TY - JOUR
T1 - Analysis of Deterioration in a Plasma Focus Device
AU - Zanelli, Daniel
AU - López, Enrique
AU - Pavez, Cristian
AU - Pedreros, José
AU - Jain, Jalaj
AU - Avaria, Gonzalo
AU - Moreno, José
AU - Bora, Biswajit
AU - Davis, Sergio
AU - Soto, Leopoldo
N1 - Funding Information:
The work is supported by IAEA CRP contract 20370 and by grant ACT-1115, CONICYT, Chile.
PY - 2018/6/25
Y1 - 2018/6/25
N2 - The Plasma Focus (PF) is a kind of dense transient plasmas in with high-pulsed voltage. To produce devices for eld application it is necessary to obtain PF equipment able to operate for a long period of time. Thus, a reliability analysis is indispensable. In this work a reliability analysis program for plasma focus devices is presented. The program considers a criticality analysis using Failure Modes and Effects Criticality Analysis (FMECA) to identify the most important failure modes of the system. Said failure modes are studied operating the Plasma Focus for many cycles, obtaining from them the characteristic curves of V(t) and İ(t). Feature Extraction (FE) techniques are applied to obtain a list of parameters that correlate to the degrading process. Furthermore, Machine Learning tools are used to learn from the obtained data, linking the changes in these parameters during its life cycle to the decay of the system in hope for future implementation of a predictive maintenance system and a reference for data analysis and prediction in PFs. The study was applied to a portable plasma focus device operated at 2 joules of stored energy.
AB - The Plasma Focus (PF) is a kind of dense transient plasmas in with high-pulsed voltage. To produce devices for eld application it is necessary to obtain PF equipment able to operate for a long period of time. Thus, a reliability analysis is indispensable. In this work a reliability analysis program for plasma focus devices is presented. The program considers a criticality analysis using Failure Modes and Effects Criticality Analysis (FMECA) to identify the most important failure modes of the system. Said failure modes are studied operating the Plasma Focus for many cycles, obtaining from them the characteristic curves of V(t) and İ(t). Feature Extraction (FE) techniques are applied to obtain a list of parameters that correlate to the degrading process. Furthermore, Machine Learning tools are used to learn from the obtained data, linking the changes in these parameters during its life cycle to the decay of the system in hope for future implementation of a predictive maintenance system and a reference for data analysis and prediction in PFs. The study was applied to a portable plasma focus device operated at 2 joules of stored energy.
UR - http://www.scopus.com/inward/record.url?scp=85050123182&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/1043/1/012049
DO - 10.1088/1742-6596/1043/1/012049
M3 - Conference article
AN - SCOPUS:85050123182
SN - 1742-6588
VL - 1043
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012049
T2 - 20th Chilean Physics Symposium
Y2 - 30 November 2016 through 2 December 2016
ER -