机械故障稀疏特征相似性度量优化研究

Research on Similarity Measure of Sparse Feature of Mechanical Fault Based on Quantum Genetic Algorithm Optimization

针对当前机械故障诊断研究忽略了对其参数的选取与优化,导致准确性较差等问题,提出基于量子遗传算法优化的机械故障稀疏特征相似性度量方法。基于先进行信号非线性混合,再进行去混合。将峭度作为目标函数,利用量子遗传算法,对盲源分离过程的分离矩阵参数与非线性去混合参数进行优化,实现机械故障盲源分离。基于故障信号处理,利用量子遗传算法与最小二乘支持向量机(LSSVM:Least Squares Support Vector Machine)相结合实现机械故障稀疏特征相似性度量。当LSSVM在机械故障诊断时对模型参数选取,利用量子遗传算法针对LSSVM模型参数进行优化。将LSSVM参数选取问题转换为优化问题,利用优化后的LSSVM分类模型实现机械故障稀疏特征相似模式分类。实验结果表明,该方法可以实现高效盲源分离,机械故障诊断准确率高,运行性能良好。

Aiming at the problem that the selection and optimization of mechanical fault parameters are neglected in the current research on mechanical fault diagnosis,which leads to poor accuracy,this paper proposes a sparse feature similarity measurement method for mechanical fault based on quantum genetic algorithm optimization.According to the first signal nonlinear mixing,then demixing.With kurtosis as the objective function and quantum genetic algorithm,the separation matrix parameters and nonlinear de-mixing parameters of blind source separation are optimized to realize blind source separation of mechanical faults.Based on fault signal processing,the similarity measurement of mechanical fault sparse features is realized by combining quantum genetic algorithm with LSSVM(Least Squares Support Vector Machine).LSSVM selects the model parameters during the mechanical fault diagnosis,the quantum genetic algorithm is used to optimize the model parameters of LSSVM.The parameter selection problem of LSSVM is transformed to an optimization problem,and the optimized LSSVM classification model is used to realize the classification of mechanical fault sparse feature similarity mode.The experimental results show that the method can achieve high efficiency blind source separation,high accuracy in mechanical fault diagnosis and good performance.