Fault Diagnosis of Tool Wear Based on Rough Set and Neural Network
Nie Pen · Mechanical Engineer · 2014
To overcome the problem of structure complexity and long training time in neural network method for fault diagnosis of tool wear with multi-sensor, a new fault diagnosis method based on rough set and neural network is presented. At first, the rough set was used to select the influencing factors input into the neutral network. Then, the genetic algorithm is used to overcome the shortcoming of the BP algorithm, such as slowness and convergence to local minimum. The model is applied to tool diagnosis, the self-organizing map method is used to get the discrete attributes fist, then an adaptive genetic algorithm is devised for attribute reduction, and finally the results of the attribute reduction is regard as the inputs of the neural network. The experimental results show that it is feasible and effective in the tool diagnosis.