Research on condition monitoring and fault diagnosis of intelligent copper ball production lines based on big data

Abstract

With the continuous upgrading and transformation of the intelligentisation of China's manufacturing industry, and in response to the requirements for further intelligentisation of the phosphor copper ball production line proposed by a new electronic material company, this study proposes a fault prediction and diagnosis method based on big data. A high-efficiency distributed big data platform is constructed, and a workshop-level monitoring centre with the Windows control centre (WinCC) as the core is formed. The WinCC configuration software is used to monitor the key parameters of the equipment during the operation phase, and the login interface is configured according to the requirements of workshop information integration, for example, display interface, alarm interface, debugging interface, trend graph and other common functions. Cloud platforms and virtual private network (VPN) communication are used to realise remote maintenance. Aiming at the common fault problems in the production process, an expert diagnosis system based on fault tree analysis is constructed by fusing the fault tree theory and expert systems. The fault tree model of the unqualified phosphor copper ball production quality and the failure of the hydraulic system is highlighted. Therefore, ensuring the safety of the phosphor copper ball production line is of great significance to the entire production system.

Intelligent fault diagnosis of rotating machinery using lightweight network with modified tree‐structured parzen estimators

“…Gears and bearings are key components of rotating machinery [1,2], the failure of which will greatly affect the performance of the machine, resulting in serious security risk and economic loss [3][4][5]. In order to ensure the continuous and stable operation of the equipment, an algorithm that can timely diagnose the failure of rotating machinery is required [6][7][8], so as to reduce economic losses. Hence, it is of great significance to develop a fault diagnosis algorithm for rotating machinery [9].…”