CAJ | 학술논문

提出了一种使用稀疏编码对机械频谱信号自学习并识别故障的方法。首先分别对每类频谱信号进行字典学习得到每类信号的字典，然后依次计算测试样本在各个类别的字典上的稀疏重构系数，利用稀疏重构系数与对应类别的字典重构测试样本。最后将重构残差作为识别依据，对机器状态进行判断。通过将振动信号从时域转化到频域，将复杂的移不变稀疏编码问题转化为普通的稀疏编码，并且得益于高效的 K-SVD 字典学习算法，计算效率得到了大幅提高。所提方案直接使用原始频谱信号作为训练集，不仅省去了特征提取过程，而且保留了更丰富的信息。经实验验证，该方案较基于时域的移不变稀疏编码具有更高的计算效率、准确率和稳定性。相对于常规诊断算法，除了有准确率的优势外，不易受负载变化的影响，所需人工干预较少。
제출료일충사용희소편마대궤계빈보신호자학습병식별고장적방법。수선분별대매류빈보신호진행자전학습득도매류신호적자전，연후의차계산측시양본재각개유별적자전상적희소중구계수，이용희소중구계수여대응유별적자전중구측시양본。최후장중구잔차작위식별의거，대궤기상태진행판단。통과장진동신호종시역전화도빈역，장복잡적이불변희소편마문제전화위보통적희소편마，병차득익우고효적 K-SVD 자전학습산법，계산효솔득도료대폭제고。소제방안직접사용원시빈보신호작위훈련집，불부성거료특정제취과정，이차보류료경봉부적신식。경실험험증，해방안교기우시역적이불변희소편마구유경고적계산효솔、준학솔화은정성。상대우상규진단산법，제료유준학솔적우세외，불역수부재변화적영향，소수인공간예교소。
An automatic learning and recognition scheme using sparse coding based on freqency domain signals was proposed here.Firstly,each dictionary for per class of frequency domain signals was obtained with a dictionary learning algorithm.Later,the test samples were sparselyly represented,respectively using the dictionaries of each class to calculate corresponding sparse reconstruction coefficients.Afterwards,the dictionaries with corresponding coefficients of the same class were applied to reconstruct the test samples.Finally,the reconstructed residual was taken as the criterion to determine machine states.Through converting vibration signals in time domain into those in frequency domain,a complex shift-invariant sparse coding problem,was simplified as an ordinary sparse coding one,and with the help of the effective K-SVD algorithm,the whole efficiency was further significantly improved.The original spectra singals were directly used as training samples in the proposed scheme,so that the complicated feature extraction was not needed,and more information was reserved.The test verification showed that the proposed technique improves greatly the efficiency and robustness compared to the shift invariant sparse coding in time-domain;compared with the traditional algorithms,besides the advantage of accuracy,this proposed scheme needs less cost and is affected less by load variation.