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http://dx.doi.org/10.1016/j.net.2020.02.001

Fault state detection and remaining useful life prediction in AC powered solenoid operated valves based on traditional machine learning and deep neural networks  

Utah, M.N. (KEPCO International Nuclear Graduate School (KINGS))
Jung, J.C. (KEPCO International Nuclear Graduate School (KINGS))
Publication Information
Nuclear Engineering and Technology / v.52, no.9, 2020 , pp. 1998-2008 More about this Journal
Abstract
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.
Keywords
Predictive maintenance; Condition based maintenance; Remaining useful life; Support vector machines; Solenoid operated valve; Deep neural network;
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1 W. Ahmad, S.A. Khan, M.M.M. Islam, J.M. Kim, A reliable technique for remaining useful life estimation of rolling element bearings using dynamic regression models, Reliab. Eng. Syst. Saf. 184 (2019) 67-76.   DOI
2 Epri, Predictive Maintenance Primer: Revision to Np-7205, 2003. Palo Alto, Ca, 1007350.
3 O. Moseler, H. Straky, Fault detection of a solenoid valve for hydraulic systems in vehicles, Ifac Proc. 33 (11) (2000) 119-124.   DOI
4 A. Adrees, Fault Detection of Solenoid Valve Using Current Signature Analysis, 2009.
5 C.-Y. Tseng, C.-F. Lin, Solenoid Valve Failure Detection for Electronic Diesel Fuel Injection Control Systems, 2005.
6 N.J. Jameson, M.H. Azarian, M. Pecht, Fault diagnostic opportunities for solenoid operated valves using physics-of-failure analysis, in: International Conference on Prognostics and Health Management, Phm, 2014, 2015.
7 H. Guo, K. Wang, H. Cui, A. Xu, J. Jiang, A novel method of fault detection for solenoid valves based on vibration signal measurement, in: IEEE International Conference on Internet of Things (Ithings) and IEEE Green Computing and Communications (Greencom) and IEEE Cyber, Physical and Social Computing (Cpscom) and IEEE Smart Data, Smartdata, 2016, pp. 870-873.
8 K. Fuhr, J. Broussard, G. White, J. Gorman, Epri Literature Review and Failure Modes and Effects Analysis (Fmea) for Welded Stainless Steel Canisters in Dry Cask Storage Systems, 2013.
9 V.P. Bacanskas, G.C. Roberts, G.J. Toman, Aging and Service Wear of Solenoid- Operated Valves Used in Safety Systems of Nuclear Power Plants, Operating Experience And Failure Identification, United States, 1987.
10 X. Wang, Y. Zheng, Z. Zhao, J. Wang, Bearing fault diagnosis based on statistical locally linear embedding, Sensors 15 (7) (2015) 16225-16247.   DOI
11 Maintenance Optimization Programme for Nuclear Power Plants, International Atomic Energy Agency, Vienna, 2018.
12 R.K. Mobley, An Introduction to Predictive Maintenance, second ed., Butterworth- Heinemann, 2002.
13 X. Li, W. Zhang, Q. Ding, Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction, Reliab. Eng. Syst. Saf. 182 (2019) 208-218.   DOI
14 Y. Lei, Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery, Elsevier Inc., 2016.
15 T. Benkedjouh, K. Medjaher, N. Zerhouni, S. Rechak, Remaining useful life estimation based on nonlinear feature reduction and support vector regression, Eng. Appl. Artif. Intell. 26 (7) (2013) 1751-1760.   DOI
16 V. Jakkula, Tutorial on Support Vector Machine (Svm), School of EECS, Washington State University, Washington, 2006.