METHOD FOR DETECTING AGING RELATED FAILURES OF PROCESS SENSORS VIA NOISE SIGNAL MEASUREMENT
DOI:
https://doi.org/10.47839/ijc.18.2.1412Keywords:
aging, failure prediction, data analysis, noise analysis, nuclear power plant, pressure sensors, principal component analysis, process sensors, spectral analysis.Abstract
In this article we examine the methods for detecting and predicting aging related process sensor failures by analyzing the noise of the sensor output signal. The study uses data from non-differential and differential pressure transmitters used in the pressure and water level measurements of the reactor pressure vessels of units 1 and 2 of the Olkiluoto nuclear power plant in Finland. The article contains a review of the current methods for detection of sensor failures. Additionally, we present a new method for detecting changes in the sensor output signal. The method creates fingerprints of the power spectra of the sensors by using Principal Component Analysis (PCA). The changes in these fingerprints together with the measurements of the redundant sensors can be used to detect indications of some of the impending sensor failures. In the experimental study we are able to produce stable fingerprints for both the non-differential and differential pressure transmitters. Also, a potential failure in one of the differential pressure transmitters in Olkiluoto unit 2 is detected by inspecting the fingerprints and analyzing the spectral changes of the transmitter output signal.References
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
Downloads
Published
How to Cite
Issue
Section
License
International Journal of Computing is an open access journal. Authors who publish with this journal agree to the following terms:• Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
• Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
• Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
