Simulation Data-driven Migration Diagnosis Method for Guide Rail Faults in Long-term Service Elevators
The existing researches of fault diagnosis of elevator guide rails has some problems,such as scarcity of horizontal vibration classification data and large difference in the distribution of training and test data sets.A simulation data-driven fault migration diagnosis method for long-term service elevator guide rails was proposed.Firstly,the horizontal dynamics model of the elevator car was constructed,different types of guide rail fault excitations as system input for simulation and rich horizontal abnormal vibration data of elevator car were obtained.Secondly,the residual network and convolutional attention mechanism were integrated to extract fault features,and the sub-domain adap-tive method was used to align the conditional distribution of source domain and target domain in unsu-pervised scenarios.Finally,the elevator horizontal vibration data under different working conditions were used as the target domain to verify the proposed method.The experimental results show that the proposed method has high fault diagnosis accuracy in unsupervised cross-domain scenarios,which pro-vides a reference for solving the problems of scarcity of fault data for long-term service elevators.
simulation data-drivenlong-term service elevatorhorizontal vibrationsubdomain adaptationfault diagnosis
国家科技期刊平台
NSTL
万方数据
维普
