本文選題：礦用變壓器　切入點(diǎn)：油浸式變壓器　出處：《工礦自動(dòng)化》2017年05期 　論文類型：期刊論文

【摘要】：針對(duì)用于礦井中有煤塵而無(wú)爆炸危險(xiǎn)的地方、以油浸式為主的變壓器,提出了一種基于優(yōu)化模糊Petri網(wǎng)的礦用變壓器故障診斷模型。根據(jù)故障征兆與故障之間的關(guān)系,利用模糊產(chǎn)生規(guī)則來(lái)建立故障診斷模型;利用Elman網(wǎng)絡(luò)算法的自學(xué)習(xí)和自適應(yīng)能力對(duì)模型初始參數(shù)進(jìn)行優(yōu)化處理,使模糊Petri網(wǎng)初始參數(shù)值的設(shè)置更加合理。Matlab仿真結(jié)果表明,優(yōu)化模型和未優(yōu)化模型的故障診斷準(zhǔn)確率分別為87.88%和75.76%,驗(yàn)證了優(yōu)化模型的有效性。
[Abstract]:A fault diagnosis model of mine transformer based on optimized fuzzy Petri net is proposed for coal dust without explosion hazard. According to the relationship between fault symptom and fault, a fault diagnosis model of mine transformer based on optimized fuzzy Petri net is presented. The fault diagnosis model is established by using fuzzy generation rules, and the initial parameters of the model are optimized by using the self-learning and adaptive ability of the Elman network algorithm. The simulation results show that the setting of the initial parameters of the fuzzy Petri net is more reasonable. The accuracy of fault diagnosis of the optimized model and the unoptimized model are 87.88% and 75.76 respectively, which verify the validity of the optimized model.
【作者單位】： 山東科技大學(xué)電氣與自動(dòng)化工程學(xué)院;日照市機(jī)電工程學(xué)校機(jī)電系;
【基金】：中國(guó)博士后科學(xué)基金資助項(xiàng)目(2015T80729) 青島市博士后研究人員應(yīng)用研究項(xiàng)目(2015190)
【分類號(hào)】：TD611.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 劉景艷;李玉東;郭順京;;基于Elman神經(jīng)網(wǎng)絡(luò)的齒輪箱故障診斷[J];工礦自動(dòng)化;2016年08期

2 戴晨曦;劉志剛;胡軻s，

本文編號(hào)：1574421