Estimation of hydrogen flow rate in atmospheric Ar:H2 plasma by using artificial neural network

Instability and fault analysis of arc plasma using advanced signal processing methods

This study focuses on the instability and fault analysis of transferred arc plasma, utilizing advanced signal processing methods. Transferred arc plasma systems find significant applications in various industries, including material processing, metallurgy, and waste management. However, the occurrence of instabilities and fault events can severely impact system performance and reliability. To address instabilities in arc plasma, various conditions were experimented. The operating parameters, such as arc voltage, arc current, acoustic, optical, and spectroscopic signals, were simultaneously recorded at a higher sampling rate. The proposed approach employs advanced signal processing methods, such as the Lyapunov exponent, fast-Fourier transform, short-time-Fourier transform, and power spectral density, to analyze the characteristics and instabilities of the transferred arc plasma process. By capturing and analyzing signals from multiple sensors, it becomes possible to identify deviations, irregularities, and fault patterns that arise during plasma operation. The outcomes of this research will have significant implications for the optimization and control of transferred arc plasma processes. By identifying and characterizing instabilities due to fault events at an early stage, system operators can take timely corrective actions, preventing potential damage and improving the overall system efficiency.