基于改进的动态独立成分分析故障检测方法

Fault detection method based on improved dynamic independent component analysis

针对滚动轴承早期故障特征微弱,且振动信号是一组随时间变化的序列,具有一定的时序相关性,导致滚动轴承早期故障检测难度增加的问题,提出了一种基于深度分解的动态独立成分分析(Deep DICA)故障检测方法。主要思想是首先增加观测数据矩阵,以便将动态过程考虑在内。然后,为了更好地挖掘出微弱的早期故障信息,提出了深度分解原理对早期故障进行特征提取。最后,建立故障检测模型进行在线故障检测,并通过轴承实验对所提出的方法进行了验证。实验结果表明,提出的基于Deep DICA的故障检测方法有很好的准确率和适用性。

Aiming at the problem that the incipient fault characteristics of rolling bearing are weak,and the vibration signal is a group of time-varying sequence,which has a certain time-series correlation,leading to the difficulty of incipient fault detection of rolling bearing,a dynamic independent component analysis fault detection method based on deep decomposition principle(Deep DICA)is proposed.The main idea is to first increase the observation data matrix in order to take the dynamic process into account.Then,in order to better dig out the weak incipient fault information,the principle of deep decomposition is proposed to extract the features of incipient faults.Finally,a fault detection model is established for online fault detection and the proposed method is verified by bearing experiments.Experimental results show that the proposed fault detection method based on Deep DICA has good accuracy and applicability.