关键词: 协同训练, 集成学习, 故障诊断, 置信度

Abstract:

Aiming at the problems of single classifier method in rolling bearing fault diagnosis, such as low accuracy, scarce fault sample mark and high feature space dimension, a co-forest bearing fault diagnosis algorithm combining collaborative training and ensemble learning is proposed. Co-forest is a collaborative training algorithm in semi-supervised learning, which contains multiple base classifiers and realizes confidence estimation in collaborative training through voting. The time domain and frequency domain characteristic indexes are extracted from the vibration signal of rolling bearing. A small number of labeled and large number of unlabeled samples are used to train the base classifier repeatedly. The fault diagnosis of rolling bearing is realized by integrating base classifier. Experimental results show that the co-forest algorithm has a higher accuracy in bearing fault diagnosis than the same type of cooperative training algorithm （co-training, tri-training）. Compared with the ISS-LPP algorithm and SS-LLTSA algorithm for the problem of high dimension of feature vectors and scarcity of labeled samples, the co-forest algorithm has certain practical application value under the condition of maintaining a high diagnostic accuracy rate without dimensional reduction and simple parameter setting.

Key words: cooperative training, ensemble learning, fault diagnosis, confidence