本文選題：滑動軸承 + 狀態(tài)監(jiān)測��； 參考：《航空動力學(xué)報(bào)》2017年10期

【摘要】：從信息融合理論出發(fā),將特征的稀疏表達(dá)作為特征融合參數(shù),提出一種結(jié)合K奇異值分解(KSVD)和最大相關(guān)最小冗余準(zhǔn)則(mRMR)的軸承摩擦故障特征融合算法。該算法采用KSVD對信號稀疏化,將稀疏系數(shù)對應(yīng)的字典原子作為特征融合的參數(shù),用以表達(dá)非線性故障信息;針對字典原子集的優(yōu)化選擇問題,基于互信息的mRMR提出一種確定最優(yōu)原子集的原子數(shù)目的準(zhǔn)則;最后,通過最大化原則融合稀疏系數(shù),提取故障狀態(tài)監(jiān)測的有效信息。軸承摩擦故障模擬實(shí)驗(yàn)的結(jié)果表明,所提方法能夠更好地融合不同特征的故障信息,相比于單特征和其他融合特征方法,提高了約12%的故障識別率。
[Abstract]:Based on the information fusion theory, the sparse representation of features is taken as feature fusion parameters, and a feature fusion algorithm based on K singular value decomposition (KSVD) and maximum correlation minimum redundancy criterion (mRMR) is proposed. The algorithm uses KSVD to sparse signals and takes dictionary atoms corresponding to sparse coefficients as feature fusion parameters to express nonlinear fault information. Based on mutual information, mRMR proposes a criterion to determine the number of atoms in the optimal atomic set. Finally, the effective information of fault state monitoring is extracted by the principle of maximization fusion of sparse coefficients. The simulation results of bearing friction fault show that the proposed method can better fuse the fault information of different features and improve the fault identification rate by about 12% compared with the single feature and other fusion feature methods.
【作者單位】： 軍械工程學(xué)院車輛與電氣工程系;西南交通大學(xué)機(jī)械工程學(xué)院;
【基金】：國家自然科學(xué)基金(51205405,51305454)
【分類號】：TH133.31

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