發(fā)布時(shí)間：2018-08-11 13:37

【摘要】：本課題來源于國家自然科學(xué)基金資助項(xiàng)目“基于粒子群優(yōu)化和濾波技術(shù)的復(fù)雜傳動(dòng)裝置早期故障診斷研究”(項(xiàng)目編號:50875247)。 齒輪箱作為機(jī)械設(shè)備中最常用的傳動(dòng)裝置,通常處于長期負(fù)載運(yùn)轉(zhuǎn),由于制造、裝配中存在的誤差,以及疲勞、老化等效應(yīng)的存在,齒輪箱在工作中總會發(fā)生故障,而它工作的狀態(tài)又直接關(guān)系到整個(gè)設(shè)備的運(yùn)行,因此對其進(jìn)行狀態(tài)監(jiān)測和故障診斷有重大的意義。 粒子濾波是一種基于模型的解決非高斯非線性隨機(jī)系統(tǒng)估計(jì)問題的有效方法。將粒子濾波應(yīng)用到齒輪箱故障診斷中能解決齒輪箱振動(dòng)信號的非高斯非線性問題。利用粒子濾波進(jìn)行故障診斷需要知道系統(tǒng)的狀態(tài)空間模型。文章通過建立齒輪箱振動(dòng)信號的ARMA模型,用ARMA模型的參數(shù)來作為齒輪箱的狀態(tài)空間模型參數(shù)。在建立ARMA模型中采用FPE準(zhǔn)則對模型定階,然后利用最小二乘法對參數(shù)進(jìn)行估計(jì)計(jì)算。仿真驗(yàn)證了粒子濾波狀態(tài)估計(jì)算法在信號降噪中的應(yīng)用,用其對實(shí)驗(yàn)室采集的齒輪箱正常工況和故障工況的振動(dòng)加速度信號進(jìn)行降噪處理,比較分析降噪前后數(shù)據(jù)的特征值,降噪后的數(shù)據(jù)特征值都優(yōu)于前者。 研究了基于粒子濾波優(yōu)化神經(jīng)網(wǎng)絡(luò)的算法,在該算法的基礎(chǔ)上建立了粒子濾波優(yōu)化神經(jīng)網(wǎng)絡(luò)模型。從降噪后的齒輪箱振動(dòng)信號中提取特征參量,對提取的特征參量利用粒子濾波優(yōu)化神經(jīng)網(wǎng)絡(luò)進(jìn)行故障識別,并取得理想效果。這同時(shí)也證明了粒子濾波信號降噪的效果是理想的。
[Abstract]:This paper comes from the project "Research on early Fault diagnosis of complex Transmission device based on Particle Swarm Optimization and filter Technology" (project No.: 50875247) funded by the National Natural Science Foundation of China. The gearbox, as the most commonly used transmission device in mechanical equipment, is usually in long-term load operation. Due to the errors in manufacture and assembly, as well as the effects of fatigue and aging, the gearbox will always fail in its work. The state of its work is directly related to the operation of the whole equipment, so it is of great significance to monitor and diagnose the state of the equipment. Particle filter is an effective method to solve the estimation problem of non-Gao Si nonlinear stochastic systems based on model. The application of particle filter to the gearbox fault diagnosis can solve the non-Gao Si nonlinear problem of the gearbox vibration signal. It is necessary to know the state space model of the system for fault diagnosis by particle filter. In this paper, the ARMA model of gearbox vibration signal is established, and the parameters of ARMA model are used as the parameters of the state space model of the gearbox. In the establishment of ARMA model, the FPE criterion is used to determine the order of the model, and the least square method is used to estimate the parameters. The application of particle filter state estimation algorithm in signal de-noising is verified by simulation. The vibration acceleration signal of gearbox under normal working condition and fault condition is processed by Particle filter, and the eigenvalues of the data before and after noise reduction are compared and analyzed. The data eigenvalues after noise reduction are better than the former. The optimization neural network algorithm based on particle filter is studied, and the particle filter optimization neural network model is established on the basis of the algorithm. The characteristic parameters are extracted from the vibration signal of the gear box after noise reduction, and the fault identification of the extracted characteristic parameters is carried out by using the particle filter to optimize the neural network, and the ideal results are obtained. At the same time, it is proved that the noise reduction effect of particle filter signal is ideal.
【學(xué)位授予單位】：中北大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2011
【分類號】：TH165.3

【引證文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前2條

1 李桃;基于粒子濾波技術(shù)的齒輪箱故障診斷研究[D];中北大學(xué);2012年

2 郭姍姍;基于改進(jìn)粒子濾波的紅外弱小目標(biāo)檢測前跟蹤算法[D];哈爾濱工程大學(xué);2012年

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本文編號：2177148