Xiaoyu Cheng¹, Mingxian Long¹, Wei He^1,2,*, Hailong Zhu¹

Computer Systems Science and Engineering, Vol.46, No.3, pp. 2821-2844, 2023, DOI:10.32604/csse.2023.037330 - 03 April 2023

Abstract Expert knowledge is the key to modeling milling fault detection systems based on the belief rule base. The construction of an initial expert knowledge base seriously affects the accuracy and interpretability of the milling fault detection model. However, due to the complexity of the milling system structure and the uncertainty of the milling failure index, it is often impossible to construct model expert knowledge effectively. Therefore, a milling system fault detection method based on fault tree analysis and hierarchical BRB (FTBRB) is proposed. Firstly, the proposed method uses a fault tree and hierarchical BRB modeling. More >

Qingyun Yan¹, You Li², Yuanhong Zhu³, Kui Cheng³, Xueli Huang³, Cong Qi³, Xuemin Ye^2,*

FDMP-Fluid Dynamics & Materials Processing, Vol.16, No.3, pp. 623-636, 2020, DOI:10.32604/fdmp.2020.09669 - 25 May 2020

Abstract A hot primary-air pipe system is the bridge connecting an air-preheater with a coal mill in power generation stations. The effective geometrical configuration of the pipe network greatly affects the air flow distribution and consequently influences the safe and economic operation of milling systems in power stations. In order to improve the properties of the air flow, in the present work the SIMPLEC method is used to simulate numerically the flow field for the original layout of the system. As a result, the internal mechanisms influencing the uneven pressure drop in each branch are explored More >