Dual fault warning method for coal mill based on Autoformer WaveBound

Abstract

The coal mill is one of the important auxiliary equipment of thermal power units. Power plant performance and reliability are greatly influenced by the coal mill. To avoid abnormal operating conditions of coal mills in time and effectively, a dual fault warning method for coal mill is proposed. Three typical faults of coal mill plugging, coal breakage and deflagration are warned by this method. Firstly, the maximum information coefficient method and double complex wavelet packet transform are employed to approximate the historical data of the coal mill. Secondly, WaveBound regularization is incorporated to improve the generalization capabilities of Autoformer. Normal state data is used in the construction of unsupervised learning models. Then, combined with a multi-step rolling prediction method, vital monitoring parameters are supervised by setting the corresponding thresholds. Furthermore, an abnormal detection method based on Wasserstein distance is developed for coal mills overall state monitoring. A dual alarm method considering univariate abnormal detection and multivariate coupling thresholds is formed. Finally, the proposed method is validated with real data from a medium-speed coal mill. The results illustrate that early warning is effectively carried out using the method.