【摘要】：針對目前數(shù)控機(jī)床故障復(fù)雜、診斷困難的問題,提出基于人工神經(jīng)網(wǎng)絡(luò)的故障診斷方法。在研究傳統(tǒng)BP神經(jīng)網(wǎng)絡(luò)故障診斷模型基礎(chǔ)上,引入改進(jìn)的BP算法-LM算法,建立機(jī)床主軸系統(tǒng)LM-BP神經(jīng)網(wǎng)絡(luò)故障診斷模型,對機(jī)床主軸系統(tǒng)故障進(jìn)行分析與診斷,再通過Matlab仿真與傳統(tǒng)BP神經(jīng)網(wǎng)絡(luò)相對比,仿真結(jié)果表明:傳統(tǒng)BP神經(jīng)網(wǎng)絡(luò)存在較難實現(xiàn)快速、準(zhǔn)確的故障定位問題,而BP神經(jīng)網(wǎng)絡(luò)LM算法作為故障診斷的核心算法收斂速度快、識別準(zhǔn)確。該方案設(shè)計合理可行,有較好的應(yīng)用前景,并給出應(yīng)用了實例。
[Abstract]:A fault diagnosis method based on artificial neural network (Ann) is proposed to solve the problem of complex fault and difficult diagnosis of CNC machine tool. On the basis of studying the traditional BP neural network fault diagnosis model, an improved BP algorithm-LM algorithm is introduced to establish the LM-BP neural network fault diagnosis model of machine tool spindle system, and to analyze and diagnose the machine tool spindle system fault. By comparing Matlab simulation with traditional BP neural network, the simulation results show that the traditional BP neural network is difficult to realize fast and accurate fault location problem, and LM algorithm of BP neural network, as the core algorithm of fault diagnosis, converges quickly. The identification is accurate. The design of this scheme is reasonable and feasible, and it has a good application prospect, and an example is given.
【作者單位】： 四川理工學(xué)院自動化與電子信息學(xué)院;
【基金】：四川理工學(xué)院學(xué)科建設(shè)項目(2014JC02) 人工智能四川省重點實驗室重點項目(2012RZY22) 四川理工學(xué)院學(xué)科特色培育項目(2013PMG04)
【分類號】：TP183;TG659

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本文編號：2273295