Hot-Pressing Furnace Current Monitoring and Predictive Maintenance System in Aerospace Applications

作     者：Chen, Hong-Ming Zhang, Jia-Hao Wang, Yu-Chieh Chang, Hsiang-Ching King, Jen-Kai Yang, Chao-Tung 

作者机构：Tunghai Univ Dept Appl Math 1727 Sec 4 Taiwan Blvd Taichung 407224 Taiwan Tunghai Univ Dept Comp Sci 1727 Sec 4 Taiwan Blvd Taichung 407224 Taiwan Aerosp Ind Dev Corp Innovat R&D Ctr 1-1 Hanxiang Rd Taichung 407803 Taiwan Tunghai Univ Res Ctr Smart Sustainable Circular Econ 1727 Sec 4 Taiwan Blvd Taichung 407224 Taiwan 

出 版 物：《SENSORS》 (传感器)

年 卷 期：2023年第23卷第4期

页      面：2230-2230页

核心收录：

学科分类：0710[理学-生物学] 071010[理学-生物化学与分子生物学] 0808[工学-电气工程] 07[理学] 0804[工学-仪器科学与技术] 0703[理学-化学] 

基　　金：National Science and Technology Council, Taiwan (R.O.C.) [NSTC 110-2221-E-029-020-MY3, 111-2622-E-029-003] NSTC [111-2621-M-029-004] 

主　　题：predictive maintenance current monitoring machine learning monitoring algorithm aerospace 

摘      要：This research combines the application of artificial intelligence in the production equipment fault monitoring of aerospace components. It detects three-phase current abnormalities in large hot-pressing furnaces through smart meters and provides early preventive maintenance. Different anomalies are classified, and a suitable monitoring process algorithm is proposed to improve the overall monitoring quality, accuracy, and stability by applying AI. We also designed a system to present the heater s power consumption and the hot-pressing furnace s fan and visualize the process. Combining artificial intelligence with the experience and technology of professional technicians and researchers to detect and proactively grasp the health of the hot-pressing furnace equipment improves the shortcomings of previous expert systems, achieves long-term stability, and reduces costs. The complete algorithm introduces a model corresponding to the actual production environment, with the best model result being XGBoost with an accuracy of 0.97.