METHOD FOR DETECTING AGING RELATED FAILURES OF PROCESS SENSORS VIA NOISE SIGNAL MEASUREMENT
T. Toosi, Detection of the Aging of Process Sensors from Signal Noise, Master’s thesis, Aalto University, 2016. (in Finnish)
T. Toosi, M. Sirola, J. Laukkanen, M. van Heeswijk and J. Karhunen, “Detecting aging of process sensors with noise signal measurement,” Proceedings of the 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2017, Bucharest, Romania, September 21-23, 2017, pp. 35-40.
J. Lienig, H. Bruemmer, Fundamentals of Electronic Systems Design, Springer International Publishing, 2017, 54 p. ISBN 978-3-319-55839-4.
A. Papoulis, S. U. Pillai, Probability, Random Variables, and Stochastic Processes fourth ed., Boston: McGraw-Hill, 2002, ISBN 0-07-366011-6.
R. Jiang, D. N. P. Murthy, “A study of Weibull shape parameter: Properties and significance,” Reliability Engineering & System Safety, vol. 96, issue 12, pp. 1619–1626, 2011. doi: 10.1016/j.ress.2011.09.003.
Wikipedia, [Online]. Available: https://en.wikipedia.org/wiki/Bathtub_curve
T. Allenius, Ageing Classification for Electrical Components, Bachelor’s thesis, University of Applied Sciences, Vaasa, 2013, 58 p. (in Finnish)
O. Mehtonen, Effect of Aging on Mechanical Impact Strength of Joints Manufactured with Isotropic Conductive Adhesives, Master’s thesis, Tampere University of Technology. 2011, 61 p. (in Finnish)
M. Ohring, Reliability and Failure of Electronic Materials and Devices, Academic Press, 1998, 692 p.
S.Y. Xu, D. A. Dillard, J.G. Dillard, “Environmental aging effects on the durability of electrically conductive adhesive joints,” International Journal of Adhesion and Adhesives, vol. 23, pp. 235-250, 2003.
H. Hashemian, D. Mitchell, R. Fain, and K. Petersen, Long term performance and aging characteristics of nuclear plant pressure transmitters, Nuclear Regulatory Commission, Washington, DC (USA). Div. of Engineering; Analysis and Measurement Services Corp., Knoxville, TN (USA), Tech. Rep., 1993.
P.M. Frank, “Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: A survey and some new results,” Automatica, vol. 26, no. 3, pp. 459–474, 1990.
S. Mandal, B. Santhi, S. Sridhar, K. Vinolia and P. Swaminathan, “Nuclear power plant thermocouple sensor-fault detection and classification using deep learning and generalized likelihood ratio test,” IEEE Transactions on Nuclear Science, vol. 64, issue 6, pp. 1526-1534, 2017.
N. Sairam and S. Mandal, “Thermocouple sensor fault detection using auto-associative kernel regression and generalized likelihood ratio test,” Proceedings of the 2016 International Conference on Computer, Electrical Communication Engineering (ICCECE), December 2016, pp. 1-6.
W. Li, M. Peng, Y. Liu, N. Jiang, H. Wang and Z. Duan, “Fault detection, identification and reconstruction of sensors in nuclear power plant with optimized PCA method,” Annals of Nuclear Energy, vol. 113, pp. 105–117, 2018.
S. Mandal, N. Sairam, S. Sridhar and P. Swaminathan, “Nuclear power plant sensor fault detection using singular value decomposition-based method,” Sādhanā, vol. 42, issue 9, pp. 1473-1480, 2017. [Online]. Available: https://www.ias.ac.in/article/fulltext/sadh/042/09/1473-1480
N. Mehranbod, M. Soroush, M. Piovoso, B.A. Ogunnaike et al., “Probabilistic model for sensor fault detection and identification,” AIChE Journal, vol. 49, no. 7, pp. 1787–1802, 2003.
H. Hashemian, K. Petersen, R. Fain, and J. Gingrich, Effect of aging on response time of nuclear plant pressure sensors, Nuclear Regulatory Commission, Washington, DC (USA). Div. of Engineering; Analysis and Measurement Services Corp., Knoxville, TN (USA), Tech. Rep., 1989.
S.K. Mitra, Digital Signal Processing: A Computer-Based Approach, third edition, New York: McGraw-Hill Companies, Inc., 2006.
J. Ortiz-Villafuerte, R. Castillo-Durán, G. Alonso, and G. Calleros-Micheland, “BWR online monitoring system based on noise analysis,” Nuclear Engineering and Design, vol. 236, no. 22, pp. 2394–2404, 2006. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0029549306001452
K. Lin and K.E. Holbert, “Pressure sensing line diagnostics in nuclear power plants,” Nuclear Power, pp. 97–122, 2010.
H. Hashemian, “On-line monitoring applications in nuclear power plants,” Progress in Nuclear Energy, vol. 53, no. 2, pp. 167-181, 2011. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0149197010001307
E. Alpaydin, Introduction to Machine Learning (Adaptive Computation and Machine Learning), The MIT Press, 2004.
P.D. Welch, “The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms,” IEEE Transactions on Audio and Electroacoustics, vol. 15, no. 2, pp. 70–73, 1967.
The MathWorks, Inc., “MATLAB Release 2015a,” 2015, natick, Massachusetts, United States.
D. Arthur and S. Vassilvitskii, “K-means++: the advantages of careful seeding,” in Proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms, 2007, pp. 1-9.
J. Blázquez and J. Ballestrı́n, “Pressure transmitter surveillance: The dominant real pole case,” Progress in Nuclear Energy the 25th Informal Meeting on Reactor Noise, vol. 29, no. 3–4, pp. 139–145, 1995. [Online]. Available: http://www.sciencedirect.com/science/article/pii/0149197095000033
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