【摘要】：提出了基于人工化學反應優(yōu)化算法(artificial chemical reaction optimization algorithm,ACROA)的自適應最稀疏窄帶分解(adaptive sparsest narrow-band decomposition,ASNBD)方法,將信號分解轉(zhuǎn)化為對濾波器參數(shù)的優(yōu)化問題,使用ACROA進行優(yōu)化,以得到信號的最稀疏解為優(yōu)化目標,在優(yōu)化過程中將信號自適應地分解成若干個具有物理意義的局部窄帶信號。對數(shù)值仿真和齒輪故障數(shù)據(jù)進行分析,結(jié)果表明該方法在抑制模態(tài)混淆、抗噪聲性能、提高分量的正交性和準確性等方面要優(yōu)于ASTFA方法、基于遺傳算法(genetic algorithm,GA)的ASNBD方法及總體平均經(jīng)驗模態(tài)分解(ensemble empirical mode decomposition,EEMD)方法,并能有效識別出齒輪的典型故障。
[Abstract]:An adaptive narrow-band decomposition (adaptive sparsest narrow-band decomposition,ASNBD) method based on the artificial chemical reaction optimization algorithm (artificial chemical reaction optimization algorithm,ACROA) is proposed. The signal decomposition is transformed into the optimization of filter parameters and optimized by ACROA. In order to obtain the sparse solution of the signal as the optimization objective, the signal is decomposed adaptively into a number of local narrow band signals with physical significance during the optimization process. The numerical simulation and gear fault data are analyzed. The results show that the proposed method is superior to ASTFA method in suppressing modal confusion, anti-noise performance, improving the orthogonality and accuracy of components, and based on genetic algorithm (genetic algorithm,. The ASNBD method of GA and the method of total average empirical mode decomposition (ensemble empirical mode decomposition,EEMD) can effectively identify the typical faults of gears.
【作者單位】： 湖南大學汽車車身先進設計制造國家重點實驗室;
【基金】：國家重點研發(fā)計劃項目(2016YFF0203400) 國家自然科學基金資助項目(51375152,51575168) 智能型新能源汽車國家2011協(xié)同創(chuàng)新中心 湖南省綠色汽車2011協(xié)同創(chuàng)新中心資助項目
【分類號】：TH132.41
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本文編號：2369795