国产日韩欧美一区二区三区三州_亚洲少妇熟女av_久久久久亚洲av国产精品_波多野结衣网站一区二区_亚洲欧美色片在线91_国产亚洲精品精品国产优播av_日本一区二区三区波多野结衣 _久久国产av不卡

?

復(fù)雜復(fù)印機(jī)故障信號(hào)的檢測(cè)與提取

2018-11-13 05:31黃燕
現(xiàn)代電子技術(shù) 2018年22期
關(guān)鍵詞:復(fù)印機(jī)蟻群

黃燕

摘 要: 針對(duì)當(dāng)前復(fù)印機(jī)故障信號(hào)檢測(cè)提取方法中存在誤檢率高的問(wèn)題,提出基于蟻群的復(fù)雜復(fù)印機(jī)故障信號(hào)的檢測(cè)與提取方法?;谙伻旱膹?fù)雜復(fù)印機(jī)故障信號(hào)的檢測(cè)中,利用檢測(cè)某一路徑的最大代價(jià)和最小代價(jià)得到螞蟻于該路徑上所釋放信息素的濃度,以此計(jì)算蟻群對(duì)于某條路徑選取的概率。更新該條路徑上信息素濃度,按照路徑上的螞蟻存留的信息素濃度對(duì)復(fù)印機(jī)故障檢測(cè)過(guò)程中路徑選擇優(yōu)先順序進(jìn)行判斷,以檢測(cè)出復(fù)印機(jī)故障信號(hào)源。將復(fù)印機(jī)故障信號(hào)源代入小波包分析中,得到復(fù)印機(jī)總故障信號(hào),計(jì)算故障信號(hào)中的各個(gè)頻帶信號(hào)相應(yīng)能量,利用各頻帶相應(yīng)能量,構(gòu)建復(fù)印機(jī)故障信號(hào)特征向量。實(shí)驗(yàn)結(jié)果表明,與當(dāng)前方法相比,所提方法誤檢率最低約為0.3%,故障檢測(cè)準(zhǔn)確性較高,檢測(cè)性能更為優(yōu)越。

關(guān)鍵詞: 復(fù)印機(jī); 故障信號(hào); 信號(hào)檢測(cè); 信號(hào)提??; 蟻群; 小波包

中圖分類號(hào): TN911.23?34; TH165 文獻(xiàn)標(biāo)識(shí)碼: A 文章編號(hào): 1004?373X(2018)22?0103?03

Abstract: In allusion to the high error detection rate of the current fault signal detection and extraction method of the photocopier, a fault signal detection and extraction method based on the ant colony is proposed for the complex photocopier. During the ant colony based fault signal detection of the complex photocopier, the concentration of the pheromone released on the path by the ant is obtained by using the maximum cost and minimum cost of detecting a certain path, so as to calculate the selection probability of a certain path by the ant colony. The pheromone concentration on the path is updated. The path selection priority during the fault detection proces of the photocopier is judged according to the pheromone concentration retained on the path by the ant, so as to detect the fault signal source of the photocopier. The fault signal source of the photocopier is substituted into wavelet packet analysis to obtain the total fault signals of the photocopier. The corresponding energy of each frequency band signal in fault signals is calculated, which is used to construct the feature vector for fault signals of the photocopier. The experimental results show that, in comparison with the current method, the proposed method has a higher fault detection accuracy and better detection performance with a false detection rate of about 0.3% at minimum.

Keywords: photocopier; fault signal; signal detection; signal extraction; ant colony; wavelet packet

0 引 言

當(dāng)今社會(huì)中,各種類型的復(fù)印機(jī)在各行各業(yè)中均有著十分廣泛的應(yīng)用[1]。因復(fù)印機(jī)為光、機(jī)和電為一體的電子設(shè)備,它的集成化程度比較高,且內(nèi)部結(jié)構(gòu)復(fù)雜,在日常的運(yùn)作中一旦產(chǎn)生故障,通常情況下非專業(yè)人員難以將其中的故障信號(hào)檢測(cè)出來(lái)[2?3]。由于復(fù)印機(jī)在工作中使用較為頻繁,在一定時(shí)期內(nèi)會(huì)產(chǎn)生靜電等問(wèn)題,這樣會(huì)導(dǎo)致與故障連接的其他位置也出現(xiàn)故障。綜上可知,復(fù)印機(jī)故障信號(hào)的檢測(cè)與提取成為了當(dāng)前急需解決的問(wèn)題。

劉洋等人提出基于RBF的設(shè)備故障檢測(cè)方法[4?5]。檢測(cè)過(guò)程中,先構(gòu)建單個(gè)傳感器預(yù)測(cè)模型與任意兩個(gè)傳感器預(yù)測(cè)模型,其次利用上述兩個(gè)模型對(duì)任意一個(gè)傳感器預(yù)測(cè)值與任意兩個(gè)傳感器預(yù)測(cè)值進(jìn)行計(jì)算,利用預(yù)測(cè)值和實(shí)際值間差值對(duì)傳感器的故障個(gè)數(shù)和位置等信息進(jìn)行判斷。該方法檢測(cè)耗時(shí)較少,但誤檢率較高。王迪等人提出基于多信號(hào)流的設(shè)備故障檢測(cè)方法[6]。以多信號(hào)為基礎(chǔ),引入故障先驗(yàn)知識(shí),得到多信號(hào)流故障檢測(cè)方案,利用引入故障概率改進(jìn)多信號(hào)流檢測(cè)方案。將該方法應(yīng)用于BEPCⅡ磁鐵電源控制設(shè)備故障檢測(cè)中,通過(guò)TEAMS測(cè)試工具箱實(shí)現(xiàn)該方法。此方法較為簡(jiǎn)單,但也存在誤檢率高的問(wèn)題。

上述方法不具備較為完善的性能,因此提出基于蟻群的復(fù)雜復(fù)印機(jī)故障信號(hào)的檢測(cè)與提取方法。

1 復(fù)雜復(fù)印機(jī)故障信號(hào)的檢測(cè)與提取

1.1 復(fù)印機(jī)故障信號(hào)檢測(cè)

2 實(shí)驗(yàn)結(jié)果與分析

在Matlab 2017上搭建實(shí)驗(yàn)平臺(tái),以圖1所示復(fù)印機(jī)作為實(shí)驗(yàn)對(duì)象進(jìn)行實(shí)驗(yàn)。實(shí)驗(yàn)過(guò)程中,分別使用不同方法對(duì)比的形式,驗(yàn)證基于蟻群的復(fù)印機(jī)故障信號(hào)的檢測(cè)與提取方法有效性。實(shí)驗(yàn)指標(biāo)為設(shè)備故障檢測(cè)誤檢率。

分析圖2實(shí)驗(yàn)結(jié)果:在額定的噪聲信號(hào)下,基于RBF的設(shè)備故障檢測(cè)方法誤檢率最低約為7.2%;基于多信號(hào)流的設(shè)備故障檢測(cè)方法誤檢率最低約為5.7%;基于蟻群的復(fù)印機(jī)故障信號(hào)的檢測(cè)方法誤檢率最低約為0.3%。通過(guò)數(shù)據(jù)對(duì)比可知,基于蟻群的復(fù)印機(jī)故障信號(hào)的檢測(cè)與提取方法誤檢率要低于當(dāng)前方法。該結(jié)果主要是由于所提基于蟻群的復(fù)印機(jī)故障信號(hào)的檢測(cè)與提取方法在運(yùn)行過(guò)程中,利用SVD理論對(duì)復(fù)印機(jī)故障中的噪聲信號(hào)進(jìn)行去除,降低了復(fù)雜復(fù)印機(jī)故障信號(hào)檢測(cè)的誤檢率。

實(shí)驗(yàn)結(jié)果如圖2所示。

3 結(jié) 論

鑒于當(dāng)前設(shè)備故障信號(hào)檢測(cè)方法中存在的問(wèn)題,提出基于蟻群的復(fù)印機(jī)故障信號(hào)的檢測(cè)與提取方法。過(guò)程中,利用SVD理論對(duì)復(fù)印機(jī)中的噪聲信號(hào)進(jìn)行去除,通過(guò)蟻群算法對(duì)復(fù)印機(jī)故障信號(hào)進(jìn)行檢測(cè),采用小波包分析將檢測(cè)結(jié)果提取出來(lái)。實(shí)驗(yàn)表明,該方法具有較強(qiáng)的可實(shí)踐性。

參考文獻(xiàn)

[1] 吳魁,王仙勇,孫潔,等.基于深度學(xué)習(xí)的故障檢測(cè)方法[J].計(jì)算機(jī)測(cè)量與控制,2017,25(10):43?47.

WU Kui, WANG Xianyong, SUN Jie, et al. Fault detection method based on the deep belief network [J]. Computer measurement & control, 2017, 25(10): 43?47.

[2] 蘆竹茂,王天正,俞華,等.基于紅外圖像分析的電力設(shè)備熱故障檢測(cè)技術(shù)研究[J].現(xiàn)代電子技術(shù),2017,40(11):123?126.

LU Zhumao, WANG Tianzheng, YU Hua, et al. Research on electrical equipment thermal fault detection technology based on infrared image analysis [J]. Modern electronics technique, 2017, 40(11): 123?126.

[3] 崔芮華,王紹敏.基于多維特征量的航空串聯(lián)故障電弧檢測(cè)[J].科學(xué)技術(shù)與工程,2017,17(13):38?43.

CUI Ruihua, WANG Shaomin. Diagnosis of Arc faults in aviation AC system based on multi?dimensional features [J]. Science technology and engineering, 2017, 17(13): 38?43.

[4] 劉洋,歐文,盧贏,等.基于徑向基神經(jīng)網(wǎng)絡(luò)的稱重設(shè)備傳感器故障檢測(cè)方法[J].傳感技術(shù)學(xué)報(bào),2017,30(6):861?866.

LIU Yang, OU Wen, LU Ying, et al. Fault detection method for weighing equipment sensor based on radial basis function neural network [J]. Chinese journal of sensors and actuators, 2017, 30(6): 861?866.

[5] 夏輝麗,郭亞男,余發(fā)軍.基于稀疏分類算法的礦物傳送設(shè)備故障診斷方法[J].工礦自動(dòng)化,2016,42(2):43?46.

XIA Huili, GUO Yanan, YU Fajun. Fault diagnosis method of mineral transmission equipment based on sparse classification algorithm [J]. Industry and mine automation, 2016, 42(2): 43?46.

[6] 王迪,劉佳,王巖峰,等.基于多信號(hào)流模型的電子設(shè)備故障診斷方法[J].強(qiáng)激光與粒子束,2017,29(7):99?103.

WANG Di, LIU Jia, WANG Yanfeng, et al. Modeling method of fault diagnosis of electronic device based on multi?signal flow [J]. High power laser and particle beams, 2017, 29(7): 99?103.

[7] 孫海燕.大型機(jī)械設(shè)備振動(dòng)系統(tǒng)故障診斷仿真研究[J].科技通報(bào),2016,32(6):88?92.

SUN Haiyan. Fault diagnosis simulation of large mechanical equipment vibration system [J]. Bulletin of science and technology, 2016, 32(6): 88?92.

[8] 謝世滿.機(jī)械設(shè)備振動(dòng)信號(hào)采集對(duì)故障優(yōu)化檢測(cè)仿真[J].計(jì)算機(jī)仿真,2017,34(6):419?422.

XIE Shiman. Simulation of fault detection for mechanical equipment vibration signal acquisition [J]. Computer simulation, 2017, 34(6): 419?422.

[9] 高迎平,李洋,常文韜,等.基于模糊動(dòng)態(tài)故障樹的化工設(shè)備故障診斷方法研究[J].工業(yè)技術(shù)經(jīng)濟(jì),2017,36(4):48?54.

GAO Yingping, LI Yang, CHANG Wentao, et al. Research on failure diagnosis method of chemical equipment based on fuzzy dynamic fault tree [J]. Industrial technology & economy, 2017, 36(4): 48?54.

[10] 段佳雷.基于分段線性非飽和隨機(jī)共振的機(jī)械早期故障診斷方法研究[J].中國(guó)測(cè)試,2017,43(8):106?112.

DUAN Jialei. Study on incipient fault diagnosis of machinery based on piecewise linearity and unsaturated stochastic resonance [J]. China measurement & testing technology, 2017, 43(8): 106?112.

猜你喜歡
復(fù)印機(jī)蟻群
游戲社會(huì):狼、猞猁和蟻群
螞蟻:比人類更有組織性的動(dòng)物
基于自適應(yīng)蟻群的FCM聚類優(yōu)化算法研究
基于奇異值差分譜分析和蟻群算法的小波閾值降噪
復(fù)印機(jī)
立體復(fù)印機(jī)
復(fù)印機(jī)為什么能復(fù)印文件和圖片
小心復(fù)印機(jī)“出賣”你
復(fù)印機(jī)成“泄密炸彈”
電能質(zhì)量監(jiān)測(cè)系統(tǒng)中基于蟻群的WSN路由算法研究