Fault state detection and remaining useful life prediction in AC powered solenoid operated valves based on traditional machine learning and deep neural networks
Autor: | Jaecheon Jung, M.N. Utah |
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Jazyk: | angličtina |
Rok vydání: | 2020 |
Předmět: |
Computer science
020209 energy Remaining useful life Solenoid 02 engineering and technology Deep neural network Fault (power engineering) Machine learning computer.software_genre Predictive maintenance 030218 nuclear medicine & medical imaging law.invention 03 medical and health sciences 0302 clinical medicine law 0202 electrical engineering electronic engineering information engineering Waveform Support vector machines Artificial neural network business.industry Condition-based maintenance Solenoid operated valve lcsh:TK9001-9401 Condition based maintenance Nuclear Energy and Engineering Frequency domain lcsh:Nuclear engineering. Atomic power Artificial intelligence Alternating current business computer |
Zdroj: | Nuclear Engineering and Technology, Vol 52, Iss 9, Pp 1998-2008 (2020) |
ISSN: | 1738-5733 |
Popis: | Solenoid operated valves (SOV) play important roles in industrial process to control the flow of fluids. Solenoid valves can be found in so many industries as well as the nuclear plant. The ability to be able to detect the presence of faults and predicting the remaining useful life (RUL) of the SOV is important in maintenance planning and also prevent unexpected interruptions in the flow of process fluids. This paper proposes a fault diagnosis method for the alternating current (AC) powered SOV. Previous research work have been focused on direct current (DC) powered SOV where the current waveform or vibrations are monitored. There are many features hidden in the AC waveform that require further signal analysis. The analysis of the AC powered SOV waveform was done in the time and frequency domain. A total of sixteen features were obtained and these were used to classify the different operating modes of the SOV by applying a machine learning technique for classification. Also, a deep neural network (DNN) was developed for the prediction of RUL based on the failure modes of the SOV. The results of this paper can be used to improve on the condition based monitoring of the SOV. |
Databáze: | OpenAIRE |
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