Fault diagnosis of coal mills based on a dynamic model and deep belief network

Abstract

As the significant ancillary equipment of coal-fired power plants, coal mills are the key to ensuring the steady operation of boilers. In this study, a fault diagnosis model was proposed on the basis of a dynamic model of a coal mill and deep belief network (DBN). First, a dynamic coal mill model that considered the joint influence of drying, ventilation and grinding forces was established. Parameters in the model were identified by designing a two-phase optimization method based on the genetic algorithm. Then, this model was used for simulating the common faults of coal mills under a variety of operating conditions and obtaining extensive data. On this basis, the DBN fault diagnosis model was established and the combination of parameters was optimized by use of an orthogonal experiment. Finally, the validity of the model was verified by using the actual operation data of the coal mill. Compared with the dynamic models built in previous studies, that constructed in this paper can significantly improve the capability to simulate and analyze the coal mill. The convergence rate of the designed two-phase optimization method was improved. The experimental results show that the proposed method of coal mill fault diagnosis based on the dynamic model and DBN has an accuracy of 95%, which proves that this method has excellent application potential.