Early Mechanical Failure Prediction Technology of CNC Machining Centers Motorized Spindle

Abstract

CNC machining center spindle early mechanical failure has hidden and complex features. In this paper, PeakVue technology early detection of potential and subtle abnormal spindle vibration signal, to solve the problem of weak signal difficult to detect. In order to quickly and accurately determine the motorized spindle potential mechanical failure, this paper PSO global search capability and fast optimization speed and fuzzy neural network(FNN) fault-tolerant ability, self-adaptable characteristics of a method is proposed. The method combines fuzzy logic, RBF neural networks and FNN, spindle in order to predict an early mechanical failure of CNC machining center. In order to optimize the parameters of fuzzy neural network structure, velocity updating formula and inertia weight of PSO has been improved, so as to establish an improved PSO optimized fuzzy neural network CNC machining center spindle early failure prediction method. The results show that: the method of early mechanical failure of the spindle has higher prediction accuracy and generalization ability stronger.