基于双加权不平衡矩阵分类器的机械故障诊断方法

Mechanical Fault Diagnosis Method Based on Twin Weighted Imbalanced Matrix Classifier

针对机械故障样本数量不平衡情景下的故障诊断模型存在精度与泛用性不高的问题,借鉴模糊属性理论获取强监督模型的思想,设计了一种双加权不平衡矩阵分类器(Twin weighted imbalanced matrix classifier,TWIMC)。TWIMC通过使用基于样本不均衡度的模糊隶属函数调节每个样本的权重,以增强对少数类样本的关注,平衡模型对所有类别样本的倾向性。同时,TWIMC依靠先验知识对核范数的奇异值进行权值分配,利用较大阈值过滤较小奇异值,进而保留矩阵样本的强关联低秩信息。最后,利用滚动轴承和齿轮故障数据集对所提方法进行验证,实验结果显示,TWIMC在不同不平衡比条件下均表现突出,展示了优异的机械故障诊断与分类性能。

Aiming at the problem of low accuracy and universality of fault diagnosis models in the scenario of imbalanced sample size for mechanical faults,a twin weighted imbalanced matrix classifier(TWIMC)is designed,drawing on the idea of obtaining a strong supervised model using fuzzy attribute theory.TWIMC adjusts the weight of each sample using a fuzzy membership function based on the degree of sample imbalance,thereby enhancing the focus on minority class samples and balancing the model's tendency towards all types of samples.Meanwhile,TWIMC utilizes prior knowledge to assign weights to the singular values of nuclear norm,preserving the strongly correlated low-rank information of matrix samples by filtering out smaller singular values with a larger threshold.Finally,the proposed method is validated using roller bearing and gear fault datasets.The experimental results showed that TWIMC performed outstandingly under different imbalance ratios,demonstrating excellent mechanical fault diagnosis and classification performance.