本文選題：故障診斷 + 免疫遺傳算法�。� 參考：《遼寧大學(xué)》2012年碩士論文

【摘要】：大規(guī)模、高精度的現(xiàn)代化機(jī)械設(shè)備的各部件之間的聯(lián)系、耦合相當(dāng)緊密，當(dāng)某一部件發(fā)生故障，整臺(tái)設(shè)備、甚至是整條生產(chǎn)線都將受到影響。滾動(dòng)軸承作為機(jī)械設(shè)備中最常見的零部件之一，其運(yùn)行狀態(tài)又直接影響到整臺(tái)機(jī)器的性能。在對(duì)滾動(dòng)軸承的診斷過程中，其診斷模式與特征向量之間是非常復(fù)雜的非線性關(guān)系，用振動(dòng)信號(hào)的時(shí)域、頻域分析方法很難全面地反映。人工神經(jīng)網(wǎng)絡(luò)在故障診斷領(lǐng)域顯示出巨大的應(yīng)用潛力。 本文以精密電機(jī)軸承故障診斷問題的研究為背景，通過對(duì)其故障特征的分析，提取適合的特征參數(shù)，利用神經(jīng)網(wǎng)絡(luò)的強(qiáng)非線性映射能力和遺傳算法的全局優(yōu)化能力建立診斷模型，探討了其在電機(jī)軸承故障診斷中的應(yīng)用。 在閱讀分析了大量關(guān)于數(shù)據(jù)驅(qū)動(dòng)的故障診斷方法的基礎(chǔ)上，本文提出一種對(duì)遺傳神經(jīng)網(wǎng)絡(luò)的故障診斷算法的改進(jìn)：為了避免進(jìn)化初期超個(gè)體的誤導(dǎo)作用，對(duì)適應(yīng)度函數(shù)做了調(diào)整；采用適合于浮點(diǎn)數(shù)編碼方式的算術(shù)交叉策略和非均勻變異策略，并增添交叉次數(shù)和變異次數(shù)，以加快模型的收斂速度。 又提出一種改進(jìn)的免疫遺傳神經(jīng)網(wǎng)絡(luò)故障診斷方法，以“自我調(diào)節(jié)”機(jī)制為理論基礎(chǔ)，提出一種基于抗體期望繁殖率的自適應(yīng)交叉變異概率調(diào)整方法，使與抗體自身息息相關(guān)。同時(shí)又受到精英策略的啟發(fā)，對(duì)算術(shù)交叉算法中的組合系數(shù)做相應(yīng)改善，使改進(jìn)后的算術(shù)交叉算法，，更加偏向于適應(yīng)度較高的個(gè)體進(jìn)行線性運(yùn)算。重新定義了基于歐氏距離的相似度的計(jì)算方法，提出一種“3σ”計(jì)算方法，使得對(duì)門限的設(shè)置更簡單直觀。 在MATLAB平臺(tái)下進(jìn)行仿真實(shí)驗(yàn)，實(shí)驗(yàn)結(jié)果表明，提出的二階段數(shù)據(jù)預(yù)處理方法是有效可行的；門限值簡單化的設(shè)定提高了該方法的泛化能力，建立的遺傳神經(jīng)網(wǎng)絡(luò)（GA-BP）故障診斷算法和免疫遺傳神經(jīng)網(wǎng)絡(luò)（IGA-BP）故障診斷算法具有更快的收斂速度，更強(qiáng)的自適應(yīng)性，對(duì)故障類型具有較為準(zhǔn)確的分類能力，診斷效果良好。
[Abstract]:The coupling between the components of a large scale and high precision modern mechanical equipment is very close. When a component fails, the whole equipment, even the whole production line, will be affected. As one of the most common parts in mechanical equipment, rolling bearing has a direct impact on the performance of the whole machine. In the diagnosis process of rolling bearing, the nonlinear relationship between the diagnostic mode and the eigenvector is very complex, so it is difficult to reflect the vibration signal in time domain and frequency domain analysis method. Artificial neural network (Ann) has shown great application potential in the field of fault diagnosis. In this paper, based on the research of bearing fault diagnosis of precision motor, through the analysis of its fault characteristics, the suitable characteristic parameters are extracted. Based on the strong nonlinear mapping ability of neural network and the global optimization ability of genetic algorithm, a diagnosis model is established, and its application in motor bearing fault diagnosis is discussed. On the basis of reading and analyzing a large number of data-driven fault diagnosis methods, this paper proposes an improved fault diagnosis algorithm for genetic neural networks: in order to avoid the misguided effect of superindividuals in the early stages of evolution, The fitness function is adjusted and the arithmetic crossover strategy and non-uniform mutation strategy suitable for floating-point coding are adopted and the crossover times and mutation times are added to speed up the convergence of the model. An improved immune genetic neural network fault diagnosis method is proposed. Based on the "self-regulation" mechanism, an adaptive crossover mutation probability adjustment method based on the expected reproduction rate of antibody is proposed, which is closely related to the antibody itself. At the same time, inspired by the elite strategy, the combination coefficients in the arithmetic crossover algorithm are improved accordingly, so that the improved arithmetic crossover algorithm is more inclined to the individual with higher fitness for linear operation. This paper redefines the similarity calculation method based on Euclidean distance, and proposes a "3 蟽" calculation method, which makes the setting of threshold more simple and intuitive. The simulation results on MATLAB platform show that the proposed two-stage data preprocessing method is effective and feasible, and the simplified threshold value improves the generalization ability of the method. The genetic neural network fault diagnosis algorithm and the immune genetic neural network IGA-BP-based fault diagnosis algorithm have faster convergence speed, stronger self-adaptability, more accurate classification ability and good diagnosis effect.
【學(xué)位授予單位】：遼寧大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TH165.3;TP18

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 韓延U

本文編號(hào)：2019030