Research on Mechanical Fault Recognition Method Based on TUCKER-DBN

Aiming at the shortcomings of traditional deep belief network (DBN), such as low classification accuracy, slow training speed, and only being suitable for one-dimensional signals, a new fault identification method was proposed by combining DBN with TUCKER decomposition. Firstly, the method uses TUCKER to decompose the compressed data, extracts its core tensor as fault features, and then inputs the core tensor into the DBN classifier for training and recognition. This method was compared with the traditional DBN fault identification method. Among the 120 samples collected, 30 ones were selected for fault identification test. The results show that the recognition rate of TUCKER-DBN is 93%, which is higher than that of the traditional DBN method. Moreover, the training time with TUCKER-DBN recognition method is shorter than that of traditional DBN fault recognition method.