Intelligent Health Evaluation Method of Slewing Bearing Adopting Multiple Types of Signals from Monitoring System

Publish Year: 1394
نوع سند: مقاله ژورنالی
زبان: English
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JR_IJE-28-4_012

تاریخ نمایه سازی: 15 آذر 1394

Abstract:

Slewing bearing, which is widely applied in tank, excavator and wind turbine, is a critical component of rotational machines. Standard procedure for bearing life calculation and condition assessment hasbeen established for general rolling bearings. Nevertheless, relatively less literatures in regard to thehealth condition assessment of slewing bearing has been published in past. Real time health condition assessment for slewing bearing is used for avoiding catastrophic failures by detectable and preventativemeasurement. In this paper, a new strategy is presented for health evaluation of slewing bearing basedon multiple characteristic parameters, and ANN (Artificial Neural Network) and ANFIS (Adaptive Neuro-Fuzzy Inference System ) models are demonstrated to predict the health condition of slewingbearings. The prediction capabilities offered by ANN and ANFIS are shown by data obtained from fulllife test of slewing bearings in NJUT test System. Various statistical performance indexes have beenutilized to compare the performance of two predicted models. The results suggest that ANFIS-based prediction model outperforms ANN models

Authors

h Wang

School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing, China Luoyang LYC bearing Co., Ltd., Luoyang, China

r hong

School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing, China

j chen

School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing, China

m tang

School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing, China