机械工程学报
機械工程學報
궤계공정학보
CHINESE JOURNAL OF MECHANICAL ENGINEERING
2015年
3期
95-103
,共9页
曾鸣%杨宇%郑近德%程军圣
曾鳴%楊宇%鄭近德%程軍聖
증명%양우%정근덕%정군골
降噪%μ-奇异值分解%滤值因子%故障诊断%齿轮
降譟%μ-奇異值分解%濾值因子%故障診斷%齒輪
강조%μ-기이치분해%려치인자%고장진단%치륜
noise reduction%μ-singular value decomposition%filter factor%fault diagnosis%gear
为了提取机械设备被强背景噪声淹没的故障特征,采用一种具有通用意义的基于奇异值分解(Singular value decomposition, SVD)的子空间降噪算法对信号进行处理,即μ-SVD降噪算法。传统的SVD降噪算法是μ-SVD降噪算法中拉格朗日乘子μ=0时的一种特殊情况。μ-SVD降噪算法包含滤值因子,能够抑制以噪声贡献占主导的奇异值对降噪后信号的信息贡献量。μ-SVD 降噪算法涉及延迟时间、嵌入维数、降噪阶次、噪声功率和拉格朗日乘子等5个参数。讨论了μ-SVD降噪算法的参数选择方法,并着重研究降噪阶次和拉格朗日乘子对降噪效果的影响。齿轮故障仿真信号和齿轮早期裂纹故障振动信号的试验结果表明,μ-SVD降噪算法在降噪效果方面要优于传统的SVD降噪算法,可以在强背景噪声情况下更好地提取出齿轮的故障特征。
為瞭提取機械設備被彊揹景譟聲淹沒的故障特徵,採用一種具有通用意義的基于奇異值分解(Singular value decomposition, SVD)的子空間降譟算法對信號進行處理,即μ-SVD降譟算法。傳統的SVD降譟算法是μ-SVD降譟算法中拉格朗日乘子μ=0時的一種特殊情況。μ-SVD降譟算法包含濾值因子,能夠抑製以譟聲貢獻佔主導的奇異值對降譟後信號的信息貢獻量。μ-SVD 降譟算法涉及延遲時間、嵌入維數、降譟階次、譟聲功率和拉格朗日乘子等5箇參數。討論瞭μ-SVD降譟算法的參數選擇方法,併著重研究降譟階次和拉格朗日乘子對降譟效果的影響。齒輪故障倣真信號和齒輪早期裂紋故障振動信號的試驗結果錶明,μ-SVD降譟算法在降譟效果方麵要優于傳統的SVD降譟算法,可以在彊揹景譟聲情況下更好地提取齣齒輪的故障特徵。
위료제취궤계설비피강배경조성엄몰적고장특정,채용일충구유통용의의적기우기이치분해(Singular value decomposition, SVD)적자공간강조산법대신호진행처리,즉μ-SVD강조산법。전통적SVD강조산법시μ-SVD강조산법중랍격랑일승자μ=0시적일충특수정황。μ-SVD강조산법포함려치인자,능구억제이조성공헌점주도적기이치대강조후신호적신식공헌량。μ-SVD 강조산법섭급연지시간、감입유수、강조계차、조성공솔화랍격랑일승자등5개삼수。토론료μ-SVD강조산법적삼수선택방법,병착중연구강조계차화랍격랑일승자대강조효과적영향。치륜고장방진신호화치륜조기렬문고장진동신호적시험결과표명,μ-SVD강조산법재강조효과방면요우우전통적SVD강조산법,가이재강배경조성정황하경호지제취출치륜적고장특정。
In order to extract machinery fault characteristics that are submerged in strong background noise, a general singular value decomposition (SVD) based subspace noise reduction algorithm is applied to signal processing, i.e.,μ-SVD based denoising method. It can be proved that the traditional SVD based denoising method is a special case of theμ-SVD based one whereμ=0.μ-SVD based denoising methodcontains a filter factor that plays a role in restraining information contributions of the noise-domain singular values to the denoised signal.μ-SVD based denoising method involves five parameters, including delay time, embedding dimension, noise reduction order, noise power and Lagrange multiplier. The selection methods for these parameters are discussed. In particular, the effects of noise reduction order and Lagrange multiplier on denoising performance are also studied. The experimental results of simulation signal with local fault and vibration signal with early crack fault in gear demonstrate that theμ-SVD based denoising method is superior to the traditional one in denoising performance, and can more effectively extract the gear fault characteristics at the presence of strong background noise.