CAJ | 학술논문

针对海量冗余数据中设备状态信息特征提取问题，借鉴生物感知系统“冗余度压缩”的信息处理原则，基于神经科学研究中的稀疏编码算法，提出了连续长时间采样时振动信号有效特征提取方法。介绍了稀疏编码算法及其模型，详细研究了稀疏编码的系数求解和字典学习两大问题。基于人工轴承故障数据集进行了实验研究，实验表明：基于稀疏编码的振动信号特征提取算法不仅能有效提取设备状态特征，而且稀疏特征具有良好的可分性。该方法可用于设备故障诊断，为基于状态的设备智能维护提供有效工具。
침대해량용여수거중설비상태신식특정제취문제，차감생물감지계통“용여도압축”적신식처리원칙，기우신경과학연구중적희소편마산법，제출료련속장시간채양시진동신호유효특정제취방법。개소료희소편마산법급기모형，상세연구료희소편마적계수구해화자전학습량대문제。기우인공축승고장수거집진행료실험연구，실험표명：기우희소편마적진동신호특정제취산법불부능유효제취설비상태특정，이차희소특정구유량호적가분성。해방법가용우설비고장진단，위기우상태적설비지능유호제공유효공구。
To solve the problem of status information feature’s extraction from mass redundant data in a device,an effective feature extraction method of vibration signals long continuously sampled was presented.This method was based on the principle of information processing in a biological perceptual system’s redundancy compression,and the sparse coding algorithm in neuroscience studying.The two problems of the sparse coding algorithm (coefficient solving and dictionary learning)were studied in detail.Tests for the proposed method were accomplished based on artificial bearing fault data sets.The results indicated that the proposed vibration signal feature extraction method can not only effectively extract status characteristics from a device’s information,but also have a good separability from sparse features;this method can be used for an equipment’s fault diagnosis,and also it provides an effective tool for intelligent maintenance of equipments based on their status.