Reconfigurable multi-stage neural networks in monitoring industrial machines

A two-stage reconfigurable neural networks (NN) is described for real-time monitoring of onset of faults in a coolant system of a CNC machine. The measured variables in the system are current and pressure signals. The steady state values of these parameters when out of healthy range, are used as stimulus for initiating a non-destructive test. This causes the closure of a flow control valve and results in the transient response of the pump outlet pressure. The transient signal is used as input to the NN which accurately identifies inception of any faults in the system. If the system is faulty, an interprocess communication system (IPC) activates the second stage of the two-stage NN which then tests the transient pattern against the known types of failure and identifies severity of the fault. The double stage design of neural network results in achieving a high accuracy of over 99 percent in fault identification and isolation.