Application of Full Vector Deep Learning in Bearing Fault Diagnosis - Chen Chaoyu; Chen Lei; Zhang Wang; Han Jie (Jixie chuandong)
To handle the numerous and jumbled data from fault monitoring systems,considering information missing with the single channel signal and the complexity and non generality of traditional intelligent diagnostic manual extracting features,a method named full vector deep learning in intelligent fault diagnosis of rolling bearing is put forward. Firstly,full vector spectrum is used to fuze the binary channel signal,the main vibration vector data after full vector fusion is acquired,the disadvantage of incomplete single channel vibration signal is overcome. Then,a full-vector deep neural network is built on this basis,combining sparse coding and de-noising coding algorithm,the fault features can be extracted automatically. Finally,the back-propagation algorithm is used to fine-tune the whole network. Experimental results show that the presented method can extract more effective fault features automatically,the classification accuracy and stability of diagnosis are improved,and the complex process of traditional methods is improved.