CAJ | 학술논문

提出了基于量子遗传算法（QGA）优化相关向量机（RVM）核函数参数的方法，通过仿真比较了量子遗传算法与其它方法在核函数参数优化方面的性能，结果表明基于量子遗传算法优化出的算法性能优于其它方法的优化性能。将基于量子遗传算法优化的相关向量机（QGA-RVM）应用于滚动轴承的故障诊断；采用总体平均经验模态分解（EE-MD）将滚动轴承故障信号自适应地分解成多个内禀模态函数（IMF），将 IMF 能量作为故障特征输入到 QGA-RVM进行最终的故障诊断。结果表明，该方法能够快速准确地诊断出滚动轴承故障，验证了该方法的有效性和稳定性；此外，通过与支持向量机（SVM）的对比分析，显示了 RVM在智能故障诊断应用中的优越性。
제출료기우양자유전산법（QGA）우화상관향량궤（RVM）핵함수삼수적방법，통과방진비교료양자유전산법여기타방법재핵함수삼수우화방면적성능，결과표명기우양자유전산법우화출적산법성능우우기타방법적우화성능。장기우양자유전산법우화적상관향량궤（QGA-RVM）응용우곤동축승적고장진단；채용총체평균경험모태분해（EE-MD）장곤동축승고장신호자괄응지분해성다개내품모태함수（IMF），장 IMF 능량작위고장특정수입도 QGA-RVM진행최종적고장진단。결과표명，해방법능구쾌속준학지진단출곤동축승고장，험증료해방법적유효성화은정성；차외，통과여지지향량궤（SVM）적대비분석，현시료 RVM재지능고장진단응용중적우월성。
A novel method to optimize relevance vector machine (RVM)'s kernel function parameters based on the quantum genetic algorithm (QGA)was proposed.It was compared with other optimization algorithms with simulations. The results showed that the optimization method based on QGA is superior to other optimization methods.The model of RVM optimized with QGA (QGA-RVM)was applied in fault diagnosis of rolling bearings.Fault signals were decomposed adaptively into some intrinsic mode functions (IMFs)with the ensemble empirical mode decomposition (EEMD).The IMF energy as fault features was inputted into QGA-RVM for final fault diagnosis.Experimental results showed that the proposed method can diagnose rolling bearings'faults rapidly and accurately,its validity and stability are verified;moreover,the superiority of RVMin intelligent fault diagnosis is revealed through the comparative analysis between QGA-RVMand SVM.