本文關(guān)鍵詞：基于狀態(tài)振動(dòng)特征的空間滾動(dòng)軸承可靠性評(píng)估方法研究　出處：《重慶大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 空間滾動(dòng)軸承 可靠性評(píng)估 PHM模型 退化趨勢(shì)預(yù)測(cè)

【摘要】：滾動(dòng)軸承作為空間活動(dòng)部件的重要組成部分，它的運(yùn)行狀態(tài)直接影響整個(gè)空間活動(dòng)件的運(yùn)行性能。實(shí)踐表明，空間活動(dòng)件的故障大多是出自其軸承的問(wèn)題，但是在空間場(chǎng)合，由于軸承應(yīng)用受到環(huán)境條件的限制，不可能采用備份來(lái)保證軸承的可靠性，所以軸承一旦出現(xiàn)問(wèn)題，將導(dǎo)致整個(gè)空間活動(dòng)件的性能破壞�？臻g環(huán)境下的滾動(dòng)軸承要承受低溫和交變溫度、原子氧侵蝕等極端環(huán)境的綜合作用，極易造成空間滾動(dòng)軸承的精度失效，加快空間滾動(dòng)軸承的損壞。由于空間環(huán)境下軸承的失效機(jī)理與地面環(huán)境下的失效機(jī)理存在差異，因此為了保證空間活動(dòng)件高可靠、長(zhǎng)壽命運(yùn)行，避免一些重大事故的發(fā)生，需在模擬空間環(huán)境下開(kāi)展?jié)L動(dòng)軸承運(yùn)行可靠度評(píng)估。 傳統(tǒng)的可靠度評(píng)估方法，將概率論和數(shù)理統(tǒng)計(jì)理論作為主要的數(shù)學(xué)工具，利用大量的具有概率重復(fù)性的失效樣本，以確定失效分布類型，從而獲得宏觀意義上一批同類設(shè)備共性的平均可靠度。然而，對(duì)于空間滾動(dòng)軸承而言，由于運(yùn)行工況不同、轉(zhuǎn)速不穩(wěn)定等因素的影響，各個(gè)滾動(dòng)軸承的損傷、故障程度不同，導(dǎo)致其運(yùn)行可靠度也必然不同。針對(duì)某個(gè)具體的空間滾動(dòng)軸承進(jìn)行運(yùn)行可靠性評(píng)估是個(gè)性問(wèn)題，而基于大樣本條件并依賴概率統(tǒng)計(jì)數(shù)據(jù)得到的平均可靠度難以滿足單個(gè)空間滾動(dòng)軸承的運(yùn)行可靠性評(píng)估要求。 由于軸承的狀態(tài)特征量能夠提供可靠性評(píng)估的重要信息，因此，基于狀態(tài)特征量的可靠性建模與分析技術(shù)是解決單個(gè)空間滾動(dòng)軸承運(yùn)行狀態(tài)可靠性評(píng)估需求的一個(gè)重要途徑。目前，反映軸承運(yùn)行狀態(tài)的特征量主要有三種，即摩擦力矩、振動(dòng)和溫度，由于摩擦力矩、溫度等參數(shù)等不能有效反映空間滾動(dòng)軸承壽命狀態(tài)的變化，因此論文選用包涵空間滾動(dòng)軸承壽命特征信息豐富的振動(dòng)信號(hào)作為狀態(tài)特征量以評(píng)估滾動(dòng)軸承運(yùn)行過(guò)程中的可靠度。比例故障率模型(Proportional hazardmodel, PHM)是其中一種最為常用的基于振動(dòng)特征的可靠性評(píng)估模型，基于比例故障率模型的可靠性評(píng)估方法的關(guān)鍵是提取反映空間滾動(dòng)軸承運(yùn)行狀態(tài)的特征指標(biāo)及確定比例故障率模型的具體數(shù)學(xué)表達(dá)式，由實(shí)時(shí)獲取的振動(dòng)信號(hào)提取振動(dòng)特征指標(biāo)，評(píng)估空間滾動(dòng)軸承的運(yùn)行可靠度。同時(shí)，結(jié)合性能退化趨勢(shì)預(yù)測(cè)理論，在已建立的比例故障率模型的基礎(chǔ)上，實(shí)現(xiàn)空間滾動(dòng)軸承的可靠度趨勢(shì)預(yù)測(cè)，以確定滾動(dòng)軸承在未來(lái)任意一段時(shí)間內(nèi)的可靠度。具體內(nèi)容安排如下： ①基于狀態(tài)振動(dòng)特征的可靠度評(píng)估技術(shù)首要解決的是特征指標(biāo)構(gòu)建的問(wèn)題，為此研究了基于多域特征融合的構(gòu)建方法。提取時(shí)域、頻域、時(shí)頻域和威布爾分布特征信息組成高維多域特征集，采用流形學(xué)習(xí)方法對(duì)多域特征進(jìn)行維數(shù)約簡(jiǎn)，，以解決高維特征集之間存在的沖突、冗余問(wèn)題，并將約簡(jiǎn)以后的特征信息作為趨勢(shì)預(yù)測(cè)的特征指標(biāo)及比例故障率模型的響應(yīng)協(xié)變量。 ②針對(duì)單個(gè)空間滾動(dòng)軸承運(yùn)行狀態(tài)可靠性評(píng)估要求，同時(shí)克服經(jīng)典的可靠性分析方法存在的問(wèn)題，提出了基于振動(dòng)特征指標(biāo)的比例故障率模型評(píng)估可靠度評(píng)估方法。將高維多域特征集維數(shù)約簡(jiǎn)后的特征信息作為比例故障率模型的響應(yīng)協(xié)變量，采用極大似然函數(shù)原理估計(jì)模型的待定參數(shù)，建立空間滾動(dòng)軸承狀態(tài)特征指標(biāo)與可靠度之間的數(shù)學(xué)模型，實(shí)現(xiàn)空間滾動(dòng)軸承運(yùn)行狀態(tài)的可靠性評(píng)估。 ③針對(duì)單個(gè)空間滾動(dòng)軸承可靠度趨勢(shì)預(yù)測(cè)問(wèn)題，提出了基于性能退化趨勢(shì)預(yù)測(cè)的空間滾動(dòng)軸承可靠度趨勢(shì)預(yù)測(cè)方法。將高維多域特征集維數(shù)約簡(jiǎn)后的特征信息作為最小二乘支持向量機(jī)的輸入，訓(xùn)練并建立趨勢(shì)預(yù)測(cè)模型，實(shí)現(xiàn)空間滾動(dòng)軸承性能退化趨勢(shì)預(yù)測(cè)。將趨勢(shì)預(yù)測(cè)結(jié)果代入已建立的比例故障率模型中，即可實(shí)現(xiàn)空間滾動(dòng)軸承的可靠度趨勢(shì)的預(yù)測(cè)。 ④在以上理論的基礎(chǔ)上，采用C#為開(kāi)發(fā)平臺(tái)，研發(fā)空間滾動(dòng)軸承性能退化趨勢(shì)預(yù)測(cè)、運(yùn)行可靠性評(píng)估等功能模塊，通過(guò)應(yīng)用對(duì)各模塊進(jìn)行檢驗(yàn)，并對(duì)本文所提方法進(jìn)行驗(yàn)證。
[Abstract]:As an important part of the rolling bearing space of the moving parts, its running state directly affects the performance of the entire space activities. The practice shows that the fault space of moving parts mostly from the bearing problem, but in space, because the bearing application is environmental conditions, it is impossible to use backup to ensure bearing so the bearing reliability, if there are problems, will lead to the damage of space activities. The performance of the rolling bearing under the space environment to bear alternating temperature, the comprehensive effect of atomic oxygen erosion and other extreme environment, extremely easy to cause the failure of space rolling bearing accuracy, accelerate the damage of space rolling bearing. Because of the space environment of the bearing the failure mechanism and failure mechanism of ground environment are different, so in order to ensure the high reliability of space activities, long service life, avoid some major accidents It is necessary to carry out the reliability evaluation of rolling bearing operation under the simulated space environment.
The reliability of the traditional evaluation method, the probability theory and mathematical statistics theory as the main mathematical tools, the use of a large number of repetitive failure probability samples to determine the failure distribution, so as to obtain the macro sense of a number of common similar equipment average reliability. However, for the space rolling bearing, because the running condition the different effects of speed, instability and other factors, each bearing damage fault degree is different, the operation reliability is also different. For a specific space bearing operation reliability assessment is based on individual issues, and a large sample and depends on the probability and statistics data from the average reliability assessment is difficult to meet the operation reliability of single space rolling bearing requirements.
Because of the important information, characteristics of bearing capacity can provide the reliability evaluation result, reliability modeling and analysis based on the amount of state characteristics is an important way to solve the single space needs assessment of the reliability of rolling bearing. At present, there are three main features reflect the running state of the bearing, the friction torque, vibration and temperature because, friction torque, temperature can not effectively reflect the change of space rolling bearing lifetime state, so the inclusion of space vibration signal of rolling bearing life rich feature information for character in order to assess the reliability of rolling bearing during operation. The proportion of the failure rate model (Proportional hazardmodel PHM) is one of a kind the reliability evaluation model based on the vibration characteristics of commonly used, key reliability evaluation method based on the proportional hazards model is The extraction characteristic index space reflect the running state of rolling bearings and determine the proportional hazards model specific mathematical expressions, the real-time vibration signal by the vibration feature extraction index, evaluation of space rolling bearing reliability. At the same time, combined with the performance degradation prediction theory, based on the proportion of the established fault rate model, reliability prediction the degree of tendency to realize the space rolling bearing, rolling bearing in the future to determine any time reliability. The main contents are as follows:
Based on the state of the vibration characteristics of primary evaluation reliability is characteristic of index construction, this research constructs the multi domain fusion method based on feature extraction. Time domain, frequency domain, time-frequency domain and Weibull distribution feature information of high dimensional multi domain feature set, the manifold learning methods for dimensionality reduction of multi domain features. In order to solve the conflict between the high dimensional collection of redundancy, and the characteristics of information reduction after as the covariate response characteristic index and proportion trend forecast failure rate model.
The single space rolling bearing reliability assessment requirements, and methods to overcome problems of classical reliability analysis, put forward the vibration characteristic index proportional hazards model evaluation method of reliability evaluation based on response covariate Gao Weiduo domain feature set feature dimension reduction as the proportion of the failure rate model, the maximum likelihood the function principle of estimation parameters of the model, establish the mathematical model of state space characteristics of rolling bearing and the reliability of the reliability evaluation, implementation of space running state of rolling bearings.
The single space rolling bearing reliability prediction problem, proposes the prediction method of reliability prediction trend of rolling bearing performance degradation of space. Based on the high dimensional multi domain feature set feature dimension reduction as the inputs of least square support vector machine, training and the establishment of trend prediction model, realize the space rolling bearing performance degradation the trend of trend prediction. Prediction results into the established proportional hazards model, reliability prediction of rolling bearing can achieve spatial trend.
(4) on the basis of the above theories, using C# as the development platform, the functional degradation trend prediction, operational reliability evaluation and other functional modules of the rolling bearing are developed, and the modules are tested by application, and the proposed method is verified.

【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TH133.33;TB114.3

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