CAJ | 학술논문

针对齿轮在磨损过程中的磨损程度，可以用振动信号来表征，并通过对磨损过程中振动信号的预测来实现磨损预测，提出了一种基于小波核的支持向量机磨损预测算法。首先，分析了最小二乘小波在磨损预测中建模方法，其核函数采用小波核，改善了系统非线性性能；然后用量子行为粒子群优化算法（ QPSO）优化SVM参数，具有较快的搜索速度并保持了时间序列的特征。验证实验中用齿轮箱振动信号的统计指标表征齿轮磨损状态。实验结果表明，该预测方法能够有效地进行齿轮磨损预测。
침대치륜재마손과정중적마손정도，가이용진동신호래표정，병통과대마손과정중진동신호적예측래실현마손예측，제출료일충기우소파핵적지지향량궤마손예측산법。수선，분석료최소이승소파재마손예측중건모방법，기핵함수채용소파핵，개선료계통비선성성능；연후용양자행위입자군우화산법（ QPSO）우화SVM삼수，구유교쾌적수색속도병보지료시간서렬적특정。험증실험중용치륜상진동신호적통계지표표정치륜마손상태。실험결과표명，해예측방법능구유효지진행치륜마손예측。
Aimed at the wear process of gear wear, vibration signals were able used to characterize wear intensity, wear prediction was able achieved by the prediction of vibration signals, a wear prediction algorithm based on wavelet kernel support vector machine ( SVM) was proposed. Firstly, the least square wavelet modeling method in the wear prediction was analyzed, and wavelet kernel was used as the kernel function to improve the nonlinear performance of the system. Then the SVM parameters were optimized by the Quan?tum?behaved Particle Swarm Optimization ( QPSO) , and the system possessed faster searching speed and maintained the characteristics of time series. The statistical indicators of the vibration of gear box were used in validation experiment to characterize gear wear intensi?ty. The results of the experiment show that the prediction method can effectively predict gear wear.