广义形态差值滤波与AN降维在故障诊断中的应用

Application of generalized morphological difference filtering and dimension reduction with AN in fault diagnosis

由于轴承与设备其他内部构件之间存在强关联耦合关系,导致其振动信号与设备状态存在非线性关系;且信号单一特征难以全面描述设备状态,而多特征虽然包含较多状态信息,但高维特征所产生的信号冗余问题,易导致模型分类精度的下降.因此,提出一种基于广义形态差值滤波(GDIF)与自编码网络(AN)的滚动轴承故障诊断方法.该方法利用广义形态差值滤波对振动信号进行降噪处理,并通过极大似然估计(MLE)与AN从信号的高维特征中获取低维本质流形,缓解高维特征存在的维数灾难问题;最后,建立极限学习机(ELM)故障诊断模型,对轴承故障类型进行识别。轴承试验结果表明,该方法能够有效对信号进行降噪;通过AN对特征进行维数约简,能够使ELM模型分类精度达到98.04%。

The strong coupling between the bearing and other internal components of the equipment leads to nonlinear relationship between vibration signal and equipment state. Moreover, the single signal feature is difficult to describe the state of the equipment comprehensively, while multi-features contain more status information, the signal redundancy caused by high-dimensional features easily declines the classification accuracy of the model. Therefore, a rolling bearings fault diagnosis method based on generalized morphological difference filter(GDIF) and autoencoder network(AN) is proposed. This method uses the GDIF to reduce the noise of vibration signals, and obtains the low-dimensional intrinsic manifold from the high-dimensional features of the signal by the max likelihood estimate(MLE) and AN algorithm, which alleviates the dimension disasters of high-dimensional features. Finally, the extreme learning machine(ELM) fault diagnosis model is established to identify the bearing fault types. The experiments show that the method can effectively suppress the noise;and the classification accuracy of the ELM Model can reach 98.04% after dimension reduction of features by AN.