To meet the needs of fault identification and imbalanced dataset in actual industrial processes

a semisupervised deep learning method was proposed for fault classification. The enhanced semi-supervised ladder network

i.e.

semi-supervised dense ladder network

was developed by improving network architecture and loss function. Dense connection strategy was applied in the network architecture to maximize information flow between layers of the ladder network

such that features among layers could be transmitted and copied. Meanwhile

to ensure consistency between training target and prediction output

prediction output loss of corrupted encoder was added into original loss function. Experiment results illustrated the proposed method could achieve ideal classification in small ratio marked dataset in actual industrial process.