Detecting aging of process sensors with noise signal measurement

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Detecting aging of process sensors with noise signal measurement. / Toosi, T.; Sirola, M.; Laukkanen, J.; Heeswijk, M. van; Karhunen, J.

2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) . Vol. 1 IEEE, 2017. p. 35-40.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Toosi, T, Sirola, M, Laukkanen, J, Heeswijk, MV & Karhunen, J 2017, Detecting aging of process sensors with noise signal measurement. in 2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) . vol. 1, IEEE, pp. 35-40, IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, Bucharest, Romania, 21/09/2017. https://doi.org/10.1109/IDAACS.2017.8095045

APA

Toosi, T., Sirola, M., Laukkanen, J., Heeswijk, M. V., & Karhunen, J. (2017). Detecting aging of process sensors with noise signal measurement. In 2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) (Vol. 1, pp. 35-40). IEEE. https://doi.org/10.1109/IDAACS.2017.8095045

Vancouver

Toosi T, Sirola M, Laukkanen J, Heeswijk MV, Karhunen J. Detecting aging of process sensors with noise signal measurement. In 2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) . Vol. 1. IEEE. 2017. p. 35-40 https://doi.org/10.1109/IDAACS.2017.8095045

Author

Toosi, T. ; Sirola, M. ; Laukkanen, J. ; Heeswijk, M. van ; Karhunen, J. / Detecting aging of process sensors with noise signal measurement. 2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) . Vol. 1 IEEE, 2017. pp. 35-40

Bibtex - Download

@inproceedings{a133823becbd42f38018dd3fb348b7ff,
title = "Detecting aging of process sensors with noise signal measurement",
abstract = "In this paper, methods for detecting failures in process sensors from the noise measurement due to aging issues are examined. The data are acquired from the water level and pressure measurement transmitters in the Olkiluoto nuclear power plant in Finland: units Olkiluoto 1 and Olkiluoto 2. Methods found from the literature about the failure indicators are presented. Changes in the sensor response time as well as in the resonance peaks in the signal are identified from the power spectrum of the signal. In addition, a new method for fingerprinting the sensors using the Principal Component Analysis (PCA) of the signal spectra is presented. By following the changes in these fingerprints and the variations between parallel measurements of the redundant sensors, symptoms of sensor failures can be detected. In the experiments we were able to produce stable fingerprints for the differential pressure transmitters used in the water level measurement. Potential failure in one differential pressure sensor in unit Olkiluoto 2 is found with the fingerprint method and by analyzing the changes in the spectrum.",
keywords = "fission reactor safety, pressure sensors, principal component analysis, sensors, Olkiluoto 1 data, Olkiluoto nuclear power plant, Principal Component Analysis, aging issues, differential pressure sensor, differential pressure transmitters, failure indicators, fingerprint method, noise measurement, noise signal measurement, parallel measurements, potential failure, power spectrum, pressure measurement transmitters, process sensors, redundant sensors, resonance peaks, sensor failures, sensor response time, signal spectra, stable fingerprints, unit Olkiluoto 2, water level measurement, Aging, Analytical models, Fingerprint recognition, Pressure sensors, Principal component analysis, Time factors, aging, data analysis",
author = "T. Toosi and M. Sirola and J. Laukkanen and Heeswijk, {M. van} and J. Karhunen",
year = "2017",
month = "9",
day = "1",
doi = "10.1109/IDAACS.2017.8095045",
language = "English",
isbn = "978-1-5386-0697-1",
volume = "1",
pages = "35--40",
booktitle = "2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)",
publisher = "IEEE",

}

RIS - Download

TY - GEN

T1 - Detecting aging of process sensors with noise signal measurement

AU - Toosi, T.

AU - Sirola, M.

AU - Laukkanen, J.

AU - Heeswijk, M. van

AU - Karhunen, J.

PY - 2017/9/1

Y1 - 2017/9/1

N2 - In this paper, methods for detecting failures in process sensors from the noise measurement due to aging issues are examined. The data are acquired from the water level and pressure measurement transmitters in the Olkiluoto nuclear power plant in Finland: units Olkiluoto 1 and Olkiluoto 2. Methods found from the literature about the failure indicators are presented. Changes in the sensor response time as well as in the resonance peaks in the signal are identified from the power spectrum of the signal. In addition, a new method for fingerprinting the sensors using the Principal Component Analysis (PCA) of the signal spectra is presented. By following the changes in these fingerprints and the variations between parallel measurements of the redundant sensors, symptoms of sensor failures can be detected. In the experiments we were able to produce stable fingerprints for the differential pressure transmitters used in the water level measurement. Potential failure in one differential pressure sensor in unit Olkiluoto 2 is found with the fingerprint method and by analyzing the changes in the spectrum.

AB - In this paper, methods for detecting failures in process sensors from the noise measurement due to aging issues are examined. The data are acquired from the water level and pressure measurement transmitters in the Olkiluoto nuclear power plant in Finland: units Olkiluoto 1 and Olkiluoto 2. Methods found from the literature about the failure indicators are presented. Changes in the sensor response time as well as in the resonance peaks in the signal are identified from the power spectrum of the signal. In addition, a new method for fingerprinting the sensors using the Principal Component Analysis (PCA) of the signal spectra is presented. By following the changes in these fingerprints and the variations between parallel measurements of the redundant sensors, symptoms of sensor failures can be detected. In the experiments we were able to produce stable fingerprints for the differential pressure transmitters used in the water level measurement. Potential failure in one differential pressure sensor in unit Olkiluoto 2 is found with the fingerprint method and by analyzing the changes in the spectrum.

KW - fission reactor safety

KW - pressure sensors

KW - principal component analysis

KW - sensors

KW - Olkiluoto 1 data

KW - Olkiluoto nuclear power plant

KW - Principal Component Analysis

KW - aging issues

KW - differential pressure sensor

KW - differential pressure transmitters

KW - failure indicators

KW - fingerprint method

KW - noise measurement

KW - noise signal measurement

KW - parallel measurements

KW - potential failure

KW - power spectrum

KW - pressure measurement transmitters

KW - process sensors

KW - redundant sensors

KW - resonance peaks

KW - sensor failures

KW - sensor response time

KW - signal spectra

KW - stable fingerprints

KW - unit Olkiluoto 2

KW - water level measurement

KW - Aging

KW - Analytical models

KW - Fingerprint recognition

KW - Pressure sensors

KW - Principal component analysis

KW - Time factors

KW - aging

KW - data analysis

U2 - 10.1109/IDAACS.2017.8095045

DO - 10.1109/IDAACS.2017.8095045

M3 - Conference contribution

SN - 978-1-5386-0697-1

VL - 1

SP - 35

EP - 40

BT - 2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)

PB - IEEE

ER -

ID: 16822477