TY - JOUR ID - 72492 TI - Intelligent Health Evaluation Method of Slewing Bearing Adopting Multiple Types of Signals from Monitoring System JO - International Journal of Engineering JA - IJE LA - en SN - 1025-2495 AU - Tang, Mingmin AU - Chen, Jie AU - Hong, Rongjing AU - Wang, Hua AD - Mechanical Engineering, Nanjing Tech University Y1 - 2015 PY - 2015 VL - 28 IS - 4 SP - 573 EP - 582 KW - Slewing bearing KW - Artificial Neural Network KW - ELMAN KW - BP KW - adaptive neuron KW - Fuzzy inference system KW - Fuzzy Clustering KW - health condition evaluation DO - N2 - Slewing bearing, which is widely applied in tank, excavator and wind turbine, is a critical component of rotational machine. Standard procedure for bearing life calculation and condition assessment was established in general rolling bearings, nevertheless, relatively less literatures, in regard to the health condition assessment of slewing bearing, were published in past. Real time health condition assessment for slewing bearing is used for the purpose of avoiding catastrophic failures by detectable and preventative measurement. In this paper, a new strategy was present for health evaluation of slewing bearing based on multiple characteristic parameters, and ANN (Artificial Neural Network ) and ANFIS(Adaptive Neuro-Fuzzy Inference System ) models were demonstrated to predicted the health condition of slewing bearings. The prediction capabilities offered by ANN and ANFIS were shown by using data obtained from full life test of slewing bearings in NJUT test System. Various statistical performance indexes were utilized to compare the performance of two predicted models. The results suggest that ANFIS-based prediction model outperforms ANN models. UR - https://www.ije.ir/article_72492.html L1 - https://www.ije.ir/article_72492_05a78ace54ded0783c913f2e26c2340d.pdf ER -