A Comparative Study Between EMD-MCKD and EMD-MEDA Techniques for the Bearing Faults Diagnosis in Induction Motors

The significance of induction motors has been greatly heightened due to the rapid advancements in modern industrial production. Therefore, a fault diagnosis based on vibration signals is therefore critical in industry and academia. In this context, we compared Empirical Mode Decomposition combined with Maximum Correlation Kurtosis Deconvolution (EMD-MCKD) to EMD with Minimum Entropy Deconvolution Adjusted (EMD-MEDA). In order to improve fault diagnosis, we apply to the reconstructed signal the technique of Maximum Correlation Kurtosis Deconvolution (MCKD). Simultaneously, subjected to Minimum Entropy Deconvolution Adjusted (MEDA). Finally, the outcomes are comprehensively analyzed and compared to identify the optimal technique for diagnosing bearing faults. This study is based upon experimental findings, and demonstrates the effectiveness and suitability of the EMD-MEDA technique, clearly promising in bearing fault diagnosis and extract the characteristics of the fault in the signal, by evaluating its accuracy and comparing its performance to EMD-MCKD.

• bearing fault diagnosis
• induction motor
• empirical mode decomposition
• minimum entropy deconvolution
• vibration signals
• signal denoising
• fault feature extraction

• Machine Fault Diagnosis Techniques
• Fault Detection and Control Systems
• Electric Motor Design and Analysis
• bearing fault diagnosis
• induction motor
• empirical mode decomposition
• minimum entropy deconvolution
• vibration signals
• signal denoising
• fault feature extraction
• Control and Systems Engineering
• Electrical and Electronic Engineering
• Engineering