發(fā)布時(shí)間：2018-06-03 20:18

  本文選題：協(xié)同進(jìn)化PSO算法 + RBF神經(jīng)網(wǎng)絡(luò)�。� 參考：《中北大學(xué)》2011年碩士論文

【摘要】：故障診斷技術(shù)是現(xiàn)代化生產(chǎn)發(fā)展的產(chǎn)物,齒輪箱是工程機(jī)械中的重要部件,齒輪和滾動(dòng)軸承是齒輪箱中的易損元件。據(jù)統(tǒng)計(jì),傳動(dòng)機(jī)械中80%的故障是由齒輪箱故障引起的,因此,對(duì)齒輪箱的運(yùn)行狀態(tài)監(jiān)測和故障模式識(shí)別一直是機(jī)械故障診斷技術(shù)中的重點(diǎn)。 本論文在潛心研究協(xié)同進(jìn)化PSO算法相關(guān)理論的基礎(chǔ)上,結(jié)合RBF神經(jīng)網(wǎng)絡(luò)優(yōu)化的具體問題,提出一種基于協(xié)同進(jìn)化PSO算法的RBF神經(jīng)網(wǎng)絡(luò)優(yōu)化模型,并將優(yōu)化后的RBF神經(jīng)網(wǎng)絡(luò)應(yīng)用于齒輪箱故障診斷技術(shù)中,以期對(duì)齒輪箱系統(tǒng)的各種異常狀態(tài)或故障工況做出及時(shí)、準(zhǔn)確而有效的判斷,指導(dǎo)齒輪箱系統(tǒng)的運(yùn)行,提高齒輪箱系統(tǒng)的可靠性、安全性和有效性,最終把由齒輪箱故障帶來的經(jīng)濟(jì)損失降低到最低水平。 實(shí)驗(yàn)結(jié)果表明,本論文所提出的基于協(xié)同進(jìn)化PSO算法的RBF神經(jīng)網(wǎng)絡(luò)優(yōu)化模型具有可行性,且優(yōu)化后RBF神經(jīng)網(wǎng)絡(luò)的測試結(jié)果與傳統(tǒng)RBF神經(jīng)網(wǎng)絡(luò)的測試結(jié)果相比具有較高的訓(xùn)練精度和較快的收斂速度。因此,通過本論文的研究,不僅為RBF神經(jīng)網(wǎng)絡(luò)提供了一種新的優(yōu)化途徑,同時(shí)也大大提高了RBF神經(jīng)網(wǎng)絡(luò)在齒輪箱故障診斷技術(shù)中的診斷效率,進(jìn)而豐富和發(fā)展了粒子群優(yōu)化算法和神經(jīng)網(wǎng)絡(luò)在齒輪箱故障診斷中的應(yīng)用。
[Abstract]:Fault diagnosis technology is the product of modern production, gearbox is an important part of construction machinery, gear and rolling bearing are vulnerable components in gearbox. According to statistics, 80% of the faults in transmission machinery are caused by gearbox faults. Therefore, the monitoring of gearbox operation status and fault pattern recognition are always the key points in mechanical fault diagnosis technology. Based on the theory of coevolutionary PSO algorithm and the specific problem of RBF neural network optimization, a RBF neural network optimization model based on coevolutionary PSO algorithm is proposed in this paper. And the optimized RBF neural network is applied to the gearbox fault diagnosis technology, in order to make timely, accurate and effective judgment on the abnormal state or malfunction condition of the gearbox system, and guide the operation of the gearbox system. Improve the reliability, safety and effectiveness of the gearbox system, and finally reduce the economic loss caused by the gearbox failure to the lowest level. Experimental results show that the proposed RBF neural network optimization model based on co-evolutionary PSO algorithm is feasible. Compared with the traditional RBF neural network, the test results of the optimized RBF neural network have higher training precision and faster convergence speed. Therefore, the research in this paper not only provides a new way to optimize the RBF neural network, but also improves the diagnosis efficiency of the RBF neural network in the gearbox fault diagnosis technology. The application of particle swarm optimization algorithm and neural network in gearbox fault diagnosis is enriched and developed.
【學(xué)位授予單位】：中北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2011
【分類號(hào)】：TH165.3;TP183

【引證文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前1條

1 顧秀江;基于粒子群—神經(jīng)網(wǎng)絡(luò)的自動(dòng)裝填控制系統(tǒng)故障診斷的研究[D];中北大學(xué);2012年

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本文編號(hào)：1974045