Generic Smart Rotor Fault Diagnosis Model with Normalised Vibration Parameters

The 2-Steps Smart Rotor Fault Diagnosis Model (SRFDM) is proposed. This consists of a supervised classic pattern recognition artificial neural network (ANN), which uses parameters extracted from the measured vibration signals from the machine. The Step-1 identifies the machine is healthy or faulty, and then the classification of faults in the Step-2 is performed. Earlier studies have used both time and frequency domain parameters as the input vectors to the ANN model. Currently these parameters are normalised with the speed synchronous vibration amplitude from the frequency domain analysis to remove the influence of the machine unbalance due to change in the machine speeds. Hence, the proposed model is likely to be applied to a typical machine irrespective of the machine operating speeds.