ADMOW方法及其在滚动轴承状态识别中的应用

ADMOW Pattern Recognition Method and its Application on State Recognition of Rolling Bearings

针对滚动轴承振动信号的状态特征及特征数据中存在'异常值'的现象,提出了基于优化加权代理判别模型(Agent discriminate model based optimization weighted,ADMOW)的模式识别方法。该方法首先通过计算样本特征值的类相似度对特征值进行评价,并依据评价结果对特征值赋予权值,以此弱化'异常值'导致模型出现偏差的问题;然后利用粒子群优化(Particle swarm optimization,PSO)算法对所建立的模型参数进行优化,得到更加准确可靠的分类模型;最后采用建立的优化加权代理预测模型对待测样本进行识别。滚动轴承实验结果表明,与以往的模式识别方法相比,该方法能有效地提高识别准确率。

Taken the state characteristic of vibration signal of rolling bearing and the'outliers'among the feature values into consideration,a new pattern recognition method,namely agent discriminate model based optimization weighted(ADMOW),is proposed in this paper.Firstly,according to its class similarity,the sample eigenvalues are evaluated.Based on the evaluation result,the eigenvalues are weighted to eliminate the'outliers'resulting in model deviation.The established model is secondly optimized by the particle swarm optimization(PSO)algorithm,hence a more accurate and reliable classification model is correspondingly obtained.Eventually,ADMOW is applied to recognize faults of rolling bearings.The experimental and analytical results indicate that this proposed method can effectively promote the recognition accuracy,compared with the previous pattern recognition methods.