Acoustic emission signals recognition for rolling bearing fault based on GA-IDBN
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摘要: 针对变工况条件下的滚动轴承故障声发射信号识别问题，提出了一种基于遗传算法（Genetic Algorithm，GA）与改进深度信念网络（Improved Deep Belief Network，IDBN）结合的故障检测与诊断新方法。对滚动轴承故障声发射监测信号的分析结果表明，GA-IDBN模型对滚动轴承的外圈、内圈、保持架故障的声发射监测信号的识别准确率明显优于DBN （深度信念网络）、支持向量机等模型的，识别准确率可达到95.5%；并且，GA-IDBN模型具有很强的普适性，可以识别出滚动轴承在不同通道、不同转速情况下的运行状态。证明了GA-IDBN模型具有很强的工程实用价值。Abstract: A new method of fault diagnosis and detection based on GA and IDBN is proposed to identify the acoustic emission signals of rolling bearing faults under variable working conditions. The experimental results show that the GA-IDBN model is superior to the original deep belief network and support vector machine model in the recognition accuracy of the acoustic emission monitoring signals of the outer ring fault and the inner ring fault and cage fault of rolling bearings.And the accuracy can reach more than 95.5%. Moreover, GA-IDBN model is universal, and can recognize the running state of different channel and speed. It is proved that GA-IDBN model has a strong practical value in engineering.
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