CDBN-IKELM的轴承变工况故障诊断方法

Bearing Fault Diagnosis Based on CDBN-IKELM Under Varying Conditions

针对现有方法在轴承变工况方面存在的诊断精度低、人工提取特征不充分等问题,提出了基于卷积深度置信网络(convolutional deep belief network,简称CDBN)与改进核极限学习机(improved Kernel-based extreme learning machine,简称IKELM)的滚动轴承故障智能识别方法。首先,由卷积深度置信网络对原始信号内的故障特征进行深层自适应提取;其次,利用等距特征映射对提取的多维特征进行降维,去除冗余特征信息;然后,采用改进的核极限学习机对特征进行分类,使用粒子群(particle swarm optimization,简称PSO)对模型重要参数进行优化,实现滚动轴承变工况下的故障识别;最后,将所提方法应用于不同工况下多种轴承故障的诊断。实验结果表明,该方法能够智能有效地识别变工况的轴承故障,诊断结果优于已有的智能故障诊断方法。

The intelligent diagnosis method has been successfully applied in the field of mechanical equipment bearing fault recognition. Aiming at the problems of low diagnosis accuracy and insufficient manual feature extraction of existing methods,an intelligent recognition method based on convolutional deep belief network and improved kernel-based extreme learning machine(CDBN-IKELM)is proposed. Firstly,the convolutional deep belief network(CDBN)is used to extract the fault features from the original signal,and then isometric feature mapping is adopted to reduce the dimension of the extracted multi-dimensional features for removing redundant feature information. Finally,improved kernel-based extreme learning machine(IKELM)is utilized to classify rolling bearing fault under variable conditions,and particle swarm optimization(PSO)is used for optimizing the important parameters of the model. Through the identification of various bearing faults under different working conditions,it is verified that the method can effectively diagnose the bearing faults under varying conditions with high accuracy,and the effect is better than existing intelligent fault diagnosis methods.