Fault diagnosis of full-hydraulic drilling rig based on RS–SVM data fusion method
- 4 Downloads
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.
KeywordsMulti-sensor data fusion Support vector machine Rough set theory Full-hydraulic drilling rig Fault diagnosis
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.
- 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.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
- 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
- 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
- 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