Abstract
Artificial neural network (ANN) mimics the operation of the biological neural network system, and using the mathematical functions to determine the artificial neuron excitation. Auto-encoder is one of the most important unsupervised learning in artificial neural networks which aims to learn a hierarchy of feature representations from input data. It is often used to reduce the dimension and widely used in learning data generation model. Its structure is similar to a multi-layer perceptron, but has the ability to reconstruct its original input. In this study, the handwritten digital classification on the MNIST database and the fault diagnosis in the Tennessee Eastman process were discussed and analyzed. We provide detailed information on how to determine the fault pattern of the process based on a standard model rather than an experience of the factory site personnel. Besides, this study also discusses the new class could be learned through the deep auto-encoder and provides diagnostic results using images.