CAJ | 학술논문

提出了一种滚动轴承故障诊断的新方法。首次将自适应最稀疏时频分析(ASTFA)方法应用于振动信号的降噪，并针对 KVPMCD 方法只选择一种最佳相关模型而忽略其他几种相关模型对预测精度贡献的缺陷，提出了一种改进的 KVPMCD 模式识别算法———人工鱼群算法优化融合 Kriging 模型的基于变量预测模型的模式识别(AKVPMCD)算法，即采用收敛速度快、鲁棒性强、具有全局寻优能力的人工鱼群智能算法(AFSIA)优化融合多种 Kriging 相关模型来提高模型预测精度。在此基础上，提出了一种基于 ASTFA 降噪和 AKVPMCD 算法的滚动轴承故障诊断方法。实验结果表明，该方法可以有效提高分类识别的精度。
제출료일충곤동축승고장진단적신방법。수차장자괄응최희소시빈분석(ASTFA)방법응용우진동신호적강조，병침대 KVPMCD 방법지선택일충최가상관모형이홀략기타궤충상관모형대예측정도공헌적결함，제출료일충개진적 KVPMCD 모식식별산법———인공어군산법우화융합 Kriging 모형적기우변량예측모형적모식식별(AKVPMCD)산법，즉채용수렴속도쾌、로봉성강、구유전국심우능력적인공어군지능산법(AFSIA)우화융합다충 Kriging 상관모형래제고모형예측정도。재차기출상，제출료일충기우 ASTFA 강조화 AKVPMCD 산법적곤동축승고장진단방법。실험결과표명，해방법가이유효제고분류식별적정도。
A new rolling bearing fault diagnosis method was proposed.ASTFA method was apG plied to the vibration signal de??noising for the first time.Aiming at the defects of KVPMCD(Kriging model variable predictive model based class discriminate)method that was chosen one of the best reG lated model only and the other correlation models’contribution to the prediction accuracy was igG nored,an improved KVPMCD pattern recognition algorithm AKVPMCD was proposed,the AFSIA (artificial fish swarm intelligence algorithm)which had high convergence speed,strong robustness and global optimization ability was used to optimize a variety of Kriging models,so as to improve the prediction precision.On the basis of above,a new fault diagnosis method of rolling bearings was proG posed based on ASTFA de??noising and AKVPMCD.The experimental results prove that this method can improve the precision of classification recognition effectively.