本文選題：局域波分解 + 粗糙集��； 參考：《中北大學(xué)》2013年博士論文

【摘要】：齒輪箱是機械系統(tǒng)的重要傳動部件,故障發(fā)生率較高,其振動信號呈現(xiàn)非線性非平穩(wěn)的特點,故障程度、部位和類型等對特征參量的影響很大。在對齒輪箱進行監(jiān)測與故障診斷時,若監(jiān)測點選擇不當(dāng)就不能采集到有效的故障信息,從而導(dǎo)致故障發(fā)生部位不易確定,敏感特征參量提取困難、故障模式識別率低。局域波分解方法將非平穩(wěn)時變信號自適應(yīng)地分解展開并映射到時頻分析平面,能夠同時展示信號的時域和頻域信息的全貌；粗糙集理論的屬性約簡技術(shù)能夠優(yōu)化故障特征參量集,提取出敏感的故障特征參量；最小二乘支持向量機的函數(shù)逼近效果良好,模式識別能力強,本文在采用局域波分解法處理故障信號以及深入研究粗糙集理論的基礎(chǔ)上,將粗糙集與最小二乘支持向量機相結(jié)合,建立了基于粗糙集支持向量機的齒輪箱智能故障診斷系統(tǒng)。本文的主要研究內(nèi)容與結(jié)論如下： (1)在分析齒輪箱振動特性的基礎(chǔ)上,提出采用局域波分解技術(shù)對齒輪箱故障信號進行處理并提取了初始的故障特征參量集。在局域波分解過程中,采用鏡像延拓與窗函數(shù)相結(jié)合的方法緩解了端點效應(yīng)問題,采用總體經(jīng)驗?zāi)B(tài)分解方法有效解決了模態(tài)混疊問題,實驗結(jié)果表明這兩種方法在齒輪箱故障信號分解中取得了較好的效果。根據(jù)衡量故障特征參量集的指標(biāo),提出采用每個工況的均方根有效值衡量故障特征參量集的穩(wěn)定性,采用每個特征參量在六個工況之間的最小均值差衡量故障特征參量集的敏感性。實驗中分別提取了基于EEMD的歸一化能量特征參量集和基于EEMD的近似熵特征參量集,通過實際計算結(jié)果表明,前者與后者的敏感性基本一致,但是穩(wěn)定性要優(yōu)于后者,因此本文采用了基于EEMD的歸一化能量特征參量集進行齒輪箱故障診斷。 (2)提出一種基于改進Naive Scaler算法的全局動態(tài)尋優(yōu)離散化算法。通過對Naive Scaler算法過程進行改進,確保能夠得到所有保證不可分辨關(guān)系的斷點；通過斷點均分樣本集、逐漸增加斷點的方法動態(tài)地從候選集中選擇斷點集,保證了整個信息系統(tǒng)分類能力不變的條件下斷點個數(shù)最少。通過與其它算法對比,實驗結(jié)果表明該算法得到的斷點個數(shù)較少,體現(xiàn)了其在連續(xù)屬性離散化方面的優(yōu)越性。 (3)提出一種基于條件等價類的屬性約簡算法。該算法在核屬性集的基礎(chǔ)上,直接針對核屬性的條件類中不能正確劃入決策類的類,在核屬性之外的其余條件屬性中找到能夠區(qū)分該類的屬性,并添加到核屬性集中,從而得到最小屬性約簡集。而基于啟發(fā)式信息的屬性約簡算法無法保證所求約簡集一定是最小屬性約簡集,實驗結(jié)果表明該算法計算復(fù)雜度較低,提高了約簡效率。 (4)提出采用粗糙集的屬性約簡技術(shù)對故障監(jiān)測點進行優(yōu)化配置。該方法將六個故障監(jiān)測點的最小屬性約簡集融合成一個大決策表進行屬性約簡,根據(jù)每個監(jiān)測點的故障特征參量在最終約簡集中出現(xiàn)的頻次判定相應(yīng)監(jiān)測點的分類能力,實驗結(jié)果表明該方法不需要對監(jiān)測對象建模,也不需要對其進行動力學(xué)分析,而是直接對監(jiān)測到的振動信號進行處理,根據(jù)各個測點的故障特征參量與故障種類之間的關(guān)聯(lián)程度選擇最佳測點,是一種行之有效的測點優(yōu)化配置方法。 (5)基于粗糙集理論提取決策規(guī)則的過程沒有學(xué)習(xí)歸納的能力,故障模式識別率較低。粗糙集理論的屬性約簡技術(shù)能夠提取敏感的故障特征參量,最小二乘支持向量機的模式識別能力強,因此為了充分利用兩者在特征參量提取與模式識別方面的優(yōu)勢,構(gòu)建了基于粗糙集支持向量機的智能故障診斷系統(tǒng)。理論與實踐都表明該系統(tǒng)在一定程度上提高了齒輪箱故障診斷性能,為非線性非平穩(wěn)故障信號的處理與識別提供了一種較通用的解決方案。
[Abstract]:The gearbox is an important transmission part of the mechanical system, the failure rate is high, the vibration signal is nonlinear and non-stationary, the degree of fault, the position and type of the gear box have great influence on the characteristic parameters. When the gear box is monitored and fault diagnosis, the effective fault information can not be collected if the monitoring points are chosen unproperly, thus guiding the gear box. The fault location is not easy to be determined, the extraction of sensitive characteristic parameters is difficult and the fault pattern recognition rate is low. The local domain wave decomposition method adaptively decomposes and maps the nonstationary time-varying signal to the time frequency analysis plane, and can simultaneously display the full features of the time and frequency domain information of the signal, and the attribute reduction technique of rough set theory can be optimized. The fault feature parameter set is extracted and the sensitive fault feature parameters are extracted. The function approximation of the least squares support vector machine is good, and the pattern recognition ability is strong. Based on the local wave decomposition method to deal with the fault signal and the rough set theory, the rough set is combined with the least square support vector machine to establish the base. Gearbox intelligent fault diagnosis system based on rough set support vector machine. The main research contents and conclusions in this paper are as follows:
(1) on the basis of analyzing the vibration characteristics of the gearbox, the local wave decomposition technique is used to deal with the gear box fault signal and extract the initial fault feature parameter set. In the local wave decomposition process, the end point effect problem is alleviated by the combination of the mirror extension and the window function, and the general empirical mode decomposition method is adopted. The model aliasing problem is effectively solved. The experimental results show that the two methods have achieved good results in the breakdown signal decomposition of the gearbox. According to the index of the fault characteristic parameter set, the stability of the fault characteristic parameter set is measured by the mean square root of each working condition, and each characteristic parameter is used between the six working conditions. The minimum mean difference is used to measure the sensitivity of the fault characteristic parameter set. In the experiment, the normalized energy characteristic parameter set based on EEMD and the approximate entropy characteristic parameter set based on EEMD are extracted respectively. The results show that the former is basically the same as the latter, but the stability is better than the latter, so this paper uses the EEMD The normalized energy characteristic parameter set is used for gearbox fault diagnosis.
(2) a global dynamic optimization discretization algorithm based on the improved Naive Scaler algorithm is proposed. By improving the Naive Scaler algorithm process, we ensure that all the breakpoints that guarantee the unresolved relation can be obtained; the breakpoint is gradually increased by dividing the sample set by the breakpoint, and the breakpoint set is dynamically selected from the candidate set. The number of breakpoints is the least under the condition that the ability of information system classification is constant. By comparing with other algorithms, the experimental results show that the number of breakpoints obtained by the algorithm is less, which reflects the superiority of the algorithm in the discretization of continuous attributes.
(3) an attribute reduction algorithm based on conditional equivalence class is proposed. On the basis of the kernel attribute set, the algorithm can not be correctly classified into the class of the decision class in the condition class of the kernel attribute, and find the attributes that can distinguish the class from the rest of the kernel attribute, and add it to the kernel attribute set, thus the minimum attribute reduction is obtained. The attribute reduction algorithm based on the heuristic information can not guarantee that the reduction set must be the minimum attribute reduction set. The experimental results show that the computational complexity of the algorithm is low, and the reduction efficiency is improved.
(4) the attribute reduction technique of rough set is proposed to optimize the fault monitoring point. This method combines the minimum attribute reduction set of six fault monitoring points into a large decision table for attribute reduction, and determines the classification ability of the corresponding monitoring points according to the frequency of the fault characteristic parameters of each monitoring point in the final reduction concentration. The experimental results show that the method does not need to model the monitoring object and does not need to carry on the dynamic analysis to it, but directly handles the monitored vibration signal, and selects the best point according to the relation between the fault characteristic parameters and the types of the fault. It is an effective method to optimize the distribution of the measured points.
(5) the process of extracting decision rules based on rough set theory does not have the ability to learn induction, and the rate of fault pattern recognition is low. The attribute reduction technique of rough set theory can extract sensitive fault feature parameters, and the least squares support vector machine has strong pattern recognition ability, so it makes full use of both feature parameters extraction and pattern recognition. In other aspects, an intelligent fault diagnosis system based on rough set support vector machine is constructed. Both theory and practice show that the system improves the fault diagnosis performance of the gear box to a certain extent and provides a more general solution for the processing and recognition of nonlinear and non-stationary fault signals.

【學(xué)位授予單位】：中北大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2013
【分類號】：TH132.41;TH165.3

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