本文選題：實驗數(shù)據(jù)　切入點：集合經(jīng)驗模態(tài)分解　出處：《沈陽理工大學》2017年碩士論文

【摘要】：隨著現(xiàn)代工業(yè)規(guī)模的不斷擴大和系統(tǒng)復雜度的日益提高,電機軸承被越來越多的應用在工業(yè)生產(chǎn)中,因此,對電機軸承進行有效精確的故障診斷便成為了一項十分有意義的科研課題。本文在基于數(shù)據(jù)驅(qū)動的基礎上,提出基于集合經(jīng)驗模態(tài)分解(EEMD)-改進的局部均值分解(ILMD)-改進萬有引力搜索算法(IGSA)-增量概率神經(jīng)網(wǎng)絡(IPNN)的電機軸承故障診斷集合方法,以此提高電機軸承故障診斷的精確性。本文全部的試驗數(shù)據(jù)都來自美國凱斯西儲大學軸承實驗中心。而在工業(yè)生產(chǎn)中,由于電機軸承的工作環(huán)境往往十分嘈雜,再加上受到其他設備本身振動的干擾,使得其振動信號含有噪聲,因此,要對采集的數(shù)據(jù)進行預處理,減低噪聲。傳統(tǒng)的降噪方法不能很好的對非平穩(wěn)、非線性的數(shù)據(jù)進行降噪,本文采用對于非平穩(wěn)非線性信號有很強分解能力的EEMD算法,通過計算相關(guān)系數(shù)并設定閾值,對數(shù)據(jù)進行降噪預處理。故障提取方面,針對LMD存在的端點效應問題,提出改進LMD方法對數(shù)據(jù)進行分解以改善端點效應影響,并計算乘積函數(shù)分量的樣本熵和能量作為特征參數(shù),組成故障特征向量,作為故障診斷神經(jīng)網(wǎng)絡的輸入。故障診斷方法采用基于統(tǒng)計原理的前饋型神經(jīng)網(wǎng)絡IPNN,IPNN不需要設置初始權(quán)值,訓練簡潔,分類能力強。由于故障診斷中,網(wǎng)絡模型的參數(shù)會對診斷性能有著重大影響,故本文采用基于時變權(quán)重和邊界變異的改進GSA優(yōu)化算法對網(wǎng)絡模型的閾值進行優(yōu)化,以改善標準GSA算法收斂速度較慢且容易陷入到局部最優(yōu)狀態(tài)等缺點,提高分類結(jié)果的精確性。通過理論研究和實驗結(jié)果可以表明,本文提出的基于數(shù)據(jù)驅(qū)動的EEMD-ILMD-IGSA-IPNN電機軸承故障診斷集合方法診斷性能良好,能夠有效的對電機軸承故障進行診斷且準確率較高。
[Abstract]:With the continuous expansion of modern industrial scale and the increasing complexity of the system, motor bearings are more and more used in industrial production. Effective and accurate fault diagnosis for motor bearings has become a very meaningful research topic. A set method of motor bearing fault diagnosis is proposed based on set empirical mode decomposition (EMD) and improved local mean decomposition (ILMD)-improved universal gravity search algorithm (IGSA-incremental probabilistic neural network / IPNN). In order to improve the accuracy of fault diagnosis of motor bearings, all the test data in this paper come from the bearing Experimental Center of case Western Reserve University. In industrial production, the working environment of motor bearings is often very noisy. In addition, the vibration signals of other equipments contain noise because they are disturbed by the vibration itself. Therefore, the collected data should be preprocessed to reduce the noise. In this paper, EEMD algorithm, which has a strong ability to decompose nonstationary nonlinear signals, is used to pre-process the data by calculating the correlation coefficient and setting a threshold. Aiming at the problem of endpoint effect in LMD, an improved LMD method is proposed to decompose the data to improve the effect of endpoint effect. The sample entropy and energy of the product function component are calculated as feature parameters to form the fault eigenvector. As the input of neural network for fault diagnosis, the method of fault diagnosis adopts the feedforward neural network IPNNNIPNN based on statistical principle, which does not need to set initial weights, is simple in training, and has strong classification ability. The parameters of the network model will have a significant impact on the diagnostic performance. Therefore, an improved GSA optimization algorithm based on time-varying weight and boundary mutation is used to optimize the threshold of the network model. In order to improve the accuracy of the classification results, the convergence speed of the standard GSA algorithm is slow and it is easy to fall into the local optimal state. The data-driven EEMD-ILMD-IGSA-IPNN motor bearing fault diagnosis set method presented in this paper has good diagnostic performance and can effectively diagnose motor bearing fault with high accuracy.
【學位授予單位】：沈陽理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM307

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