Bearing Fault Diagnosis in CNC Machine Using Hybrid Signal Decomposition and Gentle AdaBoost Learning

Purpose The most common cause of CNC machine failure is the bearing faults that influence the performance of the manufacturing system. The detection and the 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. Investigational vibration data obtained for various bearings and operational requirements were analyzed to create a structure for the monitoring and classification of bearing defects to determine the health of the machine.Methods 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 using Principal Component Analysis (PCA). Subsequently, these related features were input into Discrete AdaBoost and Gentle AdaBoost and compared to traditional Machine Learning (ML) algorithms for the classification of various bearing faults in CNC machine tools.ResultsThe proposed Gentle AdaBoost is outperformed with 100% classification accuracy by Discrete AdaBoost and other machine learning algorithms. Experimental outcomes suggest that the proposed approach has an immense capacity to prevent unplanned and unnecessary device shutdowns due to loss of bearings in the CNC machine.ConclusionThe success rate achieved using Gentle AdaBoost outperformed Discrete AdaBoost and other traditional machine learning algorithms, the performance rate obtained for defining various bearing states dependent on vibration signals.

Automated summary

This paper proposes an intelligent vibration-based condition and fault diagnostic technique for identifying bearing faults in CNC machines. The proposed approach uses Hybrid Signal Decomposition for fault diagnosis and Principal Component Analysis for feature selection. The experimental results show that it outperforms traditional machine learning algorithms and has the potential to prevent unplanned and unnecessary device shutdowns.