학술논문

Predictive Method for Determining the Operating Condition of Big-Blaster Air Cannons Using Automatic Classification of Critical Discharge of Compressed Air
Document Type
Conference
Source
2022 International Conference and Exposition on Electrical And Power Engineering (EPE) Electrical And Power Engineering (EPE), 2022 International Conference and Exposition on. :415-421 Oct, 2022
Subject
Bioengineering
Components, Circuits, Devices and Systems
Engineering Profession
Fields, Waves and Electromagnetics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Databases
Thermal engineering
Steel industry
Writing
Time division multiplexing
Discharges (electric)
Software
thermal aerodynamics
sonic airwave
Big-Blaster cannon
data acquisition
automatic classification
Language
ISSN
2644-223X
Abstract
Starting from the current requirements in industrial applications regarding the characterization and classification of the phenomenology of compressed air flow to sonic velocities in big-blaster type equipment, used for unblocking or cleaning of bunkers and silos with bulk/powder materials in cement and building material factories, coal-fired power plants, mining and steel industry, this paper presents a theoretical study of the discharge process of big-blaster equipment together with the automatic classification of each discharge by comparison with the presented theoretical results. The paper also describes the hardware and software structures of the automatic classification system, which additionally provides an automatic report and storage of the obtained results in special TDMS files or in MySQL database. Thus, we present a predictive maintenance method, which determines the operating condition of each big-blaster pneumatic cannon. The classification categories of the discharge phenomenon of big-blaster equipment obtained theoretically are validated by practical tests on a relatively large number of pieces of equipment from the range of air cannons mentioned above.