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Compound-Fault Diagnosis of rolling bearing based on Order Wavelet Packet and Rough Sets Theory

机译:基于订单小波包的滚动轴承复合故障诊断与粗糙集理论

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The diagnosis of compound-fault is always a difficult point, and there is not an effective method in equipment diagnosis field, and then a new method of compound-fault diagnosis was presented in the paper. The vibration signals at start-up in the gearbox are non-stationary signals, in order to solve the compound-fault diagnosis of rolling bearing, the order tracking wavelet packet and rough sets theory are combined and introduced into the fault diagnosis field. Firstly, the non-stationary vibration signals at start-up were resampled using computer order tracking arithmetic and equal angle distributed vibration signals were obtained, and then the signals were decomposed and recomposed with the wavelet packet. The energy distribution of every frequency band was calculated according to normalization process. A new feature vector can be obtained, then clear and concise decision rules can be obtained by rough sets theory. Finally, the result of compound-fault example proves that the proposed method has high validity and more amplitude appliance foreground.
机译:复合故障的诊断始终是一个难点,并且在设备诊断场中没有有效的方法,然后在纸上提出了一种新的复合故障诊断方法。齿轮箱启动时的振动信号是非静止信号,以解决滚动轴承的复合故障诊断,顺序跟踪小波包和粗糙集理论被组合并引入故障诊断场。首先,使用计算机订单跟踪算术和相等的角度分布振动信号重新采样启动时的非静止振动信号,然后用小波包分解信号并将信号分解并重新编译。根据归一化过程计算每个频带的能量分布。可以获得新的特征向量,然后可以通过粗糙集理论获得清晰和简明的决策规则。最后,复合故障示例的结果证明了该方法具有高有效性和更多幅度设备前景。

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