Development of a Virtual Fault Diagnostic System For Rolling Bearing
Xiaohuan Wang · Journal of Zhengzhou University · 2010
Aiming at rolling bearings,the implementation procedure of a new style fault diagnostic system is presented in this paper.The combination of rough sets and BP neural network are adopted in the design of the diagnostic system.Utilizing the knowledge reduction ability of rough sets theory,the diagnostic system preprocesses the collected fault symptom data at first,i.e.the discretization of continuous attributes by using competition learning neural networks.The intermediate output is introduced to software of Rosetta to be analyzed step by step until the smallest condition attributed sets are obtained.Based on the smallest condition attributed sets,the BP networks are built,which are used to recognize the faults of rolling bearings and then transfer the fault states back to LabView for displaying.The example analysis indicated that the system can enhance fault diagnosis convergence speed and the network training time reduces 176 steps at the same expected error.