Page 103
P. 103
factores clave para ser considerada como
la preferida para la solución.
Otra posibilidad es utilizar el reconoci-
miento de imagen TensorFlow (41), con
sus modelos de redes neuronales convo-
lucionales profundas.
Se necesitará más investigación y desa-
rrollo sobre este tema.
7d. Sistemas integrados de seguridad
La integración de la medición de la
concentración de polvo en un sistema
de control de seguridad (35) que sea
adaptable a las principales arquitecturas
de fabricación, activarán una serie de
acciones de mitigación predeterminadas
para evitar la propagación de la atmós-
fera explosiva detectada, eliminando las
fuentes de ignición mecánica y eléctri-
ca que cuentan con más de tres cuartos
del total de incidentes (Figura 14). Las
acciones incluirán: cierre de alimentado-
res en sistemas de transportación, parada
de equipos mecánicos (excepto aspira-
ciones), interrupción de la energía del
sector, etc.
Se requiere el registro de detecciones
para un análisis estadístico adicional,
útil para la validación de zonas y la cla-
sificación de áreas. La grabación de la
base de datos es válida para documen-
tación interna y cumplimiento norma-
tivo. Además, se deben proveer gene-
radores de alarmas visuales y acústicas
para la correcta evacuación del personal
(Figura 15).
· Referencias
1. National Instruments (NI) Vision Builder for
Automated Inspection (AI) software: http://
www.ni.com/vision/software/vbai/
2. Measurement of dust concentra-
tion based on VBAI (You Fu, Ninghui
Wang): http://iopscience.iop.org/arti-
cle/10.1088/1742-6596/418/1/012079/pdf
3. Concentration Supervisory Instrument for Coal
Dust (Wang Kai, Zhao Hai-shen, Chen Zheng-
hong): http://en.cnki.com.cn/Article_en/CJFD-
TOTALDONG403.010.htm
4. Dust Monitoring System Based on Video Ima-
ge Processing (Yi Chu, Guohai Liu, Congli
Mei, Yuhan Ding): https://link.springer.com/
chapter/10.1007/978-1-4471-2467-2_92#citeas
5. Dust Explosions (AGP Mendes): https://www.
semanticscholar.org/paper/Dust-Explosions-
MENDES/336b04f758b0af0a8500e35656bfc29
d311fc8f8
6. Labour Hazard Alert – Cleaning a Grain Elevator
(Employment and Social Development Canada):
https://www.canada.ca/en/employment-social-
development/services/health-safety/prevention/
grain-elevator.html
7. Implement an Advanced Soft Measurement
Method of Mine Dust Concentration Based on
K-RBF Neural Network (Hong Yu, Xuezhen
Cheng, Maoyong Cao, Xiaohang Gao): http://
www.enggjournals.com/ijet/docs/IJET15-07-02-
022.pdf
8. Coal Dust Recognition Based on Concave Point
Extraction and Ellipse Fitting (ZHANG Wei, YI
Hong-bo,WANG Xiao-wen): http://citeseerx.ist.
psu.edu/viewdoc/download?doi=10.1.1.885.404
9&rep=rep1&type=pdf
9. Extracting an Optical Finger-Print – a New
Approach to Single Particle Analysis (Eckart
Schultz, Ulrich Heimann, Stefan Scharring):
https://lmb.informatik.unifreiburg.de/papers/
download/sch_awm05.pdf
10. LAND AMETEK Opacity and Dust Monitors:
https://www.ametekland.com/products/opacit-
yanddustmonitors
11. JUNG Instruments Dust & Environmental
Measurement: http://www.junginstruments. de/
dust_and_environmental_measurement.html?gc
lid=EAIaIQobChMImozE3ZSa2gIVUwuRCh2p
SQDBEAMYASAAEgIZxfD_BwE
12. SICK Vision Sensors: http://www.cimtecau-
tomation.com/industrialautomationresources/
resources/SICK/SICK%20Lit%20Folder/
Vision,%20IVC%20&%20PLB/SICK%20Bro-
chure%20All%20Vision.pdf
13. A study on the prediction method of ER pollu-
tion level based on Deep Learning using Scat-
tering Sensor (Mi-Lim Choi, Myung Jae Lim,
Young-Man Kwon, Don-Kun Chung): http://
docsdrive.com/pdfs/medwelljournals/jeas-
ci/2017/2560-2564.pdf
14. Lowering Miners' Exposure to Respirable
Coal Mine Dust, Including Continuous Per-
sonal Dust Monitors (Mine Safety and Health
Administration, United States Department of
Labor): https://arlweb.msha.gov/regs/fedreg/
final/2014finl/2014-09084.asp
15. Air-Met Scientific Real Time Dust & Aerosol
Monitoring: https://www.airmet.com.au/assets/
Air-Met%20Downloads/Air-Met%20Pro-
duct%20Catalogue%202014.pdf
16. SICK Transmittance Dust Measuring Devices:
https://www.sick.com/us/en/dust-measuring-
devices/transmittance-dustmeasuring-devices/c/
g283715
17. SICK Scattered Light Dust Measuring Devi-
ces: https://www.sick.com/us/en/dust-measu-
ring-devices/scattered-light-dustmeasuring-
devices/c/g283714
18. Thermotemp Video Based Steam and Dust
Detection: http://www.thermotemp.de/en/sicher-
heitssysteme/videodampferkennung.php
19. Mathworks Matlab Image Recognition: https://
www.mathworks.com/discovery/imagerecogni-
tion.html?s_tid=srchtitle
20. OSHA’s Combustible Dust Awareness and
Training Program: https://www.osha.gov/dte/
grant_materials/fy08/sh-17797-08/combusti-
ble_dust.ppt
21. GRAIN INDUSTRY’S APPROACH TO DUST
EXPLOSIONS (James E. Maness): https://www.
nfpa.org/-/media/Files/News-and-Research/
Resources/Research-Foundation/foundationpro-
ceedings/jim_maness.ashx?la=en&hash=19F4E
85292E9B8D5D528CE1C364F0F2697CDBF6C
22. Air Particle Monitoring using Image Proces-
sing (Newman Sana): https://eprints.usq.edu.
au/24714/1/Sana_2013.pdf
23. ATEX European Directives: http://ec.europa.eu/
growth/sectors/mechanicalengineering/atex_es
24. Georgia Tech Dust Explosions: http://www.
oshainfo.gatech.edu/combdust/DustExplosions.
pdf
25. GreCon Combustible Dust Explosions: http://
www.grecon-us.com/sparkdetection/combusti-
ble-dust-explosions/
26. Hixson Combustible Dust Basics (Cristopher
Jarc): http://www.hixsoninc.com/_images/Com-
bustible_Dust_0114_FINAL.pdf
27. The Dedusting Method Based on a Single Still
Image (Prasad Yarlagadda, Yun-Hae Kim):
https://www.scientific.net/AMM.333-335.929
28. National Instruments LabView: http://www.
ni.com/en-us/shop/labview.html
29. Willow Telaire Dust Sensor: https://www.
willow.co.uk/breathe-freely-with-thetelaire-sm-
pwm-01a-smart-dust-sensor.html
30. My Dust Explosion Research Community:
http://www.mydustexplosionresearch.com/
31. Protection against dust explosions in industrial
plants handling carbon black (ICBA): http://
www.cabotcorp.com/~/media/files/products-
tewardship/industry-user-guides/international-
carbon-black-associationprotection-against-
dust-explosions.pdf
32. Physical Properties of Five Grain Dust Types
(Calvin B. Parnell, Jr., David D. Jones, Ross D.
Rutherford, Kerry J. Goforth): https://www.ncbi.
nlm.nih.gov/pmc/articles/PMC1474380/
33. Sintrol Dumo: https://www.sintrolproducts.
com/products/ambientmonitoring/Dumo
34. Envea AirSafe: https://www.swr-engineering.
com/en/measurementproducts/airsafe-conti-
nuous-ambient-air-dust-monitoring.html
35. Siemens Safety Systems Industrial Controls:
https://w3.siemens.com/mcms/industrial-con-
trols/en/safetysystems/pages/default.aspx
36. DURAG Optical Opacity Dust Monitor: https://
www.durag.com/productsen/measuring-monito-
ring-en/dust-monitoring-en/d-r-290-2g-en/
37. Google Cloud Vision API and Video Intelligen-
ce API: https://cloud.google.com/vision/
38. NFPA standards and codes: www.nfpa.org/
codes-and-standards/all-codes-andstandards/
39. Intrinsically safe cameras using Android:
https://www.ecomex.com/products/communica-
tion/cell-phones/smart-ex-01/
40. Siemens For the Love of Grain: https://www.
industry.usa.siemens.com/automation/us/en/
processinstrumentation-and-analytics/proces-
sinstrumentation/brochures/Documents/FBBR-
GRNBR-0213-Grain-USA.pdf
41. TensorFlow Image Recognition: https://www.
tensorflow.org/tutorials/image_recognition
n
A&G 116
• Tomo XXIX • Vol. 3 • 396-405 • (2019)
405
Estrategias para la utilización de la tecnología
“Vision Intelligence”
para la prevención de las explosiones de polvo de granos y oleaginosas
la preferida para la solución.
Otra posibilidad es utilizar el reconoci-
miento de imagen TensorFlow (41), con
sus modelos de redes neuronales convo-
lucionales profundas.
Se necesitará más investigación y desa-
rrollo sobre este tema.
7d. Sistemas integrados de seguridad
La integración de la medición de la
concentración de polvo en un sistema
de control de seguridad (35) que sea
adaptable a las principales arquitecturas
de fabricación, activarán una serie de
acciones de mitigación predeterminadas
para evitar la propagación de la atmós-
fera explosiva detectada, eliminando las
fuentes de ignición mecánica y eléctri-
ca que cuentan con más de tres cuartos
del total de incidentes (Figura 14). Las
acciones incluirán: cierre de alimentado-
res en sistemas de transportación, parada
de equipos mecánicos (excepto aspira-
ciones), interrupción de la energía del
sector, etc.
Se requiere el registro de detecciones
para un análisis estadístico adicional,
útil para la validación de zonas y la cla-
sificación de áreas. La grabación de la
base de datos es válida para documen-
tación interna y cumplimiento norma-
tivo. Además, se deben proveer gene-
radores de alarmas visuales y acústicas
para la correcta evacuación del personal
(Figura 15).
· Referencias
1. National Instruments (NI) Vision Builder for
Automated Inspection (AI) software: http://
www.ni.com/vision/software/vbai/
2. Measurement of dust concentra-
tion based on VBAI (You Fu, Ninghui
Wang): http://iopscience.iop.org/arti-
cle/10.1088/1742-6596/418/1/012079/pdf
3. Concentration Supervisory Instrument for Coal
Dust (Wang Kai, Zhao Hai-shen, Chen Zheng-
hong): http://en.cnki.com.cn/Article_en/CJFD-
TOTALDONG403.010.htm
4. Dust Monitoring System Based on Video Ima-
ge Processing (Yi Chu, Guohai Liu, Congli
Mei, Yuhan Ding): https://link.springer.com/
chapter/10.1007/978-1-4471-2467-2_92#citeas
5. Dust Explosions (AGP Mendes): https://www.
semanticscholar.org/paper/Dust-Explosions-
MENDES/336b04f758b0af0a8500e35656bfc29
d311fc8f8
6. Labour Hazard Alert – Cleaning a Grain Elevator
(Employment and Social Development Canada):
https://www.canada.ca/en/employment-social-
development/services/health-safety/prevention/
grain-elevator.html
7. Implement an Advanced Soft Measurement
Method of Mine Dust Concentration Based on
K-RBF Neural Network (Hong Yu, Xuezhen
Cheng, Maoyong Cao, Xiaohang Gao): http://
www.enggjournals.com/ijet/docs/IJET15-07-02-
022.pdf
8. Coal Dust Recognition Based on Concave Point
Extraction and Ellipse Fitting (ZHANG Wei, YI
Hong-bo,WANG Xiao-wen): http://citeseerx.ist.
psu.edu/viewdoc/download?doi=10.1.1.885.404
9&rep=rep1&type=pdf
9. Extracting an Optical Finger-Print – a New
Approach to Single Particle Analysis (Eckart
Schultz, Ulrich Heimann, Stefan Scharring):
https://lmb.informatik.unifreiburg.de/papers/
download/sch_awm05.pdf
10. LAND AMETEK Opacity and Dust Monitors:
https://www.ametekland.com/products/opacit-
yanddustmonitors
11. JUNG Instruments Dust & Environmental
Measurement: http://www.junginstruments. de/
dust_and_environmental_measurement.html?gc
lid=EAIaIQobChMImozE3ZSa2gIVUwuRCh2p
SQDBEAMYASAAEgIZxfD_BwE
12. SICK Vision Sensors: http://www.cimtecau-
tomation.com/industrialautomationresources/
resources/SICK/SICK%20Lit%20Folder/
Vision,%20IVC%20&%20PLB/SICK%20Bro-
chure%20All%20Vision.pdf
13. A study on the prediction method of ER pollu-
tion level based on Deep Learning using Scat-
tering Sensor (Mi-Lim Choi, Myung Jae Lim,
Young-Man Kwon, Don-Kun Chung): http://
docsdrive.com/pdfs/medwelljournals/jeas-
ci/2017/2560-2564.pdf
14. Lowering Miners' Exposure to Respirable
Coal Mine Dust, Including Continuous Per-
sonal Dust Monitors (Mine Safety and Health
Administration, United States Department of
Labor): https://arlweb.msha.gov/regs/fedreg/
final/2014finl/2014-09084.asp
15. Air-Met Scientific Real Time Dust & Aerosol
Monitoring: https://www.airmet.com.au/assets/
Air-Met%20Downloads/Air-Met%20Pro-
duct%20Catalogue%202014.pdf
16. SICK Transmittance Dust Measuring Devices:
https://www.sick.com/us/en/dust-measuring-
devices/transmittance-dustmeasuring-devices/c/
g283715
17. SICK Scattered Light Dust Measuring Devi-
ces: https://www.sick.com/us/en/dust-measu-
ring-devices/scattered-light-dustmeasuring-
devices/c/g283714
18. Thermotemp Video Based Steam and Dust
Detection: http://www.thermotemp.de/en/sicher-
heitssysteme/videodampferkennung.php
19. Mathworks Matlab Image Recognition: https://
www.mathworks.com/discovery/imagerecogni-
tion.html?s_tid=srchtitle
20. OSHA’s Combustible Dust Awareness and
Training Program: https://www.osha.gov/dte/
grant_materials/fy08/sh-17797-08/combusti-
ble_dust.ppt
21. GRAIN INDUSTRY’S APPROACH TO DUST
EXPLOSIONS (James E. Maness): https://www.
nfpa.org/-/media/Files/News-and-Research/
Resources/Research-Foundation/foundationpro-
ceedings/jim_maness.ashx?la=en&hash=19F4E
85292E9B8D5D528CE1C364F0F2697CDBF6C
22. Air Particle Monitoring using Image Proces-
sing (Newman Sana): https://eprints.usq.edu.
au/24714/1/Sana_2013.pdf
23. ATEX European Directives: http://ec.europa.eu/
growth/sectors/mechanicalengineering/atex_es
24. Georgia Tech Dust Explosions: http://www.
oshainfo.gatech.edu/combdust/DustExplosions.
25. GreCon Combustible Dust Explosions: http://
www.grecon-us.com/sparkdetection/combusti-
ble-dust-explosions/
26. Hixson Combustible Dust Basics (Cristopher
Jarc): http://www.hixsoninc.com/_images/Com-
bustible_Dust_0114_FINAL.pdf
27. The Dedusting Method Based on a Single Still
Image (Prasad Yarlagadda, Yun-Hae Kim):
https://www.scientific.net/AMM.333-335.929
28. National Instruments LabView: http://www.
ni.com/en-us/shop/labview.html
29. Willow Telaire Dust Sensor: https://www.
willow.co.uk/breathe-freely-with-thetelaire-sm-
pwm-01a-smart-dust-sensor.html
30. My Dust Explosion Research Community:
http://www.mydustexplosionresearch.com/
31. Protection against dust explosions in industrial
plants handling carbon black (ICBA): http://
www.cabotcorp.com/~/media/files/products-
tewardship/industry-user-guides/international-
carbon-black-associationprotection-against-
dust-explosions.pdf
32. Physical Properties of Five Grain Dust Types
(Calvin B. Parnell, Jr., David D. Jones, Ross D.
Rutherford, Kerry J. Goforth): https://www.ncbi.
nlm.nih.gov/pmc/articles/PMC1474380/
33. Sintrol Dumo: https://www.sintrolproducts.
com/products/ambientmonitoring/Dumo
34. Envea AirSafe: https://www.swr-engineering.
com/en/measurementproducts/airsafe-conti-
nuous-ambient-air-dust-monitoring.html
35. Siemens Safety Systems Industrial Controls:
https://w3.siemens.com/mcms/industrial-con-
trols/en/safetysystems/pages/default.aspx
36. DURAG Optical Opacity Dust Monitor: https://
www.durag.com/productsen/measuring-monito-
ring-en/dust-monitoring-en/d-r-290-2g-en/
37. Google Cloud Vision API and Video Intelligen-
ce API: https://cloud.google.com/vision/
38. NFPA standards and codes: www.nfpa.org/
codes-and-standards/all-codes-andstandards/
39. Intrinsically safe cameras using Android:
https://www.ecomex.com/products/communica-
tion/cell-phones/smart-ex-01/
40. Siemens For the Love of Grain: https://www.
industry.usa.siemens.com/automation/us/en/
processinstrumentation-and-analytics/proces-
sinstrumentation/brochures/Documents/FBBR-
GRNBR-0213-Grain-USA.pdf
41. TensorFlow Image Recognition: https://www.
tensorflow.org/tutorials/image_recognition
n
A&G 116
• Tomo XXIX • Vol. 3 • 396-405 • (2019)
405
Estrategias para la utilización de la tecnología
“Vision Intelligence”
para la prevención de las explosiones de polvo de granos y oleaginosas
