基于最大異類距離和正則極端學(xué)習(xí)機(jī)的模擬電路在線故障診斷
發(fā)布時(shí)間：2018-09-06 10:06
【摘要】：針對(duì)模擬電路在線故障診斷問題,提出了一種將最大異類距離和正則極端學(xué)習(xí)機(jī)結(jié)合起來的新方法。首先,利用最大異類距離對(duì)初始特征樣本進(jìn)行特征提取,壓縮樣本規(guī)模,獲取維數(shù)更低、可分性更好的特征樣本集;然后,將提取的特征樣本送入正則極端學(xué)習(xí)機(jī)進(jìn)行訓(xùn)練,再利用訓(xùn)練好的正則極端學(xué)習(xí)機(jī)對(duì)待測(cè)電路實(shí)時(shí)狀態(tài)進(jìn)行診斷,并不斷根據(jù)診斷結(jié)果和新舊樣本之間的相似度更新訓(xùn)練樣本集;最后,將所提方法用于模擬電路在線診斷中。結(jié)果表明:所提方法能夠有效實(shí)現(xiàn)模擬電路單、雙故障的在線診斷,比ELM效果好。
[Abstract]:In order to solve the problem of on-line fault diagnosis of analog circuits, a new method which combines the maximum outlier distance with the regular extreme learning machine is proposed. Firstly, the initial feature sample is extracted by using the maximum heterogeneity distance, and the sample size is compressed to obtain the feature sample set with lower dimension and better separability. Then, the extracted feature sample is sent to the regular extreme learning machine for training. Then the trained regular extreme learning machine is used to diagnose the real time state of the test circuit, and the training sample set is constantly updated according to the diagnosis results and the similarity between the new and the old samples. Finally, the proposed method is applied to the on-line diagnosis of analog circuits. The results show that the proposed method can effectively realize the on-line diagnosis of single and double faults of analog circuits, and the effect is better than that of ELM.
【作者單位】： 綏化學(xué)院電氣工程學(xué)院;華南農(nóng)業(yè)大學(xué)電子工程學(xué)院;
【基金】：廣東省高等學(xué)校優(yōu)秀青年教師培養(yǎng)計(jì)劃資助項(xiàng)目(Yq2013028) 現(xiàn)代農(nóng)業(yè)產(chǎn)業(yè)技術(shù)體系建設(shè)專項(xiàng)資金(CARS-27) 黑龍江省高等學(xué)校教改工程項(xiàng)目(JG2014011118)
【分類號(hào)】：TN710;TP181
[Abstract]:In order to solve the problem of on-line fault diagnosis of analog circuits, a new method which combines the maximum outlier distance with the regular extreme learning machine is proposed. Firstly, the initial feature sample is extracted by using the maximum heterogeneity distance, and the sample size is compressed to obtain the feature sample set with lower dimension and better separability. Then, the extracted feature sample is sent to the regular extreme learning machine for training. Then the trained regular extreme learning machine is used to diagnose the real time state of the test circuit, and the training sample set is constantly updated according to the diagnosis results and the similarity between the new and the old samples. Finally, the proposed method is applied to the on-line diagnosis of analog circuits. The results show that the proposed method can effectively realize the on-line diagnosis of single and double faults of analog circuits, and the effect is better than that of ELM.
【作者單位】： 綏化學(xué)院電氣工程學(xué)院;華南農(nóng)業(yè)大學(xué)電子工程學(xué)院;
【基金】：廣東省高等學(xué)校優(yōu)秀青年教師培養(yǎng)計(jì)劃資助項(xiàng)目(Yq2013028) 現(xiàn)代農(nóng)業(yè)產(chǎn)業(yè)技術(shù)體系建設(shè)專項(xiàng)資金(CARS-27) 黑龍江省高等學(xué)校教改工程項(xiàng)目(JG2014011118)
【分類號(hào)】：TN710;TP181
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【共引文獻(xiàn)】
相關(guān)期刊論文 前10條
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