本文選題：S變換　切入點(diǎn)：隱馬爾科夫模型　出處：《計(jì)算機(jī)與應(yīng)用化學(xué)》2016年02期

【摘要】：軸承的故障信號(hào)特征提取和故障的識(shí)別在機(jī)械化生產(chǎn)中具有重要的意義,對(duì)此提出了基于S變換特征提取和隱馬爾科夫模型的故障診斷方法。為了獲取所需的故障特征信息,首先對(duì)采集到的軸承信號(hào)進(jìn)行S變換,并對(duì)變換結(jié)果進(jìn)行奇異值分解,提取信號(hào)特征。將獲取到的奇異值構(gòu)造成信號(hào)特征矩陣,用于建立隱馬爾科夫的故障識(shí)別模型。試驗(yàn)的結(jié)果證明了本文的方法在軸承的故障檢測(cè)中的有效性。
[Abstract]:Bearing fault signal feature extraction and fault identification are of great significance in mechanized production. A fault diagnosis method based on S-transform feature extraction and hidden Markov model is proposed.In order to obtain the necessary fault feature information, the bearing signal is firstly transformed by S transform, and the result is decomposed by singular value to extract the signal feature.The obtained singular values are constructed into a signal feature matrix, which is used to establish a hidden Markov fault identification model.The experimental results show that the proposed method is effective in bearing fault detection.
【作者單位】： 昆明理工大學(xué)信息工程與自動(dòng)化學(xué)院;云南省礦物管道輸送工程技術(shù)研究中心;
【基金】：國(guó)家自然科學(xué)基金資助項(xiàng)目(51169007) 云南省科技計(jì)劃項(xiàng)目(2012CA022,2013DH034) 云南省中青年學(xué)術(shù)和技術(shù)帶頭人后備人才培養(yǎng)計(jì)劃項(xiàng)目(2011CI017)
【分類號(hào)】：TH133.3
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本文編號(hào)：1716562