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Fault diagnosis of full-hydraulic drilling rig based on RS–SVM data fusion method

Technical Paper
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Abstract

In order to improve the fault diagnosis accuracy of the full-hydraulic drilling rig, RS–SVM multi-sensor data fusion fault diagnosis method is proposed based on the rough set theory (RS) and support vector machine (SVM). In the method, the feature layer fusion structure is adopted and energy-normalized feature vectors of the fault signal sub-band are extracted by wavelet packet decomposition. Because of the advantages in evaluating fault identification parameters, removing redundant data and retaining the minimum core attribute set, the RS was introduced to the multi-sensor data fusion fault diagnosis method to avoid the dimension disaster and decrease the time consumption. In this way, the computational complexity of SVM is reduced, but its efficiency and accuracy are improved. Finally, the new fault diagnosis method was used to monitor the hydraulic motor internal leakage fault and gear tooth fracture fault of the full-hydraulic drilling rig. The experiment result shows that the classification accuracy of the new method is 64 and 100%, respectively, for hydraulic motor leakage fault and gear tooth fracture fault, and the new fault diagnosis method is effective and superior to traditional RS theory and SVM.

Keywords

Multi-sensor data fusion Support vector machine Rough set theory Full-hydraulic drilling rig Fault diagnosis 

Notes

Acknowledgements

This work was financially supported by the Sichuan Province Basic Research Plan Project (2013JY0165), the Key Research Project of Sichuan Province Department of Education and the Cultivating Programme of Excellent Innovation Team of Chengdu University of Technology under Grant No. KYTD201301.

References

  1. 1.
    Huang H (2015) Research on load sensitive intelligent control system of full hydraulic full-hydraulic drilling rig. Chengdu University Of Technology, ChengduGoogle Scholar
  2. 2.
    Yu FS, Kang H, Zhang HW (2016) Fault diagnosis for hydraulic drilling rig based on BP neural network optimized by PSO. Process Autom Instrument 37(4):42–56Google Scholar
  3. 3.
    Safizadeh MS, Latifi SK (2014) Using multi-sensor data fusion for vibration fault diagnosis of rolling element bearings by accelerometer and load cell. Inf Fusion 18:1–8CrossRefGoogle Scholar
  4. 4.
    Basir O, Yuan X (2007) Engine fault diagnosis based on multi-sensor information fusion using Dempster–Shafer evidence theory. Inf Fusion 8(4):379–386CrossRefGoogle Scholar
  5. 5.
    Sun S (2004) Multi-sensor optimal information fusion Kalman filters with applications. Aerosp Sci Technol 8(1):57–62CrossRefMATHGoogle Scholar
  6. 6.
    Shang Y, Yan CJ, Yan Z et al (2002) Synthetic insulation fault diagnostic model of oil-immersed power transformers utilizing information fusion. Proc Csee 7:025Google Scholar
  7. 7.
    Widodo A, Yang BS (2007) Support vector machine in machine condition monitoring and fault diagnosis. Mech Syst Signal Process 21(6):2560–2574CrossRefGoogle Scholar
  8. 8.
    Bacha K, Souahlia S, Gossa M (2012) Power transformer fault diagnosis based on dissolved gas analysis by support vector machine. Electr Power Syst Res 83(1):73–79CrossRefGoogle Scholar
  9. 9.
    Laouti N, Sheibat-Othman N, Othman S (2011) Support vector machines for fault detection in wind turbines. IFAC Proc Vol 44(1):7067–7072CrossRefGoogle Scholar
  10. 10.
    Cui J, Wang Y (2011) A novel approach of analog circuit fault diagnosis using support vector machines classifier. Measurement 44(1):281–289CrossRefGoogle Scholar
  11. 11.
    Lipo W (2005) Support vector machines: theory and applications. Springer, New YorkMATHGoogle Scholar
  12. 12.
    Pawlak Z (1998) Rough set theory and its applications to data analysis. Cybernet Syst 29(7):661–688CrossRefMATHGoogle Scholar
  13. 13.
    Liang J, Chin KS, Dang C et al (2002) A new method for measuring uncertainty and fuzziness in rough set theory. Int J Gen Syst 31(4):331–342MathSciNetCrossRefMATHGoogle Scholar
  14. 14.
    Yang G, Wu XP, Song YX et al (2009) Multi-sensor information fusion fault diagnosis method based on rough set theory. Syst Eng Electr 31(8):2013–2019Google Scholar
  15. 15.
    Banerjee TP, Das S (2012) Multi-sensor data fusion using support vector machine for motor fault detection. Inf Sci 217(24):96–107CrossRefGoogle Scholar
  16. 16.
    Gao SZ, Wang JS, Zhao N (2013) Fault diagnosis method of polymerization Kettle equipment based on rough sets and BP neural network. Math Probl Eng 2013:1–8Google Scholar

Copyright information

© The Brazilian Society of Mechanical Sciences and Engineering 2018

Authors and Affiliations

  1. 1.College of Nuclear Technology and Automation EngineeringChengdu University of TechnologyChengduChina

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