No abstract

No abstract

No abstract

No abstract

The efficiency of the Flexible Manufacturing System (FMS) is highly influenced by computer numerical control (CNC) machine tools. The most common causes of CNC machine tool failure is the bearing faults that influences the performance of manufacturing system. The detection and diagnosis of bearing defects are crucial to the reliable functioning of revolving machinery. This paper suggests an intelligent vibration-based condition and fault diagnostic technique for the identification of bearing faults in CNC machine tool. Investigational vibration data obtained for various bearings and operational requirements were analyzed in order to create a structure for the monitoring and classification of bearing defects in order to determine the health of the machine. Fault diagnosis was made using hybrid signal decomposition (HSD) methodology for the decomposition of the vibration signal. Vibration features derived from the received decomposed raw signal were chosen using the key component review to eliminate redundant features. Subsequently, these related features were input into support vector machines (SVM) and artificial neural network (ANN) for classification of various bearing faults in CNC machine tool. Experimental outcome suggest that the proposed approach has an immense capacity to prevent unplanned and unnecessary device shutdowns due to loss of bearings in CNC machine tool.