考慮退化和測量不確定因素的射頻晶振壽命預(yù)測技術(shù)研究
發(fā)布時間：2018-08-01 08:03
【摘要】：為了解決射頻(RF)技術(shù)的發(fā)展帶來的安全隱患,在現(xiàn)有有關(guān)射頻電路的壽命預(yù)測相關(guān)研究相對缺乏的情況下,本文總結(jié)了國內(nèi)外有關(guān)科研成果,以射頻晶體振蕩器為對象展開了壽命預(yù)測的研究工作。通過相關(guān)資料和射頻晶振的特性分析,本文提取了相位噪聲作為反映射頻晶振健康狀態(tài)的特征量,提出了兩種解決思路:從相位噪聲的冪律系數(shù)出發(fā)的貝葉斯框架預(yù)測方法和從加速度效應(yīng)出發(fā)的晶振加速靈敏度預(yù)測方法,并分別結(jié)合減小不確定因素的方法來給出振蕩器的剩余使用壽命。針對兩種模型的特點,本文分別闡述了剩余壽命預(yù)測中存在的各項誤差的來源和影響,實驗結(jié)果分別以概率區(qū)間和點估計的形式呈現(xiàn)。主要研究內(nèi)容分為三部分:1.射頻晶體振蕩器壽命特征量的提取。詳細(xì)介紹了射頻晶振的關(guān)鍵特性參量以及在加速振動條件下的變化,重點闡述了晶振加速效應(yīng)原理和相位噪聲冪律原理同它們的用于壽命預(yù)測現(xiàn)實意義。2.壽命預(yù)測方法。加速靈敏度預(yù)測方法采集了隨時間退化的加速靈敏度值,轉(zhuǎn)化為一組與初始時刻加速靈敏度比較的曲線相似度并通過基于M估計的最優(yōu)剪枝極限學(xué)習(xí)機進(jìn)行預(yù)測;相位噪聲預(yù)測方法根據(jù)振蕩器相位噪聲的Lesson理論構(gòu)建為不同頻偏處的負(fù)冪成分線性和,再根據(jù)最小二乘估計找出這些負(fù)冪成分系數(shù)隨時間退化的規(guī)律,將關(guān)鍵系數(shù)退化序列輸入相近的退化模型,結(jié)合貝葉斯框架在最大似然原則下進(jìn)行迭代,在線地更新每個監(jiān)測時刻的模型參數(shù)以及下一時刻的預(yù)測值。3.不確定因素分析。本文對從數(shù)據(jù)采集到預(yù)測結(jié)果生成全部過程中可能產(chǎn)生的不確定性來源進(jìn)行了介紹,并在兩種壽命特征量預(yù)測方法中加以體現(xiàn),作為減小不確定性的主要手段;用基于歷史樣本與現(xiàn)場樣本相似性的思路來修正單一現(xiàn)場樣本數(shù)據(jù)量不足造成的外推預(yù)測偶然誤差,反映了預(yù)測這一概念本身的不確定性以及誤差的傳播。事實證明結(jié)合不確定因素分析的兩種特征量進(jìn)行射頻電路壽命預(yù)測均得到了良好的效果,具有一定應(yīng)用價值。
[Abstract]:In order to solve the potential safety problems caused by the development of RF (RF) technology, this paper summarizes the domestic and foreign scientific research achievements under the condition of the relative lack of existing research on life prediction of RF circuits. The research on life prediction of RF crystal oscillator is carried out. Based on the relevant data and the characteristic analysis of the RF crystal oscillator, the phase noise is extracted as the characteristic quantity to reflect the healthy state of the RF crystal oscillator. Two solutions are proposed: the Bayesian frame prediction method based on the power law coefficient of phase noise and the crystal oscillator acceleration sensitivity prediction method based on the acceleration effect. The remaining service life of the oscillator is given by combining with the method of reducing uncertainty. According to the characteristics of the two models, the source and influence of the errors in the residual life prediction are discussed respectively. The experimental results are presented in the form of probabilistic interval and point estimation respectively. The main research content is divided into three parts: 1. Extraction of life characteristic of RF crystal oscillator. The key characteristic parameters of RF crystal oscillator and its changes under accelerated vibration are introduced in detail. The principle of crystal oscillator acceleration effect and phase noise power law principle and their practical significance in life prediction are expounded in detail. Life prediction method. The accelerated sensitivity prediction method gathers the accelerated sensitivity values which degenerate with time, and transforms them into a set of curve similarity compared with the initial acceleration sensitivity, and forecasts them by the optimal pruning extreme learning machine based on M estimation. According to the Lesson theory of phase noise of the oscillator, the phase noise prediction method is constructed as the linear sum of the negative power components at different frequency deviations, and the law of these negative power component coefficients degenerating with time is found according to the least square estimation. The key coefficient degenerate sequence is input into the similar degenerate model and the Bayesian framework is used to iterate under the maximum likelihood principle to update the model parameters of each monitoring time and the prediction value of the next time. Analysis of uncertain factors. In this paper, the possible sources of uncertainty in the whole process from data acquisition to prediction result generation are introduced, which are reflected in two prediction methods of life characteristic quantity, as the main means to reduce uncertainty. Based on the similarity between the historical sample and the field sample, the extrapolation error caused by the shortage of the sample data is corrected, which reflects the uncertainty of the concept of prediction and the propagation of the error. It has been proved that the life prediction of RF circuits based on the two characteristic variables of uncertainty analysis has a good effect and has certain application value.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN752
本文編號：2156845
[Abstract]:In order to solve the potential safety problems caused by the development of RF (RF) technology, this paper summarizes the domestic and foreign scientific research achievements under the condition of the relative lack of existing research on life prediction of RF circuits. The research on life prediction of RF crystal oscillator is carried out. Based on the relevant data and the characteristic analysis of the RF crystal oscillator, the phase noise is extracted as the characteristic quantity to reflect the healthy state of the RF crystal oscillator. Two solutions are proposed: the Bayesian frame prediction method based on the power law coefficient of phase noise and the crystal oscillator acceleration sensitivity prediction method based on the acceleration effect. The remaining service life of the oscillator is given by combining with the method of reducing uncertainty. According to the characteristics of the two models, the source and influence of the errors in the residual life prediction are discussed respectively. The experimental results are presented in the form of probabilistic interval and point estimation respectively. The main research content is divided into three parts: 1. Extraction of life characteristic of RF crystal oscillator. The key characteristic parameters of RF crystal oscillator and its changes under accelerated vibration are introduced in detail. The principle of crystal oscillator acceleration effect and phase noise power law principle and their practical significance in life prediction are expounded in detail. Life prediction method. The accelerated sensitivity prediction method gathers the accelerated sensitivity values which degenerate with time, and transforms them into a set of curve similarity compared with the initial acceleration sensitivity, and forecasts them by the optimal pruning extreme learning machine based on M estimation. According to the Lesson theory of phase noise of the oscillator, the phase noise prediction method is constructed as the linear sum of the negative power components at different frequency deviations, and the law of these negative power component coefficients degenerating with time is found according to the least square estimation. The key coefficient degenerate sequence is input into the similar degenerate model and the Bayesian framework is used to iterate under the maximum likelihood principle to update the model parameters of each monitoring time and the prediction value of the next time. Analysis of uncertain factors. In this paper, the possible sources of uncertainty in the whole process from data acquisition to prediction result generation are introduced, which are reflected in two prediction methods of life characteristic quantity, as the main means to reduce uncertainty. Based on the similarity between the historical sample and the field sample, the extrapolation error caused by the shortage of the sample data is corrected, which reflects the uncertainty of the concept of prediction and the propagation of the error. It has been proved that the life prediction of RF circuits based on the two characteristic variables of uncertainty analysis has a good effect and has certain application value.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN752
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