JURJEVČIČ, Boštjan ;SENEGAČNIK, Andrej ;KUŠTRIN, Igor . A Surveillance of Direct-Firing System for Pulverized-Coal Using Statistically Treated Signals from Intrusive Electrostatic Sensors. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 63, n.4, p. 265-274, june 2018. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/article/a-surveillance-of-direct-firing-system-for-pulverized-coal-using-statistically-treated-signals-from-intrusive-electrostatic-sensors/>. Date accessed: 25 apr. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2016.4264.
Jurjevčič, B., Senegačnik, A., & Kuštrin, I. (2017). A Surveillance of Direct-Firing System for Pulverized-Coal Using Statistically Treated Signals from Intrusive Electrostatic Sensors. Strojniški vestnik - Journal of Mechanical Engineering, 63(4), 265-274. doi:http://dx.doi.org/10.5545/sv-jme.2016.4264
@article{sv-jmesv-jme.2016.4264,
author = {Boštjan Jurjevčič and Andrej Senegačnik and Igor Kuštrin},
title = {A Surveillance of Direct-Firing System for Pulverized-Coal Using Statistically Treated Signals from Intrusive Electrostatic Sensors},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {63},
number = {4},
year = {2017},
keywords = {ARIMA model; control chart; fan mill; fault detection; pneumatic transport; statistical modelling},
abstract = {Operational surveillance of all vital parts of thermal power plants is nowadays more important than any time before due to requirements for their extremely flexible operation resulting from intermittent behaviour of renewable energy sources. New methods for online measuring of pneumatic transport provide new possibilities for control and early fault detection of coal grinding and conveying system in direct-fired power plant boilers. Arrays of intrusive electrostatic sensors are an attractive option due to their inexpensive application and good spatial sensitivity required in large rectangular ducts of pulverized-coal systems. In this study, statistically treated electrostatic signals are used for detection of unexpected change in operating regime of coal grinding and conveying. Model-based and model-free autocorrelation reduction techniques are used to reduce the inherent autocorrelation of data. Forming batch-means of data, a model-free autocorrelation reduction technique is proposed in combination with an autoregressive-integrated-moving-average (ARIMA) method. Residuals between real and ARIMA model fitted data are entered into exponentially-weighted-moving-average (EWMA) control chart for statistical surveillance of the process. The robust and cost-effective measuring method accompanied with a simple and intuitive control scheme proves to be effective for early fault detection of the pulverized-coal preparation system.},
issn = {0039-2480}, pages = {265-274}, doi = {10.5545/sv-jme.2016.4264},
url = {https://www.sv-jme.eu/article/a-surveillance-of-direct-firing-system-for-pulverized-coal-using-statistically-treated-signals-from-intrusive-electrostatic-sensors/}
}
Jurjevčič, B.,Senegačnik, A.,Kuštrin, I. 2017 June 63. A Surveillance of Direct-Firing System for Pulverized-Coal Using Statistically Treated Signals from Intrusive Electrostatic Sensors. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 63:4
%A Jurjevčič, Boštjan %A Senegačnik, Andrej %A Kuštrin, Igor %D 2017 %T A Surveillance of Direct-Firing System for Pulverized-Coal Using Statistically Treated Signals from Intrusive Electrostatic Sensors %B 2017 %9 ARIMA model; control chart; fan mill; fault detection; pneumatic transport; statistical modelling %! A Surveillance of Direct-Firing System for Pulverized-Coal Using Statistically Treated Signals from Intrusive Electrostatic Sensors %K ARIMA model; control chart; fan mill; fault detection; pneumatic transport; statistical modelling %X Operational surveillance of all vital parts of thermal power plants is nowadays more important than any time before due to requirements for their extremely flexible operation resulting from intermittent behaviour of renewable energy sources. New methods for online measuring of pneumatic transport provide new possibilities for control and early fault detection of coal grinding and conveying system in direct-fired power plant boilers. Arrays of intrusive electrostatic sensors are an attractive option due to their inexpensive application and good spatial sensitivity required in large rectangular ducts of pulverized-coal systems. In this study, statistically treated electrostatic signals are used for detection of unexpected change in operating regime of coal grinding and conveying. Model-based and model-free autocorrelation reduction techniques are used to reduce the inherent autocorrelation of data. Forming batch-means of data, a model-free autocorrelation reduction technique is proposed in combination with an autoregressive-integrated-moving-average (ARIMA) method. Residuals between real and ARIMA model fitted data are entered into exponentially-weighted-moving-average (EWMA) control chart for statistical surveillance of the process. The robust and cost-effective measuring method accompanied with a simple and intuitive control scheme proves to be effective for early fault detection of the pulverized-coal preparation system. %U https://www.sv-jme.eu/article/a-surveillance-of-direct-firing-system-for-pulverized-coal-using-statistically-treated-signals-from-intrusive-electrostatic-sensors/ %0 Journal Article %R 10.5545/sv-jme.2016.4264 %& 265 %P 10 %J Strojniški vestnik - Journal of Mechanical Engineering %V 63 %N 4 %@ 0039-2480 %8 2018-06-27 %7 2018-06-27
Jurjevčič, Boštjan, Andrej Senegačnik, & Igor Kuštrin. "A Surveillance of Direct-Firing System for Pulverized-Coal Using Statistically Treated Signals from Intrusive Electrostatic Sensors." Strojniški vestnik - Journal of Mechanical Engineering [Online], 63.4 (2017): 265-274. Web. 25 Apr. 2024
TY - JOUR AU - Jurjevčič, Boštjan AU - Senegačnik, Andrej AU - Kuštrin, Igor PY - 2017 TI - A Surveillance of Direct-Firing System for Pulverized-Coal Using Statistically Treated Signals from Intrusive Electrostatic Sensors JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2016.4264 KW - ARIMA model; control chart; fan mill; fault detection; pneumatic transport; statistical modelling N2 - Operational surveillance of all vital parts of thermal power plants is nowadays more important than any time before due to requirements for their extremely flexible operation resulting from intermittent behaviour of renewable energy sources. New methods for online measuring of pneumatic transport provide new possibilities for control and early fault detection of coal grinding and conveying system in direct-fired power plant boilers. Arrays of intrusive electrostatic sensors are an attractive option due to their inexpensive application and good spatial sensitivity required in large rectangular ducts of pulverized-coal systems. In this study, statistically treated electrostatic signals are used for detection of unexpected change in operating regime of coal grinding and conveying. Model-based and model-free autocorrelation reduction techniques are used to reduce the inherent autocorrelation of data. Forming batch-means of data, a model-free autocorrelation reduction technique is proposed in combination with an autoregressive-integrated-moving-average (ARIMA) method. Residuals between real and ARIMA model fitted data are entered into exponentially-weighted-moving-average (EWMA) control chart for statistical surveillance of the process. The robust and cost-effective measuring method accompanied with a simple and intuitive control scheme proves to be effective for early fault detection of the pulverized-coal preparation system. UR - https://www.sv-jme.eu/article/a-surveillance-of-direct-firing-system-for-pulverized-coal-using-statistically-treated-signals-from-intrusive-electrostatic-sensors/
@article{{sv-jme}{sv-jme.2016.4264},
author = {Jurjevčič, B., Senegačnik, A., Kuštrin, I.},
title = {A Surveillance of Direct-Firing System for Pulverized-Coal Using Statistically Treated Signals from Intrusive Electrostatic Sensors},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {63},
number = {4},
year = {2017},
doi = {10.5545/sv-jme.2016.4264},
url = {https://www.sv-jme.eu/article/a-surveillance-of-direct-firing-system-for-pulverized-coal-using-statistically-treated-signals-from-intrusive-electrostatic-sensors/}
}
TY - JOUR AU - Jurjevčič, Boštjan AU - Senegačnik, Andrej AU - Kuštrin, Igor PY - 2018/06/27 TI - A Surveillance of Direct-Firing System for Pulverized-Coal Using Statistically Treated Signals from Intrusive Electrostatic Sensors JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 63, No 4 (2017): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2016.4264 KW - ARIMA model, control chart, fan mill, fault detection, pneumatic transport, statistical modelling N2 - Operational surveillance of all vital parts of thermal power plants is nowadays more important than any time before due to requirements for their extremely flexible operation resulting from intermittent behaviour of renewable energy sources. New methods for online measuring of pneumatic transport provide new possibilities for control and early fault detection of coal grinding and conveying system in direct-fired power plant boilers. Arrays of intrusive electrostatic sensors are an attractive option due to their inexpensive application and good spatial sensitivity required in large rectangular ducts of pulverized-coal systems. In this study, statistically treated electrostatic signals are used for detection of unexpected change in operating regime of coal grinding and conveying. Model-based and model-free autocorrelation reduction techniques are used to reduce the inherent autocorrelation of data. Forming batch-means of data, a model-free autocorrelation reduction technique is proposed in combination with an autoregressive-integrated-moving-average (ARIMA) method. Residuals between real and ARIMA model fitted data are entered into exponentially-weighted-moving-average (EWMA) control chart for statistical surveillance of the process. The robust and cost-effective measuring method accompanied with a simple and intuitive control scheme proves to be effective for early fault detection of the pulverized-coal preparation system. UR - https://www.sv-jme.eu/article/a-surveillance-of-direct-firing-system-for-pulverized-coal-using-statistically-treated-signals-from-intrusive-electrostatic-sensors/
Jurjevčič, Boštjan, Senegačnik, Andrej, AND Kuštrin, Igor. "A Surveillance of Direct-Firing System for Pulverized-Coal Using Statistically Treated Signals from Intrusive Electrostatic Sensors" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 63 Number 4 (27 June 2018)
Strojniški vestnik - Journal of Mechanical Engineering 63(2017)4, 265-274
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
Operational surveillance of all vital parts of thermal power plants is nowadays more important than any time before due to requirements for their extremely flexible operation resulting from intermittent behaviour of renewable energy sources. New methods for online measuring of pneumatic transport provide new possibilities for control and early fault detection of coal grinding and conveying system in direct-fired power plant boilers. Arrays of intrusive electrostatic sensors are an attractive option due to their inexpensive application and good spatial sensitivity required in large rectangular ducts of pulverized-coal systems. In this study, statistically treated electrostatic signals are used for detection of unexpected change in operating regime of coal grinding and conveying. Model-based and model-free autocorrelation reduction techniques are used to reduce the inherent autocorrelation of data. Forming batch-means of data, a model-free autocorrelation reduction technique is proposed in combination with an autoregressive-integrated-moving-average (ARIMA) method. Residuals between real and ARIMA model fitted data are entered into exponentially-weighted-moving-average (EWMA) control chart for statistical surveillance of the process. The robust and cost-effective measuring method accompanied with a simple and intuitive control scheme proves to be effective for early fault detection of the pulverized-coal preparation system.