【摘要】：傳統(tǒng)自適應(yīng)共振理論網(wǎng)絡(luò)模型利用硬競(jìng)爭(zhēng)機(jī)制對(duì)故障類邊界處的樣本進(jìn)行分類時(shí)易造成誤分類,為此,提出了基于軟競(jìng)爭(zhēng)Yu范數(shù)自適應(yīng)共振理論的軸承故障診斷方法。將基于模糊競(jìng)爭(zhēng)學(xué)習(xí)的軟競(jìng)爭(zhēng)方法引入Yu范數(shù)自適應(yīng)共振理論模型中,根據(jù)模式節(jié)點(diǎn)與輸入樣本間隸屬度的大小,對(duì)競(jìng)爭(zhēng)層多個(gè)節(jié)點(diǎn)進(jìn)行訓(xùn)練和學(xué)習(xí)。通過對(duì)軸承故障試驗(yàn)數(shù)據(jù)的診斷分析可知,該方法不但能有效識(shí)別不同類型的故障,而且能識(shí)別不同嚴(yán)重程度故障,且診斷精度優(yōu)于自適應(yīng)共振理論模型和模糊C均值聚類模型。
[Abstract]:The traditional network model of adaptive resonance theory makes use of hard competition mechanism to classify samples at fault class boundary which is prone to misclassification. Therefore a bearing fault diagnosis method based on soft competition Yu norm adaptive resonance theory is proposed. The soft competition method based on fuzzy competition learning is introduced into the Yu norm adaptive resonance theory model. According to the size of membership degree between the mode node and the input sample, multiple nodes in the competition layer are trained and learned. Through the diagnosis and analysis of bearing fault test data, it can be seen that this method not only can effectively identify different types of faults, but also can identify different severity of faults. The diagnostic accuracy is better than the adaptive resonance theory model and fuzzy C-means clustering model.
【作者單位】： 武漢科技大學(xué)機(jī)械自動(dòng)化學(xué)院;
【基金】：國家自然科學(xué)基金資助項(xiàng)目(51405353)
【分類號(hào)】：TH133.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前8條

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本文編號(hào)：2322812