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A Novel Industrial Safety IoTs Architecture for External Corrosion Perception Based on Infrared

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Abstract

In this paper, a novel external corrosion risk online perception method is proposed to solve the dangerous external corrosion threat and supply a measurable safe risk perception ability for the industrial safe Internet of Things (IoTs) with the infrared thermal wave as the direct sensors. The three layers model is established with direct variables measuring layer, external corrosion risk soft measuring layer and monitoring cycle decision-making layer. And in the direct variable measuring layer the infrared thermal wave is applied to measure the three direct variables, area ratio of cladding defects,cladding layer thickness reading and overlapping between external and internal corrosion defects, in the direct variables measuring layer. In the external corrosion risk soft measuring layer and monitoring cycle decision-making layer, external corrosion risk can be soft measured through a cladding-condition-based risk matrix and the most optimal monitoring cycle can also be determined through a decision-making tree based on the three direct variables.

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References

  1. Kim J, Kim J, Lee Y, Lim W, Moo I (2009) Application of TRIZ crativity intensification approach tochemical process safety [J]. J Loss Prev Process Ind 22:1039–1043

    Article  Google Scholar 

  2. Eti MC, Ogaji SOT, Probert SD (2006) Reducing the cost of preventive maintenance (PM) through adopting a proactive reliability-focused culture [J]. Appl Energy 83:1235–1248

    Article  Google Scholar 

  3. Ramirez P, Utne IB (2011) Challenges with ageing plants [C]. Process Saf Prog 30:196–199

    Article  Google Scholar 

  4. Cong G (2013) Petrochemical equipment based on the risk of inspection and maintenance of state and intelligent decision-making research [D]. Dalian University of Technology

  5. (2003) The american petroleum institute. damage mechanisms affecting fixed equipment in the refining industry [S]

  6. (2014) China special equipment inspection and research institute, the pressure equipment damage pattern recognition [S]

  7. DNV-GL (2010) Risk based inspection of offshore topsides static mechanical equipment [S]

  8. Chen Y, Crespi N, Siano P (2017) eRouting: An eco-friendly navigation algorithm for traffic information industry. IEEE Trans Ind Inf 13(2):562–571

    Article  Google Scholar 

  9. Alam M, Ferreira J, Fonseca J (eds.) (2016) Intelligent Transportation Systems, introduction to intelligent Transportation System “Studies in Systems, Decision and Control”

  10. Chen Y, Lee GM, Shu L, Crespi N (2016) Industrial internet of things-based collaborative sensing intelligence: framework and research challenges. Sensors 16(2):215–233

    Article  Google Scholar 

  11. Wanping J, Cunlin Z (2005) Construct the infrared thermal wave inspection application platform [J]. Advanced Materials Industry 8:70–73

    Google Scholar 

  12. Wanping J, Cunlin Z (2004) A new material inspection and characterization tech-infrared thermograghy[J]. Advanced Materials Industry 130(9):52–55

    Google Scholar 

  13. Xun W, Wanping J (2004) Actuality & evolvementof infrared thermal wave nondestructive imaging technology[J]. NDT 26(10): 497–501

    Google Scholar 

  14. Guan W, Guo L (2015) Development and application of a portable infrared thermal wavenondestructive testing system[J]. NDT 37(5):10–12

    Google Scholar 

  15. (2008) The American Petroleum Institute. Risk-Based Inspection Technology[S]

  16. Alam M, Ferreira J, Mumtaz S, Jan MA, Rebelo R, Fonseca JA (2017) Smart cameras are making our beaches safer: a 5G-Envisioned distributed architecture for safe, connected coastal areas. IEEE Veh Technol Mag 12(4):50–59

    Article  Google Scholar 

  17. Chen Y, Shu L, Crespi N (2017) Reality Mining: a prediction algorithm for disease dynamics based on mobile big data. Inf Sci 379:82–93

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge the support of National key research and development program of China (Approved Granted No.2017YFF0210403)

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Authors and Affiliations

  1. Guangdong University of Petrochemical Technology, Maoming, 525000, Guangdong, China

    Guangpei Cong & Yunrong Lv

  2. China Special Equipment Inspection and Research Institute, Beijing, 100013, China

    Duhui Lu

  3. Beijing Waiteksin Advanced Technology Co., LTD, Beijing, 100085, China

    Yi He

Authors
  1. Guangpei Cong
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  2. Duhui Lu
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  3. Yunrong Lv
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  4. Yi He
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Correspondence to Yunrong Lv.

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Cong, G., Lu, D., Lv, Y. et al. A Novel Industrial Safety IoTs Architecture for External Corrosion Perception Based on Infrared. Mobile Netw Appl 24, 1336–1345 (2019). https://doi.org/10.1007/s11036-018-1170-4

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  • DOI: https://doi.org/10.1007/s11036-018-1170-4

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