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提出了一种利用KS检验对机械故障进行分类的新方法。通过仿真试验和齿轮故障诊断,说明该方法在数据样本含有一定噪声时也能正确判断故障。在利用少量轴承时域故障数据样本建立多故障分类系统后,仅仅需要极短时间就能准确分类多种故障。结果表明,该方法具有很好的分类能力和较高的计算效率,完全可以满足智能故障诊断的要求。
A new method to classify mechanical faults by KS test is proposed. Through the simulation test and gear fault diagnosis, it shows that this method can judge the fault correctly when the data sample contains some noise. After establishing a multi-fault classification system using a small number of bearing time domain fault data samples, it is possible to classify multiple faults accurately in just a fraction of the time. The results show that this method has good classification ability and high computational efficiency, which can fully meet the requirements of intelligent fault diagnosis.