Auto-Encoder Based Fault Early Warning Model For Primary Fan Of Power Plant

Primary fan system plays an important role in the operation of a power plant. However, due to the complicated working conditions of the primary fan and the strong coupling characteristics of multi-state variables, it is necessary to carry out feature engineering before using multivariate state estimation technique (MSET). In addition, no-linear operator should be designed to make sure matrix being invertible. This paper proposes an Auto-encoder based model to automatically construct a normal state memory matrix through unsupervised learning of neural networks. It reduces human intervention as well as the difficulty in giving a suitable non-linear operator design. This model is applied to the early warning of primary fan failure in a power plant in eastern Shandong Province.