is obtained from the result of the fault sample variation interval. The reliability factor of diagnosis evidence is calculated by combining the inherent error of sensor itself and the ability of diagnosis evidence of each sample interval to diagnose each fault mode. A two-objective optimization model based on Euclidean distance metric is constructed to obtain the optimal weight of diagnostic evidence. The ER fusion rule is used to merge the diagnostic evidence considering reliability and weight and then the diagnosis decision is made according to the fusion results. Finally, in the fault diagnosis experiment of motor rotor system, this method shows good diagnosis performance. (2) the track irregularity fault diagnosis method based on evidential reasoning. The abnormal vibration caused by the irregularity of the track leads to the decrease of the driving quality and the derailment of the train. It is a prerequisite to ensure the quality and stability of train operation to detect and diagnose track irregularity by using effective state monitoring method. For this reason, a reasoning model based on ER rule is established, and the estimated value of track irregularity amplitude is deduced by combining acceleration data collected by vehicle sensor. The effectiveness of the proposed method is verified by comparison with the classical neural network method in complete and incomplete measurement data environments. (3) the design method of generalized classifier based on evidential reasoning. Based on the research of ER rule reasoning method for equipment fault diagnosis, it can be concluded that fault diagnosis is essentially a classification decision problem based on multi-source attribute information. Therefore, a design method of generalized classifier based on evidential reasoning is proposed in order to extend the ER rule to solve the general classification problem. Firstly, the evidence is obtained from some attribute training data, the attribute and the reliability of the evidence are determined according to the classification ability of the attribute, and the classifier is constructed based on the initial parameters, and the parameters of the classifier are trained based on the sequential linear programming (SLP). The classification decision is made according to the fusion results of each attribute evidence. Finally, five kinds of international datum data sets are selected, and the ER classifier is compared with other six classical classifier methods to demonstrate the effectiveness and universality of the ER classifier.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP202;TP277

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