A Surveillance of Direct-Firing System for Pulverized-Coal Using Statistically Treated Signals from Intrusive Electrostatic Sensors

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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: 11 apr. 2021. 
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.  11 Apr. 2021
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)

Authors

Affiliations

  • University of Ljubljana, Faculty of Mechanical Engineering, Slovenia 1

Paper's information

Strojniški vestnik - Journal of Mechanical Engineering 63(2017)4, 265-274

https://doi.org/10.5545/sv-jme.2016.4264

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.

ARIMA model; control chart; fan mill; fault detection; pneumatic transport; statistical modelling