LAUSR

In the process of mechanical equipment fault diagnosis, it is difficult to obtain enough labeled samples due to the changeable operating conditions, complex working environment, and the limitation of measuring equipment. Therefore, a prototype clustering method based on unsupervised domain alignment is proposed for fault detection and diagnosis of mechanical equipment under unmarked samples. First, the relative maximum mean discrepancy index is constructed to map the data collected from different devices to the same high-dimensional space for feature alignment. Then, cluster the output and calculate the prototype for each category. Compared with the classification directly using the softmax function, the prototype clustering method can correct the local possible misjudged points through the overall average distribution. By minimizing the global distance error, prototypes obtained by clustering in the target domain are matched with prototypes of tagged samples in the source domain and, finally, realize unsupervised state recognition. Six experiments are designed to verify the effectiveness of the proposed method. The accuracy of the proposed method is 1%–2% higher even when compared to one of the most advanced fault diagnosis methods available. Also, it can be found from the results of t-stochastic neighbor embedding (SNE) visualization that the proposed method has higher knowledge transfer capability.