CAJ | 학술논문

提出了基于统计分析的时域信号稀疏化算法，对发动机的转速等在时域内表现出一定聚集性的信号进行稀疏化处理；利用提升小波变换对油温、水温等在时域内表现出趋势性的信号进行稀疏化处理，并对小波系数进行阈值量化，得到了发动机状态数据的稀疏表示。采用工程上更易实现的广义循环测量矩阵对稀疏信号进行采样观测。对5种实测信号的压缩重构效果表明，重构误差在0．02以内，信号的压缩比均在0．3以内，有良好的压缩效果。
제출료기우통계분석적시역신호희소화산법，대발동궤적전속등재시역내표현출일정취집성적신호진행희소화처리；이용제승소파변환대유온、수온등재시역내표현출추세성적신호진행희소화처리，병대소파계수진행역치양화，득도료발동궤상태수거적희소표시。채용공정상경역실현적엄의순배측량구진대희소신호진행채양관측。대5충실측신호적압축중구효과표명，중구오차재0．02이내，신호적압축비균재0．3이내，유량호적압축효과。
The time‐domain signal sparse algorithm based on the statistical analysis was proposed in order to process engine speed signal with time‐domain aggregation characteristics .The lifting wavelet transform was used for the sparse processing of trend signals such as the oil temperature and water temperature and the threshold quantization of wavelet coefficients was car‐ried out .Finally ,the sparse representation of engine status data was acquired .The sparse signal was sampled and observed with the generalized cyclic measurement matrix which can be realized easily in engineering .The results of compression and re‐construction for 5 kinds of test signals show that the reconstruction error is less than 0 .02 and the signal compression ratio is w ithin 0 .3 .